# A Comprehensive Survey on Channel Hopping and Scheduling Enhancements for TSCH Networks

Martina Maria Balbi Antunes, Lance Doherty, Thomas Watteyne

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Martina Maria Balbi Antunes, Lance Doherty, Thomas Watteyne. A Comprehensive Survey on Channel Hopping and Scheduling Enhancements for TSCH Networks. Journal of Network and Computer Applications (JNCA), 2025, 238. hal-05288324

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# A Comprehensive Survey on Channel Hopping and Scheduling Enhancements for TSCH Networks

Martina Balbi<sup>a,b,*</sup>, Lance Doherty<sup>a</sup> and Thomas Watteyne<sup>a,b</sup>

<sup>a</sup>Analog Devices, Wilmington, United States

<sup>b</sup>Inria, Paris, France

## ARTICLE INFO

**Keywords:**

Channel Hopping
IEEE 802.15.4e
Low-Power Wireless Networks
Scheduling
TSCH

## ABSTRACT

Time-Synchronized Channel Hopping (TSCH) is playing an essential role in enabling reliable and energy-efficient communication in low-power wireless applications, thanks to its scheduling and adaptive channel hopping capabilities. Advancements in these areas are vital for further improving TSCH networks performance. Enhanced scheduling algorithms can reduce energy consumption and increase network capacity, while adaptive channel hopping strategies dynamically respond to changing network conditions and interference patterns, ensuring robust communication in complex environments. This survey provides a comprehensive review of existing research on scheduling and adaptive channel hopping enhancements for TSCH networks, categorizing, analyzing, and classifying them to reveal current trends. Furthermore, it highlights open challenges that have the potential to shape the future of TSCH networks.

## 1. Introduction

The IEEE 802.15.4 standard [8] has become a prominent choice for low-power wireless communication in various domains. The traditional approach in IEEE 802.15.4 uses an asynchronous, non-beaconed Medium Access Control (MAC) mechanism, which can pose challenges. Asynchronous communication requires continuous receiver operation, leading to increased energy consumption [45]. Additionally, reliance on random access can lead to collisions, damaging communication reliability. These limitations require the development of energy-efficient protocols and algorithms to prolong the network's lifespan and enable long-term deployments.

The introduction of Time-Synchronized Channel Hopping (TSCH) in the 2012 amendment of IEEE 802.15.4e [9] has addressed these concerns. TSCH incorporates a robust architecture that includes scheduling, channel hopping, and synchronization. Scheduling assigns specific timeslots to nodes, eliminating constant listening and collisions. Channel hopping strategically switches channels to mitigate interference, promoting reliable communication [130]. All nodes maintain synchronization to ensure coordinated transmissions within designated slots. This synergy of scheduling, channel hopping and synchronization empowers TSCH to significantly enhance energy efficiency and communication reliability.

Nowadays, the IEEE 802.15.4e standard finds applications in industrial automation, smart grids, healthcare, environmental monitoring, and smart cities, among others. It also supports agricultural innovations and home automation systems, enhancing the efficiency and functionality of these sectors.

Optimizing scheduling and adaptive channel hopping mechanisms is important for maximizing the potential of TSCH. On the one hand, precise scheduling ensures efficient resource allocation, minimizing wasted energy and preventing data collisions. On the other hand, by tailoring channel hopping strategies to the dynamic environment and potential interference sources, network resilience and data integrity are significantly improved. This allows for reliable and efficient communication, even in challenging conditions.

The motivation for this survey stems from the critical role that scheduling and channel hopping play in ensuring the efficiency, reliability, and scalability of TSCH networks, yet existing research remains fragmented, particularly in channel hopping strategies. Despite their importance, a comprehensive review of both scheduling and adaptive channel hopping enhancements is lacking, making it difficult for researchers to assess existing solutions and identify future directions. The impact of this survey is twofold: it provides a structured analysis of the state of the art, categorizing and comparing existing techniques, and it highlights open challenges and emerging trends, such as AI-driven scheduling, that will shape the next generation of TSCH networks. By bridging these research gaps, this work serves as both a foundational reference for new researchers and a practical guide for designing robust TSCH-based systems.

### 1.1. Related Surveys

As TSCH networks continue to gain attention for their numerous applications, several surveys have emerged to explore various aspects of this technology. In this section, we present related surveys that contribute to the understanding of TSCH networks. These surveys offer insights into different dimensions of TSCH, covering aspects such as scheduling and network formation. This comparative analysis of related surveys serves to contextualize the contributions of our survey on channel hopping and scheduling enhancements

<figure><img src="image_2.png" /></figure>

<footnote>*Corresponding author</footnote>

<footnote>martina.balbi@analog.com (M. Balbi)</footnote>

<footnote>ORCID(s): 0009-0002-5352-8741 (M. Balbi)</footnote>
for TSCH networks. A comparative table highlighting the presented related surveys is given in Table 1.

Hermeto et al. [49] present a survey that explores slow channel hopping scheduling for the IIoT. The use of slow channel hopping enables reliability and energy efficiency by allowing nodes to use different channels for frames and retransmissions. The survey categorizes existing solutions based on their objectives, such as high-reliability and mobility support, and outlines the research community's increasing interest in deterministic slow channel hopping scheduling for IIoT. Additionally, it identifies open challenges expected to be focal points for future research.

Kharb et al. [69] introduce an overview of TSCH, focusing on its scheduling algorithms, network formation and their implications in industrial networks. The survey systematically explores TSCH features and maps associated research. It also discusses the challenges and future directions for efficient TSCH operation, with a focus on the need for improved scheduling algorithms to handle complex industrial environments.

Elsts et al. [40] conduct a survey that evaluates existing autonomous scheduling approaches for TSCH. With a focus on assessing performance across diverse scenarios, the survey analyzes advantages and disadvantages of each scheme. Additionally, it introduces a node-based channel allocation proposal, demonstrating practical improvements with up to a 6% better Packet Delivery Ratio (PDR) compared to alternatives. The survey concludes by offering guidelines for selecting an appropriate scheduler based on extensive performance evaluations.

Urke et al. [123] delves into TSCH schedulers, exploring their role in supporting the connectivity demands of concepts like Industry 4.0 and Cyber-Physical Systems. The survey systematically categorizes schedulers into centralized, collaborative, autonomous, hybrid and static types, providing a comprehensive view of historical trends, key developments and emerging patterns. Evaluation of 76 schedulers reveals insights into increasing complexity, the influence of centralized principles and the growing emphasis on autonomous mechanisms. Qualitative assessments of each scheduler highlight their objectives, enabling the identification of challenges and future research areas, including fault tolerance, scalability, non-convergecast traffic patterns and hybrid scheduling strategies.

Tabouche et al. [116] conduct a systematic mapping review that focuses on the TSCH mode and the 6TiSCH initiative. Specifically, the study explores the role of scheduling functions in managing communication resource allocation in TSCH networks. While scheduling in the IIoT has undergone extensive reviews, the survey uniquely emphasizes the underexplored dimension of traffic-awareness in SFs. By classifying and analyzing prominent traffic-aware SFs published from 2012 to 2022, the survey provides a multidimensional map to elucidate current trends and assess the empirical support for various proposals. The findings contribute to understanding of traffic-aware TSCH scheduling and pinpoint open challenges, prompting future research directions in the

design, implementation and evaluation of new traffic-aware SFs.

Kherbache et al. [70] present an examination of decentralized scheduling protocols. This work focuses on heterogeneous traffic conditions, an area that has become increasingly relevant as industrial and IoT networks grow in complexity. The authors classify decentralized TSCH scheduling into several categories, including autonomous, distributed, and reinforcement learning-based approaches. Their evaluation of these protocols under varied traffic scenarios provides valuable insights into their performance, scalability, and adaptability.

Our analysis of these works has revealed a bias towards scheduling algorithms in existing surveys, with most dedicating significant attention to this area. Also, one survey incorporates network formation, while channel hopping enhancements for TSCH networks remain unexplored.

This survey stands as the first comprehensive review to address both scheduling and channel hopping enhancements in TSCH networks concurrently. By integrating these components, we highlight their importance in enhancing network efficiency and reliability. Our work introduces novel taxonomies that classify and analyze advancements in both scheduling and channel hopping, offering a unique framework that facilitates a deeper understanding. This work not only presents the current state of research but also highlights the need to consider both elements to fully optimize TSCH network performance. Through this approach, our survey provides a resource for researchers, as effective solutions that strengthen network robustness require considering interwined aspects of TSCH operation.

## 1.2. Objectives and Contributions of This Survey

This survey aims to provide a comprehensive and structured analysis of the latest advancements in scheduling and channel hopping mechanisms for TSCH networks. Specifically, it seeks to:

* **Offer a foundational introduction to TSCH** – We provide an overview of the TSCH protocol, its key components (scheduling, channel hopping, synchronization), and its role in low-power wireless networks. This serves as a starting point for researchers new to the field.

* **Summarize and categorize** existing research on TSCH scheduling and channel hopping to offer a structured understanding of the field.

* **Identify key challenges and limitations** in current approaches, including scalability, mobility support, and adaptability to dynamic network conditions.

* **Compare and contrast** different techniques in terms of execution methods, optimization goals, evaluation methodologies, and implementation tools.

* **Highlight emerging trends** such as the role of AI and machine learning in optimizing TSCH performance.
A Survey on TSCH Channel Hopping & Scheduling

<table><caption>**Table 1**

Comparative table of TSCH surveys.</caption><thead><tr><td>Survey Title</td><td>Authors</td><td>Year</td><td>Key Contributions</td><td>Number of Works Reviewed</td><td>What Our Survey Adds</td></tr></thead><tbody><tr><td>Scheduling for IEEE802.15.4-TSCH and slow channel hopping MAC in low power industrial wireless networks: A survey</td><td>Hermeto et al. [49]</td><td>2017</td><td>Detailed look at slow channel hopping and its scheduling implications.</td><td>71</td><td>Broader range of TSCH applications and challenges.</td></tr><tr><td>A Survey on Network Formation and Scheduling Algorithms for Time Slotted Channel Hopping in Industrial Networks</td><td>Kharb et al. [69]</td><td>2019</td><td>Examines network formation along with scheduling algorithms.</td><td>52</td><td>Detailed focus on channel hopping and scheduling specifics.</td></tr><tr><td>An Empirical Survey of Autonomous Scheduling Methods for TSCH</td><td>Elsts et al. [40]</td><td>2020</td><td>Taxonomy and empirical evaluation of autonomous scheduling methods.</td><td>13</td><td>Inclusion of centralized and distributed methods.</td></tr><tr><td>A Survey of 802.15.4 TSCH Schedulers for a Standardized Industrial Internet of Things</td><td>Urke et al. [123]</td><td>2022</td><td>Classification of schedulers; holistic view of TSCH scheduling.</td><td>76</td><td>Broader analysis beyond classifications.</td></tr><tr><td>Decentralized TSCH scheduling protocols and heterogeneous traffic: Overview and performance evaluation</td><td>Kherbache et al. [70]</td><td>2023</td><td>Overview of decentralized TSCH schedulers classified into categories; evaluation in heterogeneous traffic.</td><td>32</td><td>Focus on all schedulers, not only decentralized.</td></tr><tr><td>A Comprehensive Survey on Channel Hopping and Scheduling sEnhancements for TSCH Networks</td><td>Balbi et al.</td><td>2025</td><td>Comprehensive review of TSCH scheduling and channel hopping, including new taxonomy and analysis.</td><td>85</td><td>Synthesizes literature on scheduling and channel hopping; provides foundational knowledge for developing new, more efficient TSCH methods</td></tr></tbody></table>

* **Provide future research directions**, offering insights into promising areas for further exploration and innovation.

In line with the mentioned objectives, this survey makes several key contributions:

* **First in-depth review of channel hopping enhancements** – While previous surveys mainly focus on scheduling, this work is the first to comprehensively analyze adaptive channel hopping strategies, highlighting their role in interference mitigation and network reliability.

* **Comprehensive classification of scheduling algorithms** – We introduce a new taxonomy for TSCH scheduling solutions, categorizing them based on execution methods, optimization objectives, solving methods, evaluation methods, network characteristics, design criteria and application domains.

* **Comparative analysis of surveyed techniques** – We provide an evaluation of scheduling and channel hopping mechanisms, examining their network characteristics (mobility support, topology), implementation tools (simulators, real-world deployments) and their scalability and adaptability in real-world scenarios.

* **Identification of open challenges and research directions** – We highlight key research gaps, including the need for AI-driven adaptive scheduling and channel selection, managing the energy efficiency vs. performance trade-offs and support for scalable and large deployments.

By addressing these aspects, this survey serves as both a foundational guide for beginners and a comprehensive reference for experts, offering a roadmap for future innovations in TSCH-based low-power wireless networks.

## 1.3. Survey Methodology and Outline

To ensure a thorough exploration of this topic, we've implemented a thorough methodology for selecting relevant research. The initial phase involved a targeted search on Google Scholar, using key terms like "adaptive channel hopping TSCH" and "scheduling TSCH" to identify highly relevant research in these domains. Strict selection criteria were applied, focusing solely on works explicitly tailored for TSCH networks, while excluding any research not explicitly mentioning TSCH. Building upon this initial selection, a systematic process was employed to create a comprehensive list, with each identified work undergoing rigorous scrutiny to ensure its relevance and specific focus on TSCH enhancements. Additionally, references cited within the initial selections were reviewed and added if relevant, enriching the scope of the survey and capturing interconnected contributions.

Through this approach, the survey ultimately includes 18 papers on adaptive channel hopping and 67 studies on scheduling. This exhaustive yet selective approach forms the foundation for a detailed examination of the latest advancements, trends and challenges in these aspects of TSCH networks. By employing this methodology, the survey aims to provide a reliable and up-to-date overview, including the most relevant contributions in the field.

The remainder of this article is organized as follows. Section 2 provides an overview of low-power wireless communications, introducing the TSCH protocol, the 6TiSCH
standard and prevalent evaluation and simulation tools in the
community. Section 3 delves into scheduling enhancements,
featuring a comprehensive taxonomy and literature review,
concluding with a summary and analysis of identified ad-
vancements. Section 4 explores enhancements in adaptive
channel hopping, also presenting a taxonomy and detailed
analysis. Section 5 outlines the future prospects in TSCH
networks. Finally, Section 6 concludes the paper by summa-
rizing the survey and presenting insights obtained.

## 2. Background on Low-Power Wireless Communications

The ever-growing Internet of Things (IoT) and its vast
network of devices, from smart sensors to fitness trackers,
have sparked a need for efficient and low-power wireless
communication. These battery-powered devices operate in a
constant balancing act, requiring reliable data transmission
while conserving energy [127]. Low-power communication
protocols are specifically designed to address the needs of
these constrained devices. They enable efficient communi-
cation across various applications, from smart homes and
industrial automation to healthcare and environmental mon-
itoring. By minimizing energy consumption, these protocols
not only extend the life of battery-powered devices but
also open doors for innovative applications in remote and
challenging environments.

One prominent example of a low-power communication
protocol is the already mentioned IEEE 802.15.4 standard,
widely used in various IoT scenarios. Within this standard,
TSCH stands out as a leading solution.

## 2.1. Overview of Time-Synchronized Channel Hopping

TSCH was first introduced in IEEE 802.15.4e [9] and
was specifically designed to empower IEEE 802.15.4 de-
vices for a broad spectrum of applications. At its essence,
TSCH employs a medium access technique grounded in time
synchronization for efficient low-power operation, coupled
with channel hopping to enhance overall reliability [131].
Notably, TSCH represents a substantial departure from the
conventional IEEE 802.15.4 MAC protocol, amounting to
more than just an amendment – rather, it can be better
characterized as a comprehensive “redesign” [131]. Impor-
tantly, TSCH does not alter the physical layer, allowing
it to function seamlessly on any hardware compliant with
IEEE 802.15.4.

TSCH is designed to facilitate low-power mesh net-
working, particularly suitable for challenging industrial en-
vironments, by emphasizing high end-to-end reliability, en-
hanced power conservation for prolonged battery life and
robustness [31]. Functioning as a hybrid of both Time-
Division Multiple Access (TDMA) and Frequency-Division
Multiple Access (FDMA), TSCH employs the principles of
timeslots and channel hopping to optimize its wireless com-
munication capabilities. Also, the incorporation of multi-
channel functionality enables simultaneous communications

<figure><img src="image_3.png" /><figcaption>Figure 1: TSCH slotframe</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2: TSCH timeslot</figcaption></figure>

by using different channel offsets, consequently increasing
network capacity. More importantly, channel hopping is
used to alleviate the impact of interference and multipath
fading, leading to improved communication reliability and
a reduction in energy consumption related to packet re-
transmissions [130].

Within a TSCH network, nodes achieve synchronization
and time is segmented into timeslots, typically spanning
10 ms each. These timeslots are organized into one or more
slotframes, as seen in Fig. 1, that persistently cycle over time.
The slotframe size remains flexible, adaptable to the specific
requirements of the application. A shorter slotframe yields
more frequent repetition of timeslots.

All nodes in the network also adhere to a shared com-
munication schedule which dictates their actions in each
timeslot: whether to send to a specific neighbor, receive
from a designated neighbor, or enter a sleep state. Network
scheduling poses a significant challenge, as the TSCH spec-
ification initially did not outline how to construct, update
and maintain a schedule, deeming it beyond the scope of the
IEEE 802.15.4e standard.

In a timeslot such as the one in Fig. 2, a typical exchange
occurs where one node sends a frame to its neighbor and
the neighbor reciprocates with an acknowledgment. If the
acknowledgment is not received within the specified time-
out, frame re-transmission is deferred until the next assigned
transmit timeslot.

A link represents a single element of the schedule, char-
acterized by a timeslot and a channel offset. Fig. 3(a) il-
lustrates a network featuring a tree topology comprising 8
nodes. The communication dynamics among these nodes
are detailed in Fig. 3(b). In this scheduling arrangement, the
<figure><img src="image_5.png" /><figcaption>Figure 3: Routing topology and schedule of a TSCH network</figcaption></figure>

slotframe spans 5 timeslots, with the use of only 4 channel offsets. TSCH facilitates both dedicated links, exclusively assigned to a single node for transmission (contention-free) and shared links, allocated to multiple nodes for transmission (contention-based).

In the initiation of a TSCH network, a TSCH coordinator takes charge. This coordinator configures parameters such as the network identifier, slotframe size and Frequency Hopping Sequence (FHS), all of which are shared among the nodes enlisted in its network. Subsequently, the coordinator establishes the network by initializing the Absolute Slot Number (ASN) to 0, with the ASN incrementing by one at each timeslot. Both the coordinator and other nodes engage in periodic broadcasts of Enhanced Beacon (EB) messages. These EB messages contain essential information for managing a time-synchronized network. Nodes receiving an EB can seamlessly join the network, leveraging the information provided by the EB. Upon joining the network in response to an EB received from a neighbor, a node designates that neighbor as its TSCH time source and aligns its time with the network time using the ASN. Following successful integration into the TSCH network, the node consistently broadcasts EB messages to ensure that neighboring nodes can join through it, enabling them to synchronize their time with the network.

A pair of nodes can engage in the exchange of a data packet along with its corresponding acknowledgment. The transmission of a packet by the sender occurs on a designated channel offset, and the recipient must actively listen to the same channel offset. It is important to note that the TSCH channel offset does not exhibit a one-to-one correspondence with a physical IEEE 802.15.4 channel, but it rather functions as an offset in the FHS. Essentially, 16 distinct channels are available for communication, with some frequencies possibly being removed due to suboptimal communication quality or regulatory restrictions. This channel offset is then translated into a different frequency during each iteration of the slotframe using Eq. 1, where FHS refers to the network's Frequency Hopping Sequence, ASN the current Absolute Slot Number, channelOffset the offset for which we want to calculate the frequency and finally numChannels the amount of channels present in the FHS [28].

<figure><img src="image_6.png" /><figcaption>Figure 4: 6TiSCH protocol stack</figcaption></figure>

$$ frequency = FHS[(ASN + channelOffset) \% numChannels] \quad (1) $$

## 2.2. Overview of 6TiSCH

The well-organized layered structure of IEEE 802.15.4e TSCH allows it to be integrated into an IPv6-enabled protocol stack designed for low-power wireless networks. This architectural integration has been proposed by the IETF 6TiSCH Working Group (WG) [128]. Established in October 2013, the IETF 6TiSCH WG plays a pivotal role in the incorporation of IPv6 [30] into industrial standards, fostering convergence between Operational Technology (OT, industrial networks) and Information Technology (IT, computer networks) [97].

The objective of the 6TiSCH architecture [120] is to establish an IPv6 multi-link subnet, encompassing various Low-power and Lossy Networks (LLNs) interconnected by a high-speed backbone. The 6TiSCH WG envisions a Wireless Sensor Network (WSN) architecture based on TSCH, emphasizing high-reliability packet delivery. In pursuit of this goal, it outlines a network stack, depicted in Fig. 4, where IPv6 connectivity is realized through standardized protocols such as 6LoWPAN and UDP [108], in conjunction with the Routing Protocol for Low-Power and Lossy Networks (RPL) [133] and CoAP [109] at the application layer.
Beyond these protocols, 6TiSCH introduces scheduling functions (SFs) that implement distributed slotframe scheduling strategies. It also defines the 6TiSCH operation sublayer (6top) [129] that enables the negotiation of cells between neighboring nodes. The 6top Protocol (6P) serves as a protocol enabling neighboring nodes to negotiate the addition/removal of cells from their schedules. Additionally, the working group delineates the minimal configuration necessary for nodes to join a 6TiSCH network [126].

### 2.2.1. Minimal Scheduling Function (MSF) and Scheduling Function 0 (SF0)

6P collaborates with a SF, which serves as the governing policy determining how to manage cells and initiate 6P transactions. The Minimal Scheduling Function (MSF) [18], established by the 6TiSCH WG as the default SF, extends the minimal schedule configuration. MSF operates by augmenting child-parent links based on the traffic load, categorizing cells into two types: *Autonomous Cells* and *Negotiated Cells*. *Autonomous Cells* offer connectivity to any neighbor without requiring signaling; *Negotiated Cells* are subject to the reactive traffic-based policies dictated by the 6P protocol for installation or removal from the schedule.

The Scheduling Function Zero (SF0), a minimal SF designed for the 6top sublayer is introduced by Dujovne et al. [34]. SF0 provides a basic set of functionalities applicable across various applications and comprises two key algorithms: the Scheduling Algorithm and the Relocation Algorithm.

The Scheduling Algorithm determines the number of TX and/or RX cells to allocate or delete between neighboring nodes. This algorithm collects requests for cell allocation/deletion from neighbors, along with the current cell usage. The cell estimation algorithm calculates the required cells and the allocation policy decides when to schedule the new cells, taking into account hysteresis and overprovisioning. The algorithm triggers only when there are changes in the number of effectively used or requested cells from a specific node.

Allocated cells may encounter packet loss due to various factors such as noise, interference or collisions. The Relocation Algorithm monitors PDR periodically for each allocated cell. Relocation is triggered when the PDR drops below a threshold compared to the average PDR of other allocated cells. The destination location on the schedule is randomly defined.

### 2.3. Evaluation and Simulation Tools

To evaluate the performance of the surveyed enhancements, a variety of evaluation and simulation tools have been developed. These tools are instrumental in assessing the system's effectiveness, reliability and efficiency across diverse scenarios. This section introduces the essential evaluation and simulation tools that are consistently used throughout the surveyed papers.

* **Contiki OS** [35] was tailored for memory-constrained environments like those in WSNs. The OS supports a complete TCP/IP stack through the uIP library and

employs the Protothreads programming abstraction. Developed in C, Contiki is designed for easy portability to new platforms. Contiki OS has now been succeeded by Contiki-NG.

* **Contiki-NG** [90] prioritizes dependable, low-power communications and standardized protocols such as 6LoWPAN, IPv6, 6TiSCH, RPL and CoAP. Its core objectives include facilitating fast prototyping and evaluation of IoT research, reducing time-to-market for IoT applications and providing an accessible platform for embedded systems-related courses in higher education.

* **COOJA** serves as a simulator for Contiki OS, facilitating simultaneous simulation across various system levels. It seamlessly integrates low-level simulation of sensor node hardware with high-level behavioral simulation within a unified platform. COOJA's flexibility and extensibility allow modification or replacement of all system levels, including sensor node platforms, operating system software, radio transceivers and radio transmission models.

* **OpenWSN** [132] provides a free and open-source implementation of the IEEE 802.15.4e protocol stack, along with surrounding debugging and integration tools. It is versatile, having been successfully ported to four commercially available platforms. It facilitates IPv6 connectivity and allows simulation of complete networks on a PC. The IEEE 802.15.4e implementation operates in interrupt context for precise timing and remains independent of the underlying operating system on the device, facilitating portability to other operating systems.

* **FIT IoT-LAB** [2] is part of the FIT experimental platform, a suite of components fostering experimentation on innovative services for both academic and industrial users. Serving as a scientific testbed, IoT-LAB offers complete control over network nodes and direct access to the gateways connected to these nodes. Using web-based or command-line tools, users can allocate nodes for experiments, deploy firmware and monitor metrics such as energy consumption, end-to-end delay, throughput and overhead. IoT-LAB currently stands as the largest open WSN testbed globally, with over 2,700 wireless sensor nodes spread across six sites in France. These nodes, available in fixed or mobile configurations, can be allocated in diverse topologies throughout all sites. The testbed offers a range of wireless sensors featuring distinct processor architectures and wireless chips.

* The 6TiSCH Simulator [84], implemented in Python as a discrete-event simulator, strategically mitigates common simulation challenges by introducing tailored abstractions specific to 6TiSCH. Instead of attempting to replicate physical intricacies, such as
synchronization issues due to imperfect crystals, bit-
specific transmission errors and hardware-dependent
processing delays (which are best studied with real
hardware), the simulator focuses on emulating ob-
served network behavior from the MAC layer.

* ns-3 [106] stands out as a modular, programmable, extensible and open-source simulation framework specifically tailored for computer networks. This simulation environment operates on the principles of discrete-event simulation, where each event is associated with its execution time, progressing through the execution of events in chronological order of simulation time.

3. Scheduling

Scheduling in TSCH networks refers to the allocation of
timeslots and channels for communication activities among
network nodes. By orchestrating the timing of transmis-
sions and receptions, scheduling optimizes the overall net-
work performance, ensuring reliable and timely commu-
nication in diverse and challenging environments. Unlike
some other wireless communication standards that explic-
itly define scheduling strategies, IEEE 802.15.4e leaves the
scheduling aspect open-ended. This absence of a standard-
ized scheduling algorithm allows for flexibility and adapt-
ability, acknowledging the diverse applications and require-
ments that TSCH networks may encounter.

Enhancements to TSCH scheduling are aimed at opti-
mizing its performance and reliability across diverse ap-
plications. The need for deterministic communication is
particularly significant for applications with strict timing
constraints (e.g. in the order of milliseconds), such as in-
dustrial automation [68]. Concurrently, energy efficiency
considerations drive efforts to minimize idle listening and
collisions, crucial for extending the operational lifespan of
battery-powered devices.

The challenge of scaling TSCH networks further requires
scheduling improvements to effectively handle an increasing
number of nodes and prevent congestion. Adaptability to
dynamic environments becomes central to address changes
in network topology, interference levels and evolving com-
munication requirements. The reduction of latency emerges
as a key objective to ensure timely data exchange, especially
in applications like industrial control systems.

Simultaneously, strategies for interference mitigation
play a vital role in ensuring reliability amid coexisting wire-
less technologies. Additionally, supporting heterogeneous
traffic efficiently aligns with the varied communication pat-
terns inherent to TSCH networks, collectively contributing
to an elevated overall performance and reliability.

The absence of a scheduling specification not only en-
courages innovation but also underscores the necessity of
customizing scheduling strategies to align with the distinct
characteristics and requirements of the deployment environ-
ment. In the following sections, we thoroughly examine the

<figure><img src="image_7.png" /><figcaption>Figure 5: Proposed taxonomy of scheduling enhancements.</figcaption></figure>

diverse array of scheduling algorithms tailored for TSCH
networks.

**3.1. Taxonomy of Scheduling Algorithms**

As outlined in Section 1.2, our objectives include a
detailed examination of TSCH scheduling enhancements. To
facilitate this analysis, we create a comprehensive taxonomy
that encompasses key classes that define the characteris-
tics of these enhancements. The classes include execution
method, optimization objective, application domain, solv-
ing method, evaluation method, network characteristics and
design criteria. This structured approach enables us to sys-
tematically explore how different scheduling strategies align
with the goal of enhancing network efficiency and reliability.
Each class within the taxonomy serves to highlight distinct
facets of scheduling strategies, providing insights into their
specific contributions and challenges in TSCH network en-
vironments. This thorough categorization ensures that our
review comprehensively addresses the diverse aspects of
scheduling enhancements, directly supporting our objective
to offer a detailed understanding of the current state of the
art. The proposed taxonomy for scheduling enhancements is
presented in Fig. 5.

In this section, 67 works proposing scheduling algo-
rithms were thoroughly reviewed. Fig. 6 offers a timeline
<figure><img src="image_8.png" /><figcaption>**Figure 6:** Timeline of the surveyed scheduling enhancements (the size of each bubble is proportional to its current amount of citations)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 7:** Distribution of the execution method of surveyed scheduling algorithms.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 8:** Distribution of the execution method of surveyed works per year.</figcaption></figure>

indicating the year of publication of each of the surveyed papers along with their current number of citations. The timeline highlights a steady growth in TSCH scheduling research, with a surge of diverse approaches between 2015 and 2019. After 2019, the number of new TSCH scheduling solutions decreased, likely reflecting a saturation of traditional approaches and limited room for further innovation. However, with the emerging trend of AI for edge devices, this dynamic could potentially shift, opening new avenues for innovation.

### 3.1.1. Execution Method

The execution method characterizes the approach employed in implementing scheduling algorithms. This is crucial in understanding how the scheduling algorithms are orchestrated within the network. Scheduling algorithms can be categorized as centralized, distributed or autonomous. A distribution of the execution methods used in the scheduling

algorithms reviewed is presented in Fig. 7. Furthermore, the distribution per year can be seen in Fig. 8. Initially, centralized algorithms were notably more popular, but over time, distributed algorithms gained prominence, with autonomous algorithms emerging as the current dominant trend.

In a centralized algorithm, a specific node within the network, often the network coordinator, takes charge of creating, distributing and updating the link schedule using information received from all network nodes. This approach requires frequent re-computation and redistribution of the schedule, making it less suitable for dynamic networks and large-scale deployments.

For instance, Palattella et al. [96] introduce the Traffic Aware Scheduling Algorithm (TASA), a centralized scheduling algorithm which leverages established graph theory techniques. This algorithm focuses on network topology and traffic load to meet the demands of emerging industrial applications requiring low latency, low duty cycle and minimal energy consumption. Within a centralized framework, TASA strategically employs matching [80] and coloring procedures from graph theory to plan the distribution of slots
and channel offsets across the entire network topology graph. While TASA offers significant advantages, it has limitations. One key weakness is its inability to adapt to dynamic events like packet drops. Since the schedule is based on initial traffic information, unexpected packet losses require retransmissions in subsequent slotframes, introducing delays.

Similarly, AMUS is presented by Jin *et al.* as a centralized scheduling method for TSCH networks [62], and tackles the challenges of low latency and high reliability in time-critical industrial applications. It achieves this through three key components. First, a Multi-hop Scheduling Sequence (MSS) prioritizes nodes and establishes efficient relay paths. It creates a source node sequence based on queue size for urgent data delivery. Then, routing algorithms like RPL add relay links, ensuring streamlined data flow and avoiding wasted schedule cells. Finally, the novel Tentative Cell Allocation addresses retransmissions without disrupting ongoing communication. Simulations showcase AMUS’s superiority over popular algorithms like TASA in terms of both latency and reliability.

In evaluating centralized scheduling, while it enables optimal resource allocation and streamlined network management, it also introduces significant challenges. The centralized nature inherently induces a single point of failure, making the network vulnerable to disruptions if the central controller fails. Moreover, in large-scale or highly dynamic environments, the overhead associated with gathering global network information can result in delays and scalability issues.

Distributed execution methods empower each node within the network to compute its part of the link schedule. Nodes exchange local and partial information with neighboring nodes to collectively build the overall schedule. While distributed approaches may not yield the optimal schedule, they are more energy-efficient, making them suitable for energy-constrained nodes. These methods are particularly valuable in dynamic environments where network conditions can change.

Tinka *et al.* [121] present what they believe to be the first distributed scheduling algorithm for TSCH networks capable of handling mobile nodes. The proposed scheduling comprises two algorithms: Aloha-based scheduling and Reservation-based scheduling. Aloha-based scheduling assigns a frequency channel for broadcasting advertisements to identify new neighbors, while Reservation-based scheduling improves upon Aloha-based scheduling by incorporating a dedicated slot for targeted advertisements based on gossip information. Both algorithms follow an advertisement and rendez-vous scheme, where nodes continuously advertise their presence for neighbor discovery.

Additionally, Accettura *et al.* [1] present a pioneering approach to constructing optimal multi-hop schedules in a distributed fashion. The Decentralized Traffic-Aware Scheduling algorithm, DeTAS, initiates scheduling by having each node communicates traffic information to its parent node. Upon receiving this information, the parent node updates its own traffic details and transmits the updated information

to its parent node until it reaches the sink node. The sink node categorizes child nodes into even or odd scheduling, where data transmission and reception follow specific slot offsets based on the scheduling type. However, DeTAS faces challenges when rescheduling is required due to new nodes joining or parent node changes, as each node is only aware of scheduling information for its parent and child nodes. The sink node must determine even/odd scheduling based on traffic information from child nodes, leading to increased packet requirements and overhead during rescheduling, ultimately impacting total scheduling time.

Distributed scheduling, while offering improved scalability and robustness to network changes, it faces significant challenges in ensuring global optimization. Decentralized decision-making can result in non-uniform policies that may not align optimally with network goals, potentially leading to inefficiencies in resource usage. Furthermore, the lack of a central coordination point can complicate the enforcement of comprehensive network policies, leading to inconsistencies and potential conflicts.

Autonomous execution methods involve nodes independently determining their transmission schedule without active collaboration with neighboring nodes. This decentralized approach aims to minimize the coordination overhead and is often well-suited for resource-constrained devices.

A pioneering work on autonomous scheduling algorithms is Orchestra [36], in which nodes autonomously compute individual local schedules. These schedules are updated automatically as the network's topology evolves, eliminating the need for signaling overhead and any central or distributed scheduler. Orchestra relies on existing network stack information to maintain these schedules, employing simple periodic schedules that nodes update instantly as the routing topology changes. Evaluation based on 219 individual experiments lasting up to 72 h, demonstrates that Orchestra enables autonomous TSCH scheduling, achieving end-to-end delivery ratios exceeding 99.99%. This marks a notable improvement over state-of-the-art asynchronous solutions, showcasing strong reliability while maintaining energy and latency close to the state of the art. However, it is noted that Orchestra is not collision-free and may not be suitable for burst applications due to its single cell allocation on the same channel, without dynamically resolving slot conflicts. Additionally, Orchestra lacks awareness of network load, limiting the optimization of the schedule for different traffic patterns.

Building on top of this, Deac *et al.* [29] developed an Orchestra-based scheduler tailored for applications experiencing unpredictable traffic bursts. The scheduler implementation allocates resources based on traffic congestion metrics. The primary objective of their work is to create a scheduler that dynamically assigns additional cells during high traffic periods to ensure the application's required Quality of Service (QoS). Specifically designed for convergecast traffic, the scheduler adheres to the principles of Orchestra. In convergecast scenarios, nodes closest to the collecting point are often the most constrained. To anticipate future
congestion, the scheduler monitors the remaining packets in the queue for these nodes and allocates additional cells accordingly. The allocation decision takes into account the number of descendants these nodes have. The performance analysis reveals that the proposed solution adapts to sudden changes in traffic by dynamically allocating additional cells during bursts and de-allocating them when the burst sides. This dynamic adjustment results in improved PDR, reduced delay and network stability in the face of sudden traffic increases. The scheduler performs comparably to Orchestra in scenarios without bursts, with the observed increase in the radio duty cycle attributed to the allocation of additional cells.

However, without global coordination, achieving optimal resource allocation across the entire network using an autonomous scheduling algorithm can be challenging. There is a potential for conflicts and contention, especially in scenarios where multiple nodes autonomously adjust their schedules without awareness of each other's decisions. What is more, autonomous scheduling still struggles with adaptability to dynamic network conditions. Since nodes operate based on predefined rules, the absence of reactive capabilities to network changes can lead to suboptimal performance, especially in environments where network topology or traffic patterns fluctuate frequently. This method's efficiency is also heavily reliant on the initial setup, which may not account for future changes in network configuration or usage.

In order to address the challenge of establishing a reliable autonomous schedule, Elsts et al. [39] introduce a method for forwarding dynamic data within TSCH networks. Recognizing a priori unpredictability of data source locations, wireless link quality and the routing topology, reserving slots for the worst-case scenario becomes inefficient and often impractical under high expected data rate conditions. Their proposed solution uses a hybrid approach, introducing dedicated TSCH cells alongside shared TSCH slots within the same schedule. Under realistic assumptions of wireless link diversity, the inclusion of shared slots in the TSCH schedule is demonstrated to enhance the overall PDR and system fairness. The schedule is statically constructed at compile-time and during run-time, nodes autonomously decide which TSCH links to use based on their routing state. Using a queue-size dependent contention algorithm for shared slots, nodes with lower link quality or higher data volumes can make more transmissions within each slotframe. Results indicate that in unpredictable environments, the proposed techniques outperform schedules with dedicated-only slots, enhancing the average reliability of the TSCH network.

### 3.1.2. Optimization Objective

Scheduling solutions are often designed with specific optimization objectives in mind. Each algorithm seeks to achieve a balance between various critical parameters to enhance the overall performance of TSCH networks, prioritizing ones on top of others. These objectives include

minimizing latency, minimizing overhead, ensuring maximum reliability, minimizing energy consumption, minimizing slotframe length and maximizing throughput. An overview of the optimization objectives of the surveyed papers is presented in Table 2.

Minimizing latency is a fundamental objective in TSCH networks, especially critical in applications requiring real-time data transmission, such as industrial automation and critical infrastructure monitoring. Reducing latency is essential for improving the responsiveness of the network and ensuring timely data delivery, which is vital for systems that rely on quick decision-making based on sensor data. Successfully minimizing latency can significantly enhance the operational performance of TSCH networks, leading to more reliable and faster communication. This is particularly beneficial in environments where delays can result in inefficiencies or safety concerns. Design concerns for minimizing latency involve managing the trade-off between rapid data transmission and the potential for increased packet collisions and network congestion, which can occur when many nodes attempt to transmit data simultaneously.

For instance, Chang et al. [19] introduce the Low Latency Scheduling Function (LLSF), a SF that adopts a daisy-chaining approach for timeslots rather than selecting them randomly. When nodes schedule a timeslot from one node to another, instead of random selection, LLSF daisy-chains the timeslots used in a multi-hop path. The scheduling involves three important steps: determining the gap between each reception slot from the previous hop and the previous reception slot from the same neighbor, selecting the slot with the largest gap to its left and assigning the new transmission slot to the next hop as the closest unused slot to the right of the selected reception slot. In this algorithm, however, local schedules are still uncoupled and collisions can occur. The implementation of LLSF in OpenWSN is experimentally evaluated, demonstrating an 82.8% reduction in end-to-end latency on a 5-hop path compared to SF0, with no additional costs.

Another example of an algorithm aiming to minimize latency is given by Kotsiou et al. [75]. They introduce the Low-Latency Distributed Scheduling Function (LDSF), which organizes the slotframe into smaller parts known as blocks. Each transmitter, based on its hop distance from the border router, selects the appropriate set of blocks, enabling the automatic scheduling of retransmission opportunities. In LDSF, the end-to-end delay is minimized by chaining blocks effectively. Nodes at even hop distances are scheduled during even blocks, while those at odd hop distances are scheduled during odd blocks. This ensures that, on average, the packet progresses by one hop at the end of each block.

Other algorithms aim to minimize overhead, which is crucial for maintaining the efficiency of TSCH networks, particularly as these systems scale up in terms of node count and complexity. Network overhead mainly includes the communication and computational costs associated with
maintaining network operations such as scheduling, syn-
chronization, and data transmission protocols. High over-
head can consume significant network resources, reducing
the overall efficiency and responsiveness of the system. By
reducing these overhead costs, TSCH networks can achieve
greater scalability and prolong battery life in constrained
devices, which is essential for large-scale deployments in
remote or energy-sensitive environments. Minimizing over-
head involves design concerns related to maintaining suf-
ficient network control and operational efficiency, despite
reduced communication for management tasks, which could
potentially limit the network's ability to adapt to changes or
detect and respond to errors.

For example, Kim et al. [71] present ALICE, an innova-
tive autonomous link-based cell scheduling scheme that al-
locates a unique cell for each directional link. This approach
closely interacts with the routing layer and uses only local
information, eliminating the need for additional communica-
tion overhead. In contrast to Orchestra's node-based schedul-
ing, ALICE allocates a TSCH cell for each directional link,
considering a pair of nodes and traffic direction. ALICE's
key features include tight interaction with RPL routing, al-
lowing nodes with more RPL neighbors to have more unique
TSCH cells, prevention of upstream and downstream traffic
interference, simultaneous use of multiple channels and zero
additional overhead for cell scheduling. To the best of their
knowledge, ALICE stands out as the first autonomous cell
scheduling method that outperforms Orchestra in various
aspects, including reliability, throughput, latency, routing
stability and energy consumption.

Reliability takes center stage in many algorithms, striv-
ing to ensure robust and dependable communication even in
challenging environments. This is particularly true for crit-
ical applications such as healthcare monitoring systems, in-
dustrial automation, and safety-critical infrastructure, where
data integrity and consistent network performance are cru-
cial. Reliability in these networks is impacted by factors like
interference, packet loss, and node failure. Improving relia-
bility involves enhancing the robustness of communication
links and ensuring that data packets are delivered success-
fully despite environmental challenges or hardware limita-
tions. Enhanced reliability leads to improved system stability
and trustworthiness, which are vital for deployments where
consistent data delivery directly impacts operational suc-
cess. When maximizing reliability, it is important to focus on
ensuring consistent and robust network performance under
varying conditions and challenges such as interference, node
failure, and dynamic environmental changes.

Hosni et al. [52], for instance, propose a generalized
approach inspired by the concept of duocast presented in
the ISA-100.11a standard [54] to enhance reliability by
involving multiple receivers. The impact of this technique on
network performance is explored, introducing an algorithm
that assigns several receivers to each transmission, increas-
ing the likelihood that at least one device correctly receives
the packet. Using a multipath topology facilitated by the
routing layer helps reduce the number of transmissions while

maintaining the desired reliability, resulting in increased
network capacity and a significant reduction in jitter.

Energy efficiency can also be an important concern in TSCH networks, with algorithms in this category aiming to minimize energy consumption and prolonging the lifespan of battery-powered devices. This is especially important for networks that are deployed in environments where power resources are limited, such as remote sensing applications and wearable technologies. Energy efficiency in TSCH networks means optimizing the active and idle times of the nodes to reduce unnecessary power usage, which can dramatically extend the operational lifetime of the devices. Effective energy management is crucial not only for prolonging device lifespans but also for ensuring that networks can operate autonomously over extended periods without the need for frequent maintenance. The key concerns when minimizing energy consumption revolve around balancing lower power usage with maintaining network connectivity and performance, especially in scenarios where nodes need to be active to respond to network demands promptly.

An example of an algorithm that minimizes energy is given by Jung et al. [65]. They introduce a slot scheduling mechanism designed for TSCH networks named PAAS (Parameterized Adaptive and Autonomous Scheduling), which operates as an autonomous and distributed algorithm, dynamically adapting to traffic intensity, slotframe length and reliability requirements. PAAS is engineered to minimize energy consumption, ensure reliability and autonomously adapt to changing network conditions, providing a parameterized and adaptive slot scheduling solution. The central concept revolves around constructing a slot schedule that adjusts to the offered traffic load. PAAS demonstrated a reduction in energy consumption by up to 63% compared to existing scheduling algorithms, with a reliability of 99.997%.

Aiming for efficient slotframe utilization, other algo-
rithms intend to minimize the length of the slotframe, op-
timizing network resources. Minimizing slotframe length is
crucial for enhancing the responsiveness and throughput of
TSCH networks. By reducing the cycle time of communica-
tions, networks can adapt more quickly to changing condi-
tions and facilitate more timely data transmission. However,
this objective must be balanced against potential increases in
energy consumption, as shorter slotframes typically require
nodes to activate more frequently, potentially draining bat-
tery life more rapidly.

Hashimoto et al. [48] intend to shorten slotframe size by
introducing a per-flow reliability- and delay-aware schedul-
ing algorithm. The proposed method enhances reliability by
introducing additional timeslots for retransmission on each
link, ensuring the fulfillment of required reliability levels.
This not only achieves the necessary reliability and mini-
mizes forwarding delay but also avoids a substantial increase
in the number of allocated slots. Through simulations, the
proposed method showcases a reduction of up to 70%, in
the number of slots allocated to neighbor links of a sink
when compared to conventional methods. This reduction
<figure><img src="image_11.png" /><figcaption>**Figure 9:** Distribution of the application domains of surveyed scheduling algorithms.</figcaption></figure>

contributes to shortening the slotframe size while concurrently meeting the requirements of end-to-end reliability and forwarding delay in industrial WSNs. However, it is important to note that shared cells introduce a vulnerability to collisions, potentially impacting the reliability of retransmissions.

Algorithms with throughput as the primary objective aim to maximize the amount of data transmitted over the network within a given timeframe. Maximizing throughput is a pivotal objective in TSCH networks, particularly when handling high volumes of data across diverse deployment scenarios such as industrial monitoring, smart cities, and multimedia transmissions. Higher throughput ensures that the network can handle larger data loads efficiently, reducing the time it takes for information to traverse from one node to another, thereby enhancing the overall performance and capability of the network. When maximizing throughput, the primary concerns include managing network congestion and ensuring fair resource allocation among nodes, as well as dealing with the potential increase in power consumption due to higher data transmission rates.

Ojo et al. [94] focus on addressing throughput maximization and max-min fair scheduling challenges within centralized TSCH networks. The work introduces a polynomial time algorithm designed for the throughput maximization scheduling problem. Subsequently, an auction-based scheduling algorithm employing a first-price sealed-bid auction mechanism is presented. Simulation results demonstrate that the proposed algorithm achieves throughput performance close to the optimal solution but with significantly lower complexity. However, it is noted that prioritizing throughput may result in increased energy consumption.

### 3.1.3. Application Domain

TSCH scheduling algorithms address specific requirements and challenges of diverse application domains. These domains include Internet of Things (IoT), Industrial Internet of Things (IIoT), Convergecast, Wireless Sensor Networks (WSN) and Industrial Wireless Sensor Networks (IWSN).

The tailored solutions provided by TSCH scheduling algorithms cater to the distinct needs of these domains, ensuring efficient, reliable and timely communication in scenarios ranging from everyday IoT devices to critical industrial processes. The distribution of the application domains of the surveyed works can be seen in Fig. 9.

In the IoT, TSCH scheduling is essential for enabling efficient communication among numerous interconnected devices. IoT networks require managing diverse traffic types and maintaining reliability with minimal energy use, making TSCH's flexible scheduling capabilities highly beneficial. Jung et al. [66] address these challenges by proposing the SSAP framework, designed to maximize network lifetime and meet reliability and latency needs essential in IoT settings. SSAP optimizes TSCH scheduling by decomposing it into slot and channel scheduling, and control of the medium access period, both implemented through scalable, low-complexity mechanisms. This approach enhances channel utilization and conserves energy by adjusting device activity periods, key for battery-operated sensors. While this study significantly advances IoT communication efficiency, it also underscores the need for more dynamic scheduling solutions that can adapt to the evolving and unpredictable conditions of IoT environments.

In IIoT, TSCH scheduling is critical for ensuring reliable and timely communication among industrial devices. Amezcua et al. [5] contribute to this need with the Channel Ranking Scheduling Function (CRSF), which dynamically adjusts the scheduling of slots based on real-time data demands. CRSF uses a buffer-based bandwidth allocation mechanism that scales the number of slots based on the buffer queue size at each node, increasing efficiency in variable traffic conditions typical in industrial settings. The effectiveness of CRSF was compared to established TSCH schedulers like Orchestra and MSF in industrial-like environments. Findings reveal that CRSF maintains a high PDR and low radio duty cycle, effectively conserving energy. However, this efficiency comes at the cost of increased end-to-end latency, highlighting a critical trade-off in IIoT network design—balancing prompt data delivery with network reliability and energy efficiency.

In convergecast scenarios where multiple nodes transmit data to a common sink, optimized TSCH scheduling is crucial for efficient data gathering and minimizing collisions and latency. Oh et al. [89] developed 'Escalator,' an autonomous scheduling scheme for TSCH networks using the RPL routing protocol, which streamlines scheduling by creating a conflict-free schedule without additional information exchange. This innovation results in constant packet transmission delays, even as the number of nodes or traffic volume increases, showcasing lower end-to-end delays and higher PDR compared to existing methods in testbed evaluations. Despite its effectiveness in reducing delays for upward traffic, Escalator's limitation lies in its lack of support for downward traffic, pointing to potential areas for future enhancements in TSCH scheduling for bidirectional communication needs in convergecast networks.
A Survey on TSCH Channel Hopping & Scheduling

<table><caption>**Table 2**

Overview of optimization objectives of surveyed papers</caption><thead><tr><th></th><th>Minimum Latency</th><th>Maximum Reliability</th><th>Minimum Energy</th><th>Maximum Throughput</th><th>Minimum Overhead</th><th>Minimum Slotframe Length</th></tr></thead><tbody><tr><td>TASA [96]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Min et al. [82]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Hashimoto et al. [48]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Kausa [42]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>AMUS [62]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>CLS [23]</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Ojo et al. [91]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>TMS-DC [92]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>QSS [22]</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>EES [93]</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>LLTT [119]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ojo et al. [94]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Javan et al. [55]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Ahrar et al. [3]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>RL-TSCH [87]</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>MSU-TSCH [60]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>SDN-TSCH [124]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Niaz et al. [88]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>HRL-TSCH [67]</td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Tinka et al. [121]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeTAS [1]</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Morell et al. [83]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Wave [113]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DISCA [114]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>OTF [98]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>OTF+PID [32]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Muraoka et al. [86]</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>LLSF [19]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeAMON [4]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Stripe [64]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>e-SF0 [37]</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>DIS_TSCH [53]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Hosni et al. [51]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>E-OTF [105]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>LOST [137]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Hosni et al. [52]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Municio et al. [85]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>ReSF [27]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>TREE [78]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Adaptive PID [136]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>E-MSF [47]</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>LV [125]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSAP [66]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>OST [58]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>LDSF [75]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>REA-6TISCH [41]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>CRSF [5]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Bommisetty et al. [14]</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td></tr><tr><td>DPLLS [13]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RL-SF [101]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>EDSF [134]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mobility-TSCH [61]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Orchestra [36]</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>SPHERE [39]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>PAAS [65]</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>e-TSCH-Orch [104]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Escalator [89]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TESLA [59]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>ALICE [71]</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Park et al. [99]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>A3 [72]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>OSCAR [95]</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>ATRIA [21]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>SSTAS [29]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>LLAS [100]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DCS [6]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ECA [111]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Total</td><td>36</td><td>32</td><td>16</td><td>11</td><td>10</td><td>5</td></tr></tbody></table>

WSNs depend on TSCH scheduling to facilitate organized communication among sensor nodes, a feature especially vital in applications like environmental monitoring. In these applications, it is essential for sensors to not only transmit data efficiently but also conserve energy. Addressing this, Hwang *et al.* [53] introduced a distributed scheduling algorithm, DIS_TSCH, specifically tailored for WSNs. This algorithm uses the RPL topology for network construction and incorporates compact network location information to enable each sensor node to independently compute a timeslot offset and a channel offset within a slotframe window. The innovation of DIS_TSCH lies in its ability to allocate consecutive timeslots to nodes along the same routing path, effectively minimizing the end-to-end packet transfer delay in IEEE 802.15.4e WSNs. Simulation results highlighted by the authors demonstrate that this strategic timeslot allocation not only enhances the speed of data transmission but also contributes to energy efficiency by reducing the time nodes

spend in active communication states. Thus, DIS_TSCH represents a significant advancement in TSCH scheduling, optimizing both communication efficiency and energy use in sensor networks.

IWSNs operate in demanding industrial environments where robust and predictable communication is crucial, particularly in applications like process automation that require precise timing and reliability. Addressing these requirements, Tavakoli *et al.* [119] introduced LLTT, a cross-layer design specifically tailored for reliable and low-latency communication in dense TSCH-based IWSNs. LLTT employs a novel approach by selecting a balanced two-level complete k-ary tree [115] as the topology structure based on the number of network nodes, which enhances the parallelism in TSCH schedules. This k-ary tree structure allows for high parallelization, where each node has k children, optimizing the distribution of communications across the network to reduce collisions and enhance throughput.
<figure><img src="image_12.png" /><figcaption>**Figure 10:** Distribution of solving methods of surveyed scheduling algorithms.</figcaption></figure>

LLTT uses an optimized graph isomorphism algorithm to map this ideal tree structure onto the network's existing connectivity graph. This mapped topology then informs a lightweight TSCH schedule generator, which creates a schedule that maximizes parallel communications and minimizes delays. The effectiveness of LLTT was demonstrated in an in-vehicle testbed experiment involving 31 sensor nodes. Results from these tests show that LLTT not only ensures low-latency data delivery but also excels in handling high data generation rates through its periodic aggregation technique. This leads to a significant reduction in average communication latency, proving LLTT's suitability for high-stakes industrial settings where delay-sensitive operations are common.

### 3.1.4. Solving Method

Developing solving methods for TSCH scheduling presents a challenge that combines the need for algorithmic complexity with the practical constraints of the devices on which these algorithms operate. Effective TSCH scheduling involves optimizing a multitude of factors such as communication slots, energy consumption, and timely data transfer, all of which add layers of complexity to the optimization process. The sophisticated algorithms required to manage these factors are inherently complex and often computationally intensive.

However, the devices implementing these algorithms in TSCH networks are typically embedded systems characterized by limited processing power and battery life. These constraints require solving methods that are both effective and lightweight, balancing computational depth with the practical feasibility of running on resource-constrained devices. Moreover, these algorithms must perform efficiently in real-time, adapting swiftly to dynamic network conditions without the luxury of extensive computational resources.

Additionally, as TSCH networks can vary greatly in size and complexity, the solving methods must be scalable and robust, capable of maintaining performance across different configurations and as network dynamics change. This landscape has led to the development of a range of scheduling

solutions, from highly optimized yet computationally demanding algorithms to simpler, more heuristic approaches.

In addressing the complexities of TSCH scheduling, a variety of solving methods have been developed, each with distinct approaches and implications. Fig. 10 shows the most popular solving methods found. Mathematical programming techniques, such as Linear Programming (LP) and Non-linear Programming (NLP), offer a precise framework to optimize specific objectives. LP is particularly suited for scenarios where relationships and constraints among variables remain linear. It excels in providing precise solutions efficiently, which is ideal for TSCH scheduling tasks like minimizing latency or energy consumption. The Recurrent Low-Latency Scheduling Function (ReSF), developed by Daneels et al. [27], exemplifies the application of LP in optimizing slot allocations to achieve minimal latency, showcasing how LP can effectively streamline network operations.

However, while LP offers clarity and computational efficiency, it may not encompass the full complexity of network dynamics where variables interact in non-linear ways. This is where Non-linear Programming (NLP) comes into play, offering a more flexible approach capable of handling the intricate, non-linear interactions inherent in many real-world TSCH networks. NLP's capacity to model complex relationships makes it indispensable for formulating more comprehensive scheduling solutions. For instance, the work by Ojo et al. [93] illustrates NLP's application in maximizing energy efficiency through a sophisticated model, the Energy Efficient Scheduler (EES), that factors in diverse network conditions and energy metrics.

Despite their advantages, both LP and NLP face significant challenges, particularly when applied in embedded systems with resource constraints. The primary limitation of LP is its inability to effectively manage non-linear system dynamics, which are common in advanced TSCH networks. On the other hand, NLP, while versatile, often requires substantial computational resources to solve, which may not be feasible in real-time or resource-limited scenarios. Additionally, the complexity of NLP solutions can escalate with the size of the network, posing scalability issues.

Graph-based methods provide a powerful framework for modeling and optimizing TSCH scheduling challenges by representing the network as a graph, where nodes symbolize devices and edges denote communication links. These methods use various graph algorithms to efficiently determine optimal schedules or resource allocations.

A notable application of graph-based methods in TSCH scheduling is illustrated by the work of Min et al. [82], who optimize communication in delay-sensitive, heterogeneous wireless industrial networks. This work uses graph theory concepts like matching and coloring to allocate resources effectively within tree topologies, a common structure in many industrial settings. The key to their approach is the strategic use of multiple slotframes, which are varied in size and prioritized based on the urgency and speed of data requirements. This method allows for the efficient handling of diverse traffic types, aligning resource allocation with
<figure><img src="image_13.png" /><figcaption>**Figure 11:** Distribution of AI/ML based scheduling algorithms</figcaption></figure>

the specific demands of each data stream, thereby reducing delays and improving the responsiveness of the network.

However, while graph-based methods are highly effective for optimizing network configurations and are particularly useful in environments with complex topologies, they do come with challenges. The computational complexity of graph algorithms can vary significantly, with more sophisticated algorithms requiring greater processing power. This can pose limitations in resource-constrained environments. Additionally, the static nature of some graph-based approaches may not adequately address the dynamic changes in network topology or traffic patterns, potentially leading to suboptimal performance under changing conditions.

Incorporating AI and ML techniques into TSCH scheduling algorithms represents a significant shift toward more intelligent, adaptive decision-making processes. Although historically only a small fraction (9%) of scheduling enhancements have employed AI/ML techniques, there has been a notable increase in their adoption, with AI/ML-driven solutions rising to 19% of total methods reviewed since 2019. This trend shows the growing recognition of AI/ML's potential to revolutionize scheduling approaches by introducing dynamic learning capabilities that adapt to changing network conditions. Fig. 11 shows the distribution of AI/ML-based scheduling algorithms.

Multi-Armed Bandits (MAB) algorithms, for instance, manage the exploration-exploitation trade-off essential in dynamically changing TSCH networks. Javan *et al.* [55] reconceptualize the scheduling problem as a Combinatorial Multi-Armed Bandit (CMAB) process [20], which allows for dynamic adjustment to channel and timeslot selections based on performance feedback rather than relying on potentially inaccurate Channel State Information (CSI). This method enhances the robustness of the scheduling process, particularly in environments where traditional methods falter due to information inaccuracies. However, under high traffic conditions, the greedy nature of cell allocation in CMAB might restrict resources to less demanding nodes, potentially leading to suboptimal network performance.

Reinforcement Learning (RL) techniques are also increasingly utilized to tackle resource allocation challenges [25]. By framing the scheduling problem within a Markov Decision Process (MDP) and employing Q-Learning algorithms, approaches like RL-TSCH by Nguyen *et al.* [87] enable individual nodes to act as RL agents, making decisions based on their current state and network conditions. This method effectively addresses complex scheduling scenarios, optimizing for factors such as throughput, energy efficiency, and packet delays. Still, the success of such methods hinges on the accuracy of the state information and the learning algorithm's ability to converge on effective policies.

Hierarchical Reinforcement Learning (HRL) [10] takes this a step further by structuring decision-making into multiple levels, as seen in Jurado *et al.* [67]'s HRL-TSCH. This approach divides responsibilities between high-level schedule management and low-level timeslot and channel assignments, allowing for refined control that adapts to both macro and micro network dynamics. Such a structured approach can significantly enhance network efficiency but requires sophisticated implementation to balance the decision-making process across different network layers.

Lastly, Phasic Policy Gradient (PPG) [24] methods, like the one introduced by Bommisetty *et al.* [14], adapt RL principles to refine TSCH scheduling decisions further. By dividing the learning process into distinct policy and auxiliary phases, PPG allows for more nuanced policy adjustments that consider both throughput and energy efficiency. This method represents an advancement over purely distributed or centralized RL approaches, providing a balanced solution that mitigates the limitations of previous models.

Overall, the integration of AI/ML techniques into TSCH scheduling introduces substantial improvements in terms of adaptability and decision-making accuracy. However, these methods also bring challenges related to computational demand, data requirements, and the need for effective integration into existing network infrastructures.

PID (Proportional-Integral-Derivative) controllers [63] are well-established in control theory for dynamically adjusting parameters based on feedback, making them highly effective for TSCH scheduling. Domingo-Prieto *et al.* [32] demonstrate their use in 6TiSCH networks, where PID controllers optimize the queue size and the number of unused slots per slotframe. By calculating the required cells based on real-time traffic demands and adjusting allocations accordingly, this PID-based approach enhances network responsiveness and efficiency.

The main advantage of PID controllers in TSCH networks lies in their ability to adapt allocations dynamically, preventing resource over-provisioning and underutilization. This responsiveness ensures optimal network operation even as conditions fluctuate. However, the success of PID controllers depends on the accuracy of the feedback they receive; delays or inaccuracies can lead to suboptimal performance. Additionally, the complexity of tuning PID parameters to suit specific network scenarios poses a challenge.
<figure><img src="image_14.png" /><figcaption>**Figure 12:** Evaluation methods in surveyed scheduling enhancements</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 13:** Distribution of the evaluation tools used in the surveyed scheduling algorithms</figcaption></figure>

In addition to the traditional and emerging methodologies discussed, several works have explored innovative or hybrid approaches that push the boundaries of TSCH scheduling solutions. These methods significantly enhance scheduling flexibility and efficiency. For example, Ojo et al. [92] employ Genetic Algorithms (GAs) [50] to optimize timeslot and frequency assignments, a technique that has shown considerable success in both cellular [46] and cognitive [44] radio networks. Their approach uniquely addresses TSCH scheduling by maximizing throughput and minimizing deadline violations, showcasing a significant evolution from their earlier work, which used graph-theoretical models.

Another groundbreaking method is presented by Veisi et al. [124], who integrate the principles of Software-Defined Networking (SDN) with TSCH to create SDN-TSCH. This approach redefines network orchestration within TSCH environments, assigning specific responsibilities to the controller, such as time source selection and maintenance of a structured control plane. By establishing a collision-free control plane, SDN-TSCH ensures reliable connectivity through potentially unreliable wireless links.

### 3.1.5. Evaluation Method

The effectiveness and performance of TSCH scheduling algorithms are rigorously assessed through diverse evaluation methods, ranging from simulations to real-life deployments, and often a combination of both, providing a holistic view of algorithmic performance across diverse scenarios.

Deployments offer insights into practical scenarios, demonstrating the algorithms' viability in actual wireless network environments. By implementing algorithms in actual conditions, such as those provided by the FIT IoT-LAB testbed used by Hosni et al. [51] and Jeong et al. [59], researchers can observe firsthand the viability and robustness of their solutions in handling real-world complexities and dynamics. These deployments help validate the scalability and reliability of algorithms, highlighting performance metrics like response time, energy efficiency, and fault tolerance.

Simulations, on the other hand, provide a controlled and scalable platform to analyze algorithmic behavior under various conditions that might be impractical or impossible to recreate physically. Vergados et al. [125], for instance, use the 6TiSCH simulator to methodically test and refine their scheduling strategies by adjusting traffic patterns. Simulations are crucial for stress-testing algorithms against theoretical models and extreme conditions, providing detailed insights into their operational limits and capabilities.

The synergy of both approaches enriches the evaluation process, offering a comprehensive understanding of how TSCH scheduling algorithms perform in different contexts. This approach not only confirms the theoretical effectiveness of algorithms but also tests their practical robustness and adaptability.

An overview of the evaluation method and tools used by the surveyed papers is given in Table 3. The most popular evaluation tools used by the surveyed scheduling enhancements, out of the ones mentioned in Section 2.3, can be seen in Fig. 13. Furthermore, as illustrated in Fig. 12, simulation emerged as the predominant evaluation method in the surveyed literature. The popularity of simulation tools reflects their critical role in the iterative design and testing of TSCH scheduling solutions, allowing for fast prototyping and refinement before real-world application.

### 3.1.6. Network Characteristics

The network characteristics, namely network topology, mobility and 6TiSCH support play important roles in shaping the efficiency and adaptability of scheduling solutions. Understanding these characteristics helps in designing algorithms that are robust and efficient under varying network conditions. An overview of the network characteristics found in all surveyed papers is presented in Table 4.

The topology of a network greatly influences the design and performance of scheduling algorithms. Different topologies like linear, star, tree, and mesh (see Fig. 14) present unique challenges and opportunities. For instance, linear topologies might suffer from higher delays due to the sequential nature of data transmission, requiring scheduling solutions that minimize hop-to-hop communication time.
A Survey on TSCH Channel Hopping & Scheduling

<table><caption>**Table 3**

Overview of evaluation methods and tools used by surveyed papers</caption><thead><tr><th rowspan="2"></th><th colspan="2">Method</th><th rowspan="2">6TiSCH Simulator</th><th colspan="6">Tool</th></tr><tr><th>Simulation</th><th>Deployment</th><th>Contiki OS</th><th>Contiki-NG</th><th>COOJA</th><th>FIT IoT-LAB</th><th>ns-3</th><th>OpenWSN</th></tr></thead><tbody><tr><td>TASA [96]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Min et al. [82]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hashimoto et al. [48]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kausa [42]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AMUS [62]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CLS [23]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ojo et al. [91]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TMS-DC [92]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>QSS [22]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>EES [93]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LLTT [119]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ojo et al. [94]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Javan et al. [55]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Ahrar et al. [3]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RL-TSCH [87]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>MSU-TSCH [60]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SDN-TSCH [124]</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Niaz et al. [88]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>HRL-TSCH [67]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tinka et al. [121]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeTAS [1]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Morell et al. [83]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wave [113]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DISCA [114]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>OTF [98]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>OTF+PID [32]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Muraoka et al. [86]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>LLSF [19]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DeAMON [4]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stripe [64]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>e-SF0 [37]</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DIS_TSCH [53]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Hosni et al. [51]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>E-OTF [105]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>LOST [137]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Hosni et al. [52]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Municio et al. [85]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ReSF [27]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TREE [78]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Adaptive PID [136]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>E-MSF [47]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>LV [125]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSAP [66]</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>OST [58]</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>LDSF [75]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>REA-6TiSCH [41]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CRSF [5]</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Bommisetty et al. [14]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DPLLS [13]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RL-SF [101]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>EDSF [134]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mobility-TSCH [61]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Orchestra [36]</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>SPHERE [39]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>PAAS [65]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>e-TSCH-Orch [104]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Escalator [89]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>TESLA [59]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>ALICE [71]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Park et al. [99]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>A3 [72]</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>OSCAR [95]</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>ATRIA [21]</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>SSTAS [29]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>LLAS [100]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>DCS [6]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>ECA [111]</td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>**Figure 14:** Examples of topologies in a 4 node network</figcaption></figure>

Mesh networks, on the other hand, offer multiple paths for data transfer, which can reduce delays but complicate the scheduling due to increased potential for collisions.

An example of an algorithm tailored for a specific topology is given by Juc *et al.* [64]. In their work, they propose “Stripe”, a distributed scheduling protocol designed to allocate timeslots with temporal alignments that optimize
multi-hop forwarding for minimal delay. The protocol establishes a tree-like topology, where nodes receive optimized bandwidth allocation based on their position in the tree and the weight of the associated subtree to facilitate efficient forwarding.

The consideration of mobility within TSCH networks adds another layer of complexity. Dynamic changes in network topology due to node mobility demand scheduling solutions that can effectively adapt to these variations. Whether mobility is a factor or not significantly influences the robustness and resilience of the scheduling algorithms, particularly in scenarios where nodes may relocate or join and leave the network dynamically.

For instance, Jerbi et al. [60] introduce a dynamic scheduling approach for TSCH that accommodates mobility and adapts to topology changes. Referred to as Mobile Scheduling Updated TSCH (MSU-TSCH), the approach focuses on scheduling timeslots by prioritizing the closest nodes to the sink. MSU-TSCH enables instantaneous and dynamic parent node changes without restrictions on parental shifting or communication traffic. It dynamically configures the assignment between connections and devices when a new device joins the network. Despite its effectiveness, the introduction of node mobility tracking produces notable overhead and dynamic adaptation to traffic patterns is not explicitly considered. Furthermore, MSU-TSCH has the drawback of computing the TSCH schedule independently at each node, which could significantly increase communication delay, which makes it unsuitable for certain delay-sensitive applications.

To address MSU-TSCH's limitations, Jerbi et al. [61] propose Mobility-TSCH. This improved protocol dynamically adjusts timeslot assignments based on traffic volume and latency requirements. It also adapts to topology changes, supports mobility and optimizes timeslot allocation by prioritizing nodes closest to the sink.

Additionally, the support for the 6TiSCH protocol, designed to enhance the capabilities of TSCH networks and introduced in Section 2.2, adds a specific dimension to scheduling considerations. Networks leveraging 6TiSCH benefit from advanced features and functionalities, and scheduling solutions compatible with this protocol can capitalize on its enhancements.

Palattella et al. [98] present the On-The-Fly (OTF) bandwidth reservation module, which operates as a distributed mechanism to dynamically adapt the scheduled bandwidth based on network requirements. Positioned atop the 6top sublayer (see Section 2.2), the OTF module employs a distributed bandwidth allocation algorithm. This algorithm adjusts the number of scheduled cells for a node's neighbors when the data flow deviates significantly. To address potential bandwidth estimation instabilities, the authors implement an allocation algorithm module before forwarding requests to 6top, incorporating configurable over-provisioning through a threshold. Still, the authors acknowledge the challenge of finding the optimal balance

between over-provisioning and minimized communication overhead.

Righetti et al. [105] conduct an extensive simulation analysis of OTF to assess its ability to meet the low latency and high reliability requirements of critical applications targeted by TSCH. The study specifically explores the impact of the RPL protocol and the 6P on OTF's performance, focusing on routing and distributed resource negotiation. The results reveal that OTF's performance is significantly influenced by frequent changes in the preferred parent selected by RPL and failures in resource negotiation, leading to congestion that is challenging for the network to recover from. To address these limitations, the authors propose an enhanced version of the bandwidth reservation algorithm, named Enhanced-OTF (E-OTF). This modified solution incorporates a mechanism that enables nodes to promptly react to congestion, thereby reducing latency and packet loss. The results demonstrate that E-OTF effectively mitigates the effects of parent changes and 6top failures without introducing significant additional energy consumption overhead.

Furthermore, Duy et al. [37] present e-SF0, an enhanced version of the 6TiSCH SF0 presented in Section 2.2.1. They introduce a novel approach where a slotframe is divided into multiple portions, each characterized by a density value representing the ratio of unavailable slot offsets (assigned for communication) to the length of the portion. Nodes use a statistical monitoring function provided by the SF to monitor the density of each portion. When a node intends to add cells to its neighbor, it selects the corresponding slot offsets in the portion with the lowest density value between the two nodes, minimizing the probability of negotiation errors.

Similarly, aiming to enhance 6TiSCH MSF (see Section 2.2.1), Hamza et al. [47] propose a distributed Enhanced Minimal Scheduling Function (E-MSF). The introduced scheme is designed to meet several goals. Firstly, it aims for dynamic scheduling by allocating and deallocating cells without relying on a threshold-based mechanism. Secondly, it targets a reduction of transmitted data during the scheduling negotiation phase by introducing a prediction system model that anticipates the data to be transmitted for each pair of nodes in the next slotframe. Lastly, the design aims to achieve reduced latency by minimizing the overhead control packets, thereby diminishing the end-to-end data transmission delay and the queued data.

### 3.1.7. Design Criteria

Scheduling algorithms can be tailored to incorporate specific features to address diverse requirements. They can be considered to be traffic-aware, priority-aware or energy-aware. These criteria allow for customization based on the specific needs and challenges of the deployment environment. An overview of the design criteria across all surveyed papers is provided in Table 5.

Traffic-aware algorithms have had significant attention in the literature due to the inherent challenges posed by traffic load variations in TSCH networks. The fluctuations in traffic
<table><caption>**Table 4**</caption><thead><tr><th rowspan="2"></th><th colspan="4">Supported Topology</th><th rowspan="2">Mobility</th><th rowspan="2">6TiSCH</th></tr><tr><th>Linear</th><th>Star</th><th>Tree</th><th>Mesh</th></tr></thead><tbody><tr><td>TASA [96]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Min et al. [82]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Hashimoto et al. [48]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Kausa [42]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>AMUS [62]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>CLS [23]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Ojo et al. [91]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TMS-DC [92]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>QSS [22]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>EES [93]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LLTT [119]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Ojo et al. [94]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Javan et al. [55]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Ahrar et al. [3]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RL-TSCH [87]</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>MSU-TSCH [60]</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>SDN-TSCH [124]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Niaz et al. [88]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>HRL-TSCH [67]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Tinka et al. [121]</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>DeTAS [1]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Morell et al. [83]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Wave [113]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>DISCA [114]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>OTF [98]</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>OTF+PID [32]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Muraoka et al. [86]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>LLSF [19]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>DeAMON [4]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Stripe [64]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>e-SF0 [37]</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>DIS_TSCH [53]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Hosni et al. [51]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>E-OTF [105]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>LOST [137]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Hosni et al. [52]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Municio et al. [85]</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td></tr><tr><td>ReSF [27]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>TREE [78]</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Adaptive PID [136]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>E-MSF [47]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>LV [125]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>SSAP [66]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>OST [58]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>LDSF [75]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>REA-6TiSCH [41]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>CRSF [5]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Bommisetty et al. [14]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>DPLLS [13]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>RL-SF [101]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>EDSF [134]</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Mobility-TSCH [61]</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Orchestra [36]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>SPHERE [39]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>PAAS [65]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>e-TSCH-Orch [104]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Escalator [89]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>TESLA [59]</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>ALICE [71]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Park et al. [99]</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>A3 [72]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>OSCAR [95]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>ATRIA [21]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>SSTAS [29]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>LLAS [100]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>DCS [6]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>ECA [111]</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr></tbody></table>

Overview of the network characteristics identified in the surveyed papers

load have a profound impact on network performance. Increased traffic load can induce packet loss and degrade reliability, while decreased traffic load may lead to additional energy consumption. To address these issues, traffic-aware solutions have been proposed to dynamically construct schedules that adapt to changing patterns of network traffic. This adaptability ensures robust performance under varying network conditions. Still, implementing traffic-aware features often increases the complexity of scheduling algorithms. Accurately predicting traffic patterns requires sophisticated analytical models and can introduce significant computational overhead. Additionally, these algorithms must be exceptionally responsive to real-time changes, which demands high processing capabilities and can strain network resources.

For instance, Cheng *et al.* [21] introduce ATRIA, a novel Autonomous Traffic-Aware transmission scheduling

method. This solution aims to address the challenges associated with calculating traffic loads and assigning cells without introducing additional communication overhead. ATRIA enables devices to autonomously schedule transmissions to meet their traffic demand without the need for information exchange with neighboring devices. Each device in the network running ATRIA, can detect its traffic load based on local routing information, choose an optimal slotframe length according to performance requirements specified by the application and subsequently schedule one or more cells based on its specific traffic load.

On the other hand, energy-aware algorithms, while less explored compared to traffic-aware ones, introduce a unique perspective into scheduling. In this category, energy consumption is optimized by strategically scheduling communication activities, contributing to prolonged device lifespan in battery-powered networks [127]. While energy-aware algorithms offer substantial benefits, they also face several challenges. Balancing energy efficiency with network performance demands, such as latency and reliability, is a primary challenge. Overemphasis on energy conservation can lead to increased delays and reduced data throughput, potentially affecting the overall efficacy of the network.

For instance, Van der Lee *et al.* [78], introduce a scheduling algorithm aimed at addressing reliability concerns while considering energy costs. The algorithm functions as a swarm, optimizing communication resources allocated to each device within a short time frame. The main objective is to achieve high reliability in terms of Packet Reception Ratio (PRR) while keeping device energy consumption low. Referred to as the TREE algorithm, it operates at the MAC layer independently of other layers, treating all incoming packets uniformly. TREE employs one packet queue per neighbor, filling these queues as packets await the next available transmission opportunity. The algorithm is guided by two fundamental principles: triggering transmission cell scheduling for a neighbor with a loaded queue and releasing underused or interfering cells. These principles ensure that communication resources are scheduled based on the device's need for each neighbor, while interfering or unused cells are released over time to prevent excessive energy consumption.

Finally, priority-aware algorithms operate by assigning priorities to network nodes and making scheduling decisions based on them. These priorities are generated based on specific algorithms tailored to the network's requirements. The main benefit of priority-aware algorithms is their ability to manage network resources in a way that aligns with the operational goals of the network, such as minimizing delays for critical communications and maximizing throughput for high-importance tasks.

To give an example, the LOST algorithm introduced by Zorbas *et al.* [137] defines the priority as the ratio between a node's generated traffic and its distance from the coordinator. LOST prioritizes nodes with a larger number of packets in their queue, reducing the probability of buffer overflows and decreasing delay.
<table><caption>**Table 5**

Overview of the design criteria of the surveyed papers</caption><thead><tr><th></th><th>Traffic-aware</th><th>Priority-aware</th><th>Energy-aware</th></tr></thead><tbody><tr><td>TASA [96]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>Min et al. [82]</td><td>✓</td><td></td><td></td></tr><tr><td>Hashimoto et al. [48]</td><td></td><td>✓</td><td></td></tr><tr><td>Kausa [42]</td><td>✓</td><td></td><td></td></tr><tr><td>AMUS [62]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>CLS [23]</td><td>✓</td><td></td><td></td></tr><tr><td>Ojo et al. [91]</td><td>✓</td><td></td><td></td></tr><tr><td>TMS-DC [92]</td><td>✓</td><td></td><td></td></tr><tr><td>OSS [22]</td><td></td><td>✓</td><td></td></tr><tr><td>EES [93]</td><td></td><td></td><td>✓</td></tr><tr><td>LLTT [119]</td><td></td><td>✓</td><td></td></tr><tr><td>Ojo et al. [94]</td><td>✓</td><td></td><td></td></tr><tr><td>Javan et al. [55]</td><td>✓</td><td></td><td></td></tr><tr><td>Ahrar et al. [3]</td><td>✓</td><td></td><td></td></tr><tr><td>RL-TSCH [87]</td><td>✓</td><td></td><td></td></tr><tr><td>MSU-TSCH [60]</td><td>✓</td><td></td><td></td></tr><tr><td>SDN-TSCH [124]</td><td>✓</td><td></td><td></td></tr><tr><td>Niaz et al. [88]</td><td>✓</td><td></td><td></td></tr><tr><td>HRL-TSCH [67]</td><td>✓</td><td></td><td></td></tr><tr><td>Tinka et al. [121]</td><td>✓</td><td></td><td></td></tr><tr><td>DeTAS [1]</td><td>✓</td><td></td><td></td></tr><tr><td>Morell et al. [83]</td><td>✓</td><td></td><td></td></tr><tr><td>Wave [113]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>DiSCA [114]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>OTF [98]</td><td>✓</td><td></td><td></td></tr><tr><td>OTF+PID [32]</td><td>✓</td><td></td><td></td></tr><tr><td>Muraoka et al. [86]</td><td>✓</td><td></td><td></td></tr><tr><td>LLSF [19]</td><td>✓</td><td></td><td></td></tr><tr><td>DeAMON [4]</td><td>✓</td><td></td><td></td></tr><tr><td>Stripe [64]</td><td>✓</td><td></td><td></td></tr><tr><td>e-SF0 [37]</td><td>✓</td><td></td><td></td></tr><tr><td>DIS_TSCH [53]</td><td></td><td>✓</td><td></td></tr><tr><td>Hosni et al. [51]</td><td>✓</td><td></td><td></td></tr><tr><td>E-OTF [105]</td><td>✓</td><td></td><td></td></tr><tr><td>LOST [137]</td><td>✓</td><td></td><td></td></tr><tr><td>Hosni et al. [52]</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Municio et al. [85]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>ReSF [27]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>TREE [78]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Adaptive PID [136]</td><td>✓</td><td></td><td></td></tr><tr><td>E-MSF [47]</td><td>✓</td><td></td><td></td></tr><tr><td>LV [125]</td><td>✓</td><td></td><td></td></tr><tr><td>SSAP [66]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>OST [58]</td><td>✓</td><td></td><td></td></tr><tr><td>LDSF [75]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>REA-6TISCH [41]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>CRSF [5]</td><td>✓</td><td></td><td></td></tr><tr><td>Bommisetty et al. [14]</td><td></td><td></td><td>✓</td></tr><tr><td>DPLLS [13]</td><td>✓</td><td></td><td></td></tr><tr><td>RL-SF [101]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>EDSF [134]</td><td>✓</td><td></td><td></td></tr><tr><td>Mobility-TSCH [61]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>Orchestra [36]</td><td>✓</td><td></td><td></td></tr><tr><td>SPHERE [39]</td><td>✓</td><td></td><td></td></tr><tr><td>PAAS [65]</td><td>✓</td><td></td><td>✓</td></tr><tr><td>e-TSCH-Orch [104]</td><td>✓</td><td></td><td></td></tr><tr><td>Escalator [89]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>TESLA [59]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>ALICE [71]</td><td>✓</td><td>✓</td><td></td></tr><tr><td>Park et al. [99]</td><td>✓</td><td></td><td></td></tr><tr><td>A3 [72]</td><td>✓</td><td></td><td></td></tr><tr><td>OSCAR [95]</td><td>✓</td><td></td><td></td></tr><tr><td>ATRIA [21]</td><td>✓</td><td></td><td></td></tr><tr><td>SSTAS [29]</td><td>✓</td><td></td><td></td></tr><tr><td>LLAS [100]</td><td>✓</td><td></td><td></td></tr><tr><td>DCS [6]</td><td>✓</td><td></td><td></td></tr><tr><td>ECA [111]</td><td>✓</td><td></td><td></td></tr></tbody></table>

## 3.2. Summary and Analysis

TSCH scheduling has undergone a remarkable evolution, driven by the search for more reliable, energy-efficient and scalable communication. An overview of the surveyed works in this section is presented in Table 6. Researchers have explored various execution methods, with Orchestra [36] pioneering autonomous scheduling for flexibility, TASA [96] offering centralized control for optimal allocation in stable conditions and DeTAS [1] using distributed decision-making for robustness in dynamic environments.

Centralized scheduling algorithms excel in creating efficient and reliable schedules but face challenges like prolonged initialization times and susceptibility to sudden network changes. Distributed algorithms, while capable of responding swiftly to network changes and reducing signaling overhead, often involve additive communication and may lack prioritization strategies. On the other hand, autonomous

algorithms boast minimal local signaling and node autonomy but struggle with limited awareness of network load and optimization for diverse traffic patterns.

Optimization objectives and design criteria, the guiding principles shaping these scheduling algorithms, are as diverse as the challenges they address. Prioritizing latency, as exemplified by LLSF [19], ensures timely communication in industrial applications. In energy-aware algorithms, like PAAS [65], nodes calculate energy consumption for optimal slotframe design. Traffic-aware strategies, showcased in TESLA [59], dynamically adapt schedules to varying network loads, striking a balance between reliability and energy efficiency.

When prioritizing reliability, a scheduling algorithm can enhance network stability and reduce packet loss, ensuring critical data delivery. However, this may lead to increased latency and energy consumption. On the other hand, algorithms focused on minimizing latency can provide swift data transmission, crucial for real-time applications, but might sacrifice reliability and energy efficiency. Energy-efficient scheduling aims to conserve node power, extending network lifetime, yet this could compromise latency and reliability. Striking a balance between these objectives becomes essential, as emphasizing one aspect often involves trade-offs with others.

The infusion of AI/ML into scheduling solutions has emerged as a transformative trend. Learning algorithms, such as MAB, RL and variants like HRL, are reshaping how TSCH networks handle resource allocation. Works like Javan et al. [55] leverage MAB to navigate the trade-off between exploration and exploitation, achieving commendable performance in dynamic environments. The application of PPG in Bommisetty et al. [14] adds another dimension, enhancing adaptability in TSCH scheduling.

Integrating AI/ML into TSCH networks brings promising advantages, including adaptability to dynamic environments, performance improvement through learning from historical data, self-optimization capabilities, predictive maintenance and efficient traffic prediction. However, this comes with challenges such as increased system complexity, resource-intensive computations leading to higher energy consumption, dependence on substantial training data and limited interpretability of ML models. Careful consideration of these advantages and disadvantages is required to harness the full potential of AI/ML in enhancing TSCH networks, ensuring optimal performance while addressing associated complexities and potential risks.

Evaluation methodologies play an important role in validating the efficacy of these scheduling enhancements. Evaluating in real-world deployments, exemplified by works like Hosni et al. [51] that use the FIT IoT-LAB, provide insights into practical scenarios, demonstrating algorithm viability in dynamic wireless network environments. Simulations, as embraced by LV [125] using the 6TiSCH simulator, offer controlled environments for assessing algorithmic behaviors, ensuring scalability and adaptability under varying conditions.
A Survey on TSCH Channel Hopping & Scheduling

<table><caption>Table 6</caption><thead><tr><th rowspan="2"></th><th colspan="3">Execution Method</th><th colspan="4">Optimization Objective</th><th colspan="4">Solving Method</th><th colspan="2">Evaluation Method</th><th colspan="5">Network Characteristics</th><th colspan="2">Design Criteria</th><th rowspan="2">Application Domain</th></tr><tr><th>Centralized</th><th>Distributed</th><th>Autonomous</th><th>Min Latency</th><th>Min Overhead</th><th>Max Reliability</th><th>Max Energy</th><th>Max Slotframe Len</th><th>Linear Prog</th><th>Non-linear Prog</th><th>Graph</th><th>AI/ML</th><th>Control Theory</th><th>Other</th><th>Simulation</th><th>Deployment</th><th>Linear Topology</th><th>Star Topology</th><th>Tree Topology</th><th>Mesh Topology</th><th>Mobility</th><th>6TISCH</th><th>Traffic-aware</th><th>Priority-aware</th><th>Energy-aware</th></tr></thead><tbody><tr><td>TASA [96]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Min et al. [82]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Hashimoto et al. [48]</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Kausa [42]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>AMUS [62]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>CLS [23]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Ojo et al. [91]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>TMS-DC [92]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>QSS [22]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>EES [93]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>LLTT [119]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>Ojo et al. [94]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Javan et al [55]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Ahrar et al. [3]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>RL-TSCH [87]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>MSU-TSCH [69]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>SDN-TSCH [124]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Niaz et al. [88]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>HRL-TSCH [67]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Tinka et al. [121]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>WSN</td></tr><tr><td>DeTAS [1]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>WSN</td></tr><tr><td>Morell et al. [83]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>Wave [113]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>DiSCA [114]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>OTF [98]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>OTF+PID [32]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Muraoka et al. [86]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>LLSF [19]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>DeAMON [4]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Stripe [64]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>e-SF0 [37]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>DIS_TSCH [53]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Hosni et al. [51]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>E-OTF [105]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>LOST [137]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Hosni et al. [52]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Municio et al. [85]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>ReSF [27]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>TREE [78]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Adaptive PID [136]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>E-MSF [47]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>LV [125]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>SSAP [66]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>OST [58]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>LDSF [75]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>REA-6TISCH [41]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>CRSF [5]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Bommisetty et al. [14]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>DPLLS [13]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>RL-SF [101]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>EDSF [134]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Mobility-TSCH [61]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>Orchestra [36]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>SPHERE [39]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>PAAS [65]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>e-TSCH-Orch [104]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>Escalator [89]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>TESLA [59]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>ALICE [71]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>Park et al. [99]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>A3 [72]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>OSCAR [95]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>ATRIA [21]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>SSTAS [29]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IWSN</td></tr><tr><td>LLAS [100]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr><tr><td>DCS [6]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>Convergecast</td></tr><tr><td>ECA [111]</td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>IoT</td></tr></tbody></table>

Overview of the surveyed scheduling enhancements

## 4. Adaptive Channel Hopping

The application of channel hopping is widespread across various protocols and standards, including IEEE 802.15.1 Bluetooth [7], WirelessHART, ISA100.11a [54] and TSCH. These communication frameworks share a common reliance on a timeslotted methodology for orchestrating network communications. At the start of each synchronized timeslot, every device seamlessly transitions to a new channel using a predetermined hopping sequence pattern.

The incorporation of a channel hopping technique in these protocols aims to mitigate the potential for disruptions in single-channel communications, contributing to enhanced overall network reliability. Furthermore, channel hopping

enhances the overall network bandwidth available for applications by facilitating concurrent transmissions from multiple nodes, up to the number of physically available channels. While the bandwidth for individual nodes remains constant at any given time, the use of up to 16 channels in the 2.4 GHz ISM band significantly boosts peak throughput from 250 kbps to 4 Mbps [16]. This expanded bandwidth supports larger networks with a higher number of devices or enables shorter sampling periods for more precise monitoring of the physical system, all without spatial channel reuse considerations.

However, in dynamic real-world environments, fixed channel hopping configurations encounter difficulties stemming from environmental factors such as electromagnetic
<figure><img src="image_17.png" /><figcaption>Figure 15: Overlapping TSCH and Wi-Fi channels</figcaption></figure>

interference, varying signal propagation characteristics and the presence of coexisting wireless networks. These factors contribute to the degradation of network performance, creating obstacles for the adaptability needed in reliable and efficient communication. Signal attenuation, resulting from phenomena like multi-path fading and interference, exacerbates the problem by increasing the rate of packet transmission failure [122]. This, in turn, leads to numerous retransmission attempts and unnecessary energy wastage [15].

Using the conventional TSCH approach, with fixed channel configurations, challenges arise due to potential interference from neighboring Wi-Fi or Bluetooth networks that operate without coordination [15]. Research conducted [107] revealed that, in addition to packet loss, a TSCH network may experience heightened latency and increased energy consumption in the presence of substantial Wi-Fi interference. This phenomenon arises due to channel overlap between the two standards, as illustrated in Fig. 15.

The motivation for adopting adaptive channel hopping techniques arises from the recognition that network conditions are inherently dynamic. Changes in the radio frequency environment, network topology and the presence of interference sources demand a level of adaptability not achievable through fixed configurations alone. Adaptive channel hopping techniques aim to address this challenge by dynamically adjusting the channel and timeslot configurations based on real-time observations and feedback [130].

It's important to recognize that the use of frequency channels by surrounding radio signals is unevenly distributed. Certain ISM sub-bands experience less congestion or interference than others. This variability in channel occupancy should be dynamically leveraged to strategically navigate across channels, optimizing spectrum use intelligently. Achieving this level of adaptability requires moving beyond the standard TSCH framework.

The implementation of adaptive channel hopping is achieved through the use of denylisting<fnref n="1" /> mechanisms [77].

Denylisting serves as a strategic technique aimed at excluding channels presenting unfavorable conditions from the hopping list. Recent advancements have witnessed the introduction of diverse denylisting schemes designed to facilitate adaptive TSCH channel hopping. However, a prevalent assumption within existing denylisting schemes is the consideration of external interference as having a stationary probabilistic model, implying time-invariant statistical properties [33, 117, 138]. This assumption often proves unrealistic in practical scenarios, where real-life communications involve dynamically evolving underlying processes [135]. Furthermore, the presence or absence of external users linked to other wireless technologies does not consistently exhibit a stable statistical property. Each non-802.15.4 protocol employs distinct regulations for channel selection, user partitioning and more. This complexity results in a dynamic and unpredictable spectrum use pattern that cannot be accurately expressed through a time-invariant probabilistic model, demanding continuous learning and dynamic tracking.

Watteyne et al. [130], conducted pioneering research showcasing the effectiveness of frequency hopping in enhancing reliability compared to single-channel operation. In their experimental study, blind frequency hopping achieved a notable 56% reduction in the average Expected Transmission Count (ETX) and a corresponding 38% decrease in network churn, indicating changes in parent nodes within the routing tree. The authors explored varying denylist sizes and determined that enabling nodes to use the top six channels while denylisting the remaining ten proved to be the optimal solution for their specific scenario.

## 4.1. Taxonomy of Adaptive Channel Hopping Enhancements

Continuing from the objectives set in Section 1.2, this survey aims to integrate channel hopping strategies into our comprehensive analysis of TSCH enhancements. The proposed taxonomy systematically categorizes key components that influence the design and functionality of adaptive channel hopping enhancements. By structuring the classification along five key dimensions—channel assessment methods, denylist scope, channel offset assignment, AI/ML-based approaches, and evaluation methods—we ensure a structured and comparable analysis of existing strategies.

* **Channel Assessment Methods:** Effective channel assessment is fundamental in TSCH networks, as it determines how interference and environmental dynamics are detected and mitigated. Various methods, from passive sensing to predictive modeling, influence how networks decide when to switch channels.

* **Denylist Scope:** The denylist mechanism is crucial for filtering out poor-quality channels, but different

<footnote>we will also adopt the term “allowlisting” instead of “whitelisting” to signify the inclusion of channels with undesirable conditions.</footnote>

<footnote n="1">¹While the term “blacklisting” is commonly used in previous literature, for the purpose of this work, we will be using the term “denylisting” to describe the exclusion of channels with undesirable conditions. Similarly,</footnote>
<figure><img src="image_18.png" /><figcaption>**Figure 16:** Proposed taxonomy of adaptive channel hopping enhancements</figcaption></figure>

strategies exist (local, global or hybrid). This categorization allows us to compare how different scopes impact network resilience.

* **Channel Offset Assignment:** Assigning channels efficiently is key to minimizing collisions and improving robustness. Some strategies rely on assigning a single channel to network links, while others opt to assign multiple channels.

* **AI/ML Based Approaches:** As TSCH networks evolve, machine learning techniques offer promising methods for optimizing channel hopping based on real-time network conditions. This category helps assess whether AI-driven techniques outperform traditional strategies.

* **Evaluation Methods:** The effectiveness of any enhancement depends on how it is tested and validated. This category distinguishes between real-world implementations, testbeds, and simulation-based analyses.

By categorizing these strategies, we can assess their impact on improving TSCH network robustness and performance. This taxonomy not only supports our objective to provide a unified review of scheduling and channel hopping but also highlights how different channel hopping strategies can be integrated to optimize overall network performance. Each class in the taxonomy is designed to offer clear insights into the potential benefits and limitations of various channel hopping techniques, guiding future research towards addressing the identified gaps. The proposed taxonomy is represented in Fig. 16.

In this section, we survey 18 papers that present adaptive channel hopping enhancements. A timeline indicating the

year of publication of each paper along with its current amount of citations is shown in Fig. 17. Similar to what happened with the scheduling enhancements, this timeline highlights a steady growth and peaks in 2018.

### 4.1.1. Channel Assessment

Channel assessment refers to the metric used to determine the suitability of a channel for communication. It plays a crucial role in selecting optimal channels and deciding whether to include or exclude specific channels on a denylist. In this work, three primary metrics were identified for channel assessment: Packet Delivery Ratio (PDR), Received Signal Strength Indicator (RSSI) and Hybrid approaches. A summary of the channel assessment methods used in the papers reviewed is presented in Table 7.

RSSI is a metric used in channel assessment, providing insights into the energy level of the received signal and the overall noise in the channel. RSSI measures the strength of the signal, which helps to estimate the channel's quality based on signal characteristics. A higher RSSI typically indicates a stronger and clearer signal, contributing to improved channel reliability.

For instance, Du and Roussos' pioneering work [33] introduce the A-TSCH protocol that evaluates the channel quality using RSSI. The A-TSCH protocol sets itself apart by selectively hopping among a subset of channels considered "reliable", in contrast to TSCH, which uses all 16 channels in the 2.4 GHz band indiscriminately. This approach places a significant load on the system, resulting in heightened energy consumption and delays over time. The negotiation method, involving denylist piggybacking into EBs, faces inefficiencies and potential delays in updating due to infrequent EB exchanges. Furthermore, there's uncertainty regarding whether neighboring nodes use the same denylist, given the broadcast nature of EBs.

Similarly, Tavakoli et al. [117] present an enhanced version of the A-TSCH solution, known as E-TSCH, featuring two key components. Firstly, it incorporates a novel Non-intrusive Channel-quality Estimation (NICE) technique to assess the quality of the channels through Energy Detection (ED) based on RSSI. Secondly, it includes an Enhanced Beacon hopping Sequence List (EBSL) that selectively uses the strongest channel for broadcasting EBs. The proposed link estimation procedure, while outperforming A-TSCH, is executed exclusively at the sink and is contingent on a maximum acceptable clock drift.

Despite the improvement in denylist distribution using a subset of stronger channels for broadcast EBs, both A-TSCH and E-TSCH encounter challenges in ensuring uniform usage of the same denylist among all nodes due to the absence of acknowledgment packets for beacon transmissions.

PDR is another metric that measures the percentage of successfully delivered packets over a communication channel. It assesses the reliability of a channel by determining how effectively packets are transmitted without loss or errors. Higher PDR values tend to indicate better channel quality.
<figure><img src="image_19.png" /><figcaption>**Figure 17:** Timeline of the surveyed adaptive channel hopping enhancements (the size of each bubble is proportional to its current amount of citations</figcaption></figure>

An example of a PDR based method is introduced by Kotsiou *et al.* [73]. Their method, named LABeL, aims to identify channels for denylisting, particularly for the 6TiSCH protocol stack. Drawing on insights from their prior work [76] that highlighted the limitations of traditional metrics like RSSI, they decide to use PDR as a more accurate metric to measure channel quality. Each node independently computes the PDR for unicast data packets related to specific neighbors and channels by tracking the number of ACKs and the total transmitted packets.

Similarly, Cena *et al.* [17] propose Adaptive Channel Capacity Shaping (ACCS), which operates on a per-link basis and implements a sort of probabilistic denylisting. ACCS evaluates the quality of all channels for each link, describing this quality using the failure probability derived from the PDR. Results indicate significant improvements in both energy consumption and reliability. While there is an observable increase in transmission latency, it remains acceptable for applications with less stringent timing requirements, such as those focused on collecting information from a physical system.

Hybrid approaches integrate both PDR and RSSI metrics to offer a comprehensive assessment of channel quality. By leveraging the strengths of both metrics, hybrid methods aim to provide a more nuanced evaluation, considering both successful packet delivery and signal strength.

For example, in the work of Queiroz *et al.* [103] both PDR and RSSI are used to estimate the quality of the links, using therefore a hybrid approach. In addition to denylisting channels with low quality, this work introduces the concept of a greylist for channels with uncertain quality. The proposed mechanism is compared to the channel hopping strategy used in the TSCH protocol, both with and without channel offset management, specifically in tree topology networks. Simulations show that the triple-channel approach (denylist, greylist, allowlist) outperforms the blind TSCH

<table><caption>**Table 7**

Overview of channel assessment methods in surveyed papers</caption><thead><tr><th></th><th>RSSI</th><th>PDR</th><th>Hybrid</th></tr></thead><tbody><tr><td>A-TSCH [33]</td><td>✓</td><td></td><td></td></tr><tr><td>E-TSCH [117]</td><td>✓</td><td></td><td></td></tr><tr><td>Li et al. [79]</td><td></td><td>✓</td><td></td></tr><tr><td>Shih et al. [110]</td><td></td><td>✓</td><td></td></tr><tr><td>LABeL [73]</td><td></td><td>✓</td><td></td></tr><tr><td>E-TSCH+A-CQE [57]</td><td>✓</td><td></td><td></td></tr><tr><td>Elsts et al. [38]</td><td></td><td></td><td>✓</td></tr><tr><td>AB-TSCH [102]</td><td></td><td>✓</td><td></td></tr><tr><td>Dakdouk et al. [26]</td><td></td><td>✓</td><td></td></tr><tr><td>MABO-TSCH [43]</td><td></td><td>✓</td><td></td></tr><tr><td>Zorbas et al. [138]</td><td></td><td>✓</td><td></td></tr><tr><td>LOST [137]</td><td></td><td>✓</td><td></td></tr><tr><td>E-TSCH+DCS [118]</td><td></td><td></td><td>✓</td></tr><tr><td>Kotsiou et al. [77]</td><td></td><td></td><td></td></tr><tr><td>Kotsiou et al. [74]</td><td></td><td>✓</td><td></td></tr><tr><td>DMABB-CH [56]</td><td></td><td>✓</td><td></td></tr><tr><td>Queiroz et al. [103]</td><td></td><td></td><td>✓</td></tr><tr><td>ACCS [17]</td><td></td><td>✓</td><td></td></tr></tbody></table>

approach by 8% in PDR and improves performance by 51% to 59% over methods without channel offset management.

### 4.1.2. Denylist Scope

A denylisting algorithm can be implemented either at a local or global level, each having its own advantages and drawbacks. Local denylisting involves tailoring denylists at the individual node level, allowing each node to maintain unique list based on its local observations. While local denylisting provides granularity and adaptability, it introduces challenges due to high complexity and the potential for collisions when multiple radio links use the same timeslot, even with varied denylists and channel offsets. A summary of the scope of the denylists employed in the works reviewed is presented in Table 8.

For example, Shih *et al.* [110] present a mechanism for regenerating FHSs when the channel number changes due to denylisting. In this method, each pair of nodes maintain a local denylist, which allows the results to be representative of a large scale network.

On the other hand, the global approach ensures that all nodes in a network share the same list of favorable radio channels [112]. While this approach simplifies management and ensures uniformity, it may not be optimal as radio links
exhibit varying performance based on their locations within the network [76].

Zorbas et al. [138], for instance, examine the shortcomings of a localized denylisting solution proposed in the literature for multi-hop networks and introduce a distributed solution to overcome these challenges. Some existing denylisting solutions for multi-hop TSCH networks rely on centralized multi-offset assignment [43], using multiple channel offsets to increase the likelihood of generating a channel with good performance and avoiding frequency overlap with concurrently transmitting neighbors. However, the authors' analysis reveals a high probability that none of the assigned offsets will produce an available frequency, particularly when numerous channels are already denlisted. To address this issue, the researchers propose a global denylisting approach using a single channel offset and distributing the denlisted channels across the entire TSCH network. With each removal of a denlisted channel, the set of available frequencies is updated, ensuring that the modulo computation consistently produces an allowlisted radio channel.

Moreover, some adaptive channel hopping enhancements employ a hybrid approach, combining elements of both local and global denylisting for a balanced strategy. Others forego denylisting altogether, relying on alternative methods to dynamically adapt to changing channel conditions. The choice between local, global, or a mix of both denylist scopes reflects the trade-offs inherent in achieving adaptability, collision avoidance and overall network reliability.

Elsts et al. [38] propose an upstream and a downstream-driven approach to adaptive channel selection. In the upstream approach, nodes periodically sample RSSI to assess the qualities of candidate channels. Upon detecting a busy channel in the hopping sequence, the nodes search for a replacement using predefined quality thresholds. Once a suitable channel is found, the hopping sequence is updated and new sequence information is rapidly disseminated to downstream nodes through several EB packets, generating a global denylist. In the downstream approach, each downstream node uses a hybrid scope by managing two denylists: a local denylist containing current information about locally detected busy channels and a shared global denylist with the last version of the local denylist acknowledged by the upstream nodes. Only the shared denylist is used for downstream packet receptions, as the transmitting upstream nodes may not be aware of the local denylists of downstream nodes. This ensures effective communication while mitigating the impact of local interference conditions on downstream packet reception.

### 4.1.3. Channel Offset Assignment

The assignment of channel offsets plays an important role in determining the frequency at which nodes communicate during specific timeslots. This assignment can be categorized into two main approaches: single-channel

<table><caption>**Table 8**

Overview of the denylist scope in surveyed papers</caption><thead><tr><th></th><th>Local</th><th>Global</th><th>Hybrid</th><th>None</th></tr></thead><tbody><tr><td>A-TSCH [33]</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>E-TSCH [117]</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Li et al. [79]</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Shih et al. [110]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>LABEL [73]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>E-TSCH+A-CQE [57]</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Elsts et al. [30]</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>AB-TSCH [102]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Dakdouk et al. [26]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>MABO-TSCH [43]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Zorbas et al. [138]</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>LOST [137]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>E-TSCH+DCS [118]</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Kotsiou et al. [77]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Kotsiou et al. [74]</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>DMABB-CH [56]</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Queiroz et al. [103]</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>ACCS [17]</td><td>✓</td><td></td><td></td><td></td></tr></tbody></table>

assignment and multi-channel assignment. In the single-channel assignment scenario, a link is allocated one timeslot and a corresponding channel offset for communication. On the other hand, multi-channel assignment involves assigning one timeslot along with multiple channel offsets to a given link, enabling the negotiation of denylists locally between transmitting and receiving nodes. The channel offset assignment strategy of the surveyed papers is presented in Table 9.

In single-channel assignment, such as the one used by Tavakoli et al. [118], a link operates within a specified frequency for the entire timeslot, simplifying collision avoidance but potentially limiting adaptability. Still, problems could arise if nodes maintain different local denylists. For instance, in Fig. 18, nodes (A, B) and (C, D) have different denylists—specifically, nodes (A, B) remove channels 13–15 and 20–23, while nodes (C, D) remove channels 19–23. Given an ASN of 50, the mapping function assigns the same radio channel to both links, leading to collisions due to parallel transmissions.

In contrast, multi-channel assignment provides the flexibility to dynamically switch between different frequencies within the timeslot. In this case each link reserves multiple channel offsets, allowing it to select the optimal frequency for communication while avoiding denlisted channels. For example, in Fig. 18, link (AB) receives three different channel offsets. The first two offsets map to a denlisted channel and are discarded. The third offset corresponds to a valid frequency and is used for transmission. This approach ensures orthogonality in the distribution of channel offsets, preventing interference between links.

For example, Zorbas et al. [137] employ a localized denylisting method to contain undesirable channels within local nodes without disseminating them throughout the entire network. This approach enhances a channel offset assignment phase, allowing each node to allocate multiple offsets per link. Each node maintains a set of available channel offsets, ensuring enough channel offsets even in high-density areas with numerous parallel links [12]. In the event that the channel generated for one offset by Eq. 1 is denlisted, the node generates a new channel using the next available offsets until an allowlisted channel is obtained. If no allowlisted channel is generated through all possible offsets, the node defers its transmission.
<figure><img src="image_20.png" /><figcaption>Figure 18: Frequency selection method for single and multiple channel offsets</figcaption></figure>

<table><caption>Table 9</caption><thead><tr><th></th><th>Single-Channel</th><th>Multi-Channel</th></tr></thead><tbody><tr><td>A-TSCH [33]</td><td>✓</td><td></td></tr><tr><td>E-TSCH [117]</td><td>✓</td><td></td></tr><tr><td>Li et al. [79]</td><td>✓</td><td></td></tr><tr><td>Shih et al. [110]</td><td>✓</td><td></td></tr><tr><td>LABEL [73]</td><td>✓</td><td></td></tr><tr><td>E-TSCH+A-CQE [57]</td><td>✓</td><td></td></tr><tr><td>Elsts et al. [38]</td><td>✓</td><td></td></tr><tr><td>AB-TSCH [102]</td><td>✓</td><td></td></tr><tr><td>Dakdouk et al. [26]</td><td></td><td>✓</td></tr><tr><td>MABO-TSCH [43]</td><td></td><td>✓</td></tr><tr><td>Zorbas et al. [138]</td><td>✓</td><td>✓</td></tr><tr><td>LOST [137]</td><td></td><td>✓</td></tr><tr><td>E-TSCH+DCS [118]</td><td>✓</td><td></td></tr><tr><td>Kotsiou et al. [77]</td><td>✓</td><td></td></tr><tr><td>Kotsiou et al. [74]</td><td>✓</td><td></td></tr><tr><td>DMABB-CH [56]</td><td></td><td>✓</td></tr><tr><td>Queiroz et al. [103]</td><td></td><td>✓</td></tr><tr><td>ACCS [17]</td><td>✓</td><td></td></tr></tbody></table>

Overview of the channel offset assignment strategy in surveyed papers

While the multi-channel offset scheme supports both centralized and distributed scheduling algorithms, challenges persist in determining the optimal number of channel offsets per link, especially in the presence of extended denylists. The balance between adaptability and collision avoidance remains a key consideration in designing efficient channel offset assignment strategies within TSCH networks.

### 4.1.4. AI/ML based

A subset of the works reviewed use AI to solve the adaptive channel hopping problem. Among the surveyed papers, only 5 out of 18 studies adopt AI/ML-based algorithms to tackle the challenges posed by dynamic network conditions. These approaches harness the capabilities of AI and ML to enable more intelligent and adaptive decision-making.

Notably, four of the surveyed works delve into the application of MABs. In the context of adaptive channel hopping, MAB algorithms provide a framework for dynamically exploring and exploiting different channels, effectively learning over time to make informed decisions that optimize network performance. The way TSCH can be combined with MAB algorithms can be seen in Fig. 19. Starting with a set of offsets, each offset is combined with the ASN using Eq. 1

<figure><img src="image_21.png" /><figcaption>Figure 19: TSCH combined with MAB algorithms</figcaption></figure>

to map to a specific frequency channel. The MAB algorithm then evaluates these mapped channels and selects the most suitable one, in this case, channel 15.

For instance, one of the most prominent enhancements that are AI based is the one introduced by Gomes et al. [43]. The Multihop and Blacklist-based Optimized TSCH protocol (MABO-TSCH), employs a distributed denylist approach, where the hopping sequence is locally constructed through information exchange in the form of normal data packets and acknowledgements. Addressing the challenging task of channel quality estimation, MABO-TSCH leverages MAB optimization techniques in which each physical channel is treated as an arm and nodes estimate their PDR.

Javan et al. [56], used a specific MAB variant known as Multi-Armed Bernoulli Bandit (MABB), in which they pragmatically assume that the influence of external interferences may generally exhibit a non-stationary pattern. The problem of adaptive channel hopping is formulated on top of a multi-offset TSCH schedule using the dynamic MAB formalism [11], with the objective of maximizing
the average PDR. This formulation stands out as more realistic compared to prior work, considering the potential nonstationary of external interference. Drawing on recent advancements in MAB theory [81], a learning algorithm is deployed to compute the channel selection policy. The proposed algorithm boasts low computational complexity, and the physical test setup implementation illustrates its memory footprint within the constraints of embedded devices used in IoT scenarios. Through simulations, the proposed scheme is compared against default TSCH and a state-of-the-art multiarmed bandit-based scheme. The results underscore that accounting for the nonstationarity of interference can enhance network performance in terms of PDR, leading to improved energy efficiency and reduced end-to-end delay.

The use of MABs therefore emerges as a strategic approach to give network nodes the ability to learn and adapt their channel hopping strategies based on real-time feedback. This learning process enables the network to dynamically optimize channel selection by introducing a layer of intelligence.

## 4.2. Summary and Analysis

The section on adaptive channel hopping enhancements provides an exploration of various methodologies designed to enhance the performance of TSCH networks in dynamic and interference-prone environments. Key aspects such as channel assessment, denylist scope, channel offset assignment and the integration of AI-based algorithms were systematically surveyed across 18 relevant papers.

Channel assessment methods, crucial for identifying optimal radio channels, were categorized into PDR, RSSI and Hybrid approaches. Pioneering works like A-TSCH [33] and E-TSCH [117] employ RSSI for channel quality estimation, while others, such as Li et al. [79] and Shih et al. [110], use PDR. Hybrid approaches, integrating more than one metric, were observed in certain works like E-TSCH+A-CQE [57].

The denylist scope was discussed in terms of local and global strategies, with several enhancements adopting either one or a mix of both. Global denylisting ensures that all nodes share the same list of favorable radio channels, while local denylisting allows individual pairs of nodes to have distinct lists. Adaptive strategies leveraging denylisting aim to dynamically adjust channel configurations based on real-time observations.

Channel offset assignment was also addressed, emphasizing the use of multiple offsets to enhance network reliability. Works like MABO-TSCH [43] and LOST [137] proposed multi-channel offset schemes, supporting centralized and distributed scheduling algorithms.

The introduction of AI-based algorithms, particularly MABs and MABBs, marked a novel approach in achieving adaptive channel hopping.

In summary, the surveyed papers showcase the diversity of approaches in addressing the challenges of TSCH networks, underscoring the importance of adaptive strategies, AI integration and robust evaluation methodologies for achieving enhanced reliability and efficiency in dynamic

wireless environments. A full comparison table of the surveyed adaptive channel hopping enhancements is presented in Table 10.

However, challenges persist, particularly in addressing interference from neighboring networks like Wi-Fi and Bluetooth. The dynamic nature of interference and varying channel occupancy patterns require continuous adaptability. The surveyed works highlight the limitations of fixed configurations and advocate for adaptive channel hopping to counter issues like increased latency, heightened energy consumption and packet transmission failures.

# 5. Future Prospects

Despite the significant advancements in scheduling and channel hopping for TSCH networks, there are still areas that could be further enhanced. This section aims to briefly highlight these potential avenues where TSCH networks could be improved.

## 5.1. Scalability and Large-Scale Deployments

Most proposed scheduling and channel hopping enhancements are evaluated through simulations or small testbeds, limiting their applicability to real-world, large-scale deployments. As the number of nodes in a TSCH network increases, synchronization overhead, slot allocation complexity, and interference management become more challenging. Future research should focus on developing highly scalable scheduling algorithms that maintain efficiency in dense networks.

## 5.2. Learning-Based Approaches

The integration of AI and ML in TSCH remains an emerging area. While RL and multi-agent learning techniques have been proposed for scheduling and channel hopping, they often struggle with long convergence times, limited generalizability, and high computational overhead. Future work should explore hybrid AI-driven approaches that combine heuristics with learning-based decision-making to achieve adaptive, real-time optimization. For instance, self-learning schedulers that can dynamically adapt to traffic patterns and environmental changes without requiring predefined rules could be beneficial. Furthermore, the advent of ultra-low power AI accelerators, such as Analog Devices' MAX78000² and Greenwave's GAP9³ presents a promising avenue for overcoming these challenges. These accelerators enable energy-efficient execution of AI models, making it feasible to deploy real-time learning-based solutions in TSCH networks.

## 5.3. Energy Efficiency vs. Performance Trade-offs

Many scheduling algorithms prioritize latency reduction and throughput maximization but often at the cost of increased energy consumption. As TSCH networks are primarily used in low-power wireless applications, achieving

<footnote n="2">² https://www.analog.com/en/products/max78000.html</footnote>

<footnote n="3">³ https://greenwaves-technologies.com/gap9�os/</footnote>
<table><caption>**Table 10**</caption><thead><tr><th rowspan="2"></th><th colspan="3">Channel Assessment</th><th colspan="4">Denylist Scope</th><th colspan="2">Channel Offset Assignment</th><th colspan="2">AI/ML</th><th colspan="2">Evaluation Method</th></tr><tr><th>RSSI</th><th>PDR</th><th>Hybrid</th><th>Local</th><th>Global</th><th>Hybrid</th><th>None</th><th>Single-Channel</th><th>Multi-Channel</th><th>MAB</th><th>MABB</th><th>None</th><th>Simulation</th><th>Deployment</th></tr></thead><tbody><tr><td>A-TSCH [33]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>E-TSCH [117]</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Li et al. [79]</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Shih et al. [110]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>LABEL [73]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>E-TSCH+A-CQE [57]</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Elsts et al. [38]</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>AB-TSCH [102]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Dakdouk et al. [26]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>MABO-TSCH [43]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Zorbas et al. [138]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>LOST [137]</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>E-TSCH+DCS [118]</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Kotsiou et al. [77]</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Kotsiou et al. [74]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>DMABB-CH [56]</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Queiroz et al. [103]</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>ACCS [17]</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Comparison between adaptive channel hopping enhancements

an optimal trade-off between energy efficiency and performance remains a challenge. Future research could focus on dynamic scheduling mechanisms that intelligently switch between energy-saving and high-performance modes based on network conditions.

## 5.4. Robustness in Dynamic Environments

Channel hopping strategies have already improved TSCH's resilience to interference, but existing solutions still exhibit slow adaptation to dynamic spectrum conditions. To improve robustness, future work could integrate more proactive interference mitigation techniques, such as real-time spectrum sensing, predictive channel selection, and adaptive scheduling that reacts to environmental changes. AI-driven channel hopping solutions, able to learn from their environments and predict interference, could further enhance the robustness of TSCH in highly dynamic scenarios.

## 5.5. Security and Privacy Considerations

The deterministic nature of TSCH scheduling makes it susceptible to targeted attacks such as jamming and selective slot blocking. Current security mechanisms, including encryption and authentication, provide basic protection but introduce additional overhead that may not be suitable for ultra-low power devices. Future work should focus on lightweight cryptographic solutions, intrusion detection systems, and trust-based scheduling mechanisms to enhance security without compromising energy efficiency.

# 6. Conclusion

This survey has provided a comprehensive review of scheduling and adaptive channel hopping enhancements for TSCH networks, highlighting their critical role in improving network reliability, efficiency, and scalability. By categorizing and analyzing existing research, we have identified key trends, limitations, and emerging approaches in TSCH scheduling and adaptive channel hopping. Our findings reveal that while significant progress has been made in optimizing scheduling algorithms for latency reduction, energy

efficiency, and throughput maximization, challenges remain in adapting these mechanisms to dynamic network conditions, mobility, and heterogeneous traffic patterns.

Despite these advancements, several challenges remain. TSCH networks must balance latency, energy consumption, and reliability, particularly in large-scale deployments. The integration of AI-driven scheduling with adaptive channel hopping is an open research problem, requiring cross-layer optimization to achieve efficient resource allocation. Additionally, managing the trade-offs between energy efficiency and performance in real-world deployments remains a critical issue.

By addressing these challenges, this survey provides a foundational guide for both new and experienced researchers in the field of low-power wireless networking. It serves as a structured reference to existing solutions, highlights research gaps, and outlines key directions for future innovation. The continued evolution of TSCH scheduling and channel hopping mechanisms, combined with AI-driven strategies and low-power hardware accelerators, will be instrumental in shaping the next generation of energy-efficient and high-performance TSCH networks.

# 7. Acknowledgement

This document is issued within the frame and for the purpose of the OpenSwarm project. This project has received funding from the European Union's Horizon Europe Framework Programme under Grant Agreement No. 101093046. Views and opinions expressed are however those of the author(s) only and the European Commission is not responsible for any use that may be made of the information it contains.

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