# Overcoming the Technical Hurdles of IoT Adoption: the FITNESS Project Vision and Insights

Nicolas Cassiau, Nadjib Achir, Cédric Adjih, Guillaume Andrieux, Walid Bechkit, Siwar Ben Hadj Said, Olivier Boissier, Aymen Bouferroum, Richard Combes, Fabien Courrèges, et al.

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Nicolas Cassiau, Nadjib Achir, Cédric Adjih, Guillaume Andrieux, Walid Bechkit, et al. Overcoming the Technical Hurdles of IoT Adoption: the FITNESS Project Vision and Insights. 2025, 10.5281/zenodo.17119689. hal-05257163

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<figure><img src="image_1.png" /></figure>
# Overcoming the Technical Hurdles of IoT Adoption: the FITNESS Project Vision and Insights

Nicolas Cassiau<fnref n="1" />, Nadjib Achir<fnref n="4" />, Cédric Adjih<fnref n="4" />, Guillaume Andrieux<fnref n="13" />, Walid Bechkit<fnref n="10" />, Siwar Ben Hadj Said<fnref n="2" />, Olivier Boissier<fnref n="8" />, Aymen Bouferroum<fnref n="4" />, Richard Combes<fnref n="7" />, Fabien Courreges<fnref n="3" />, Hiba Dakdouk<fnref n="1" />, Amira Dhaouadi<fnref n="4" />, Jean-François Diouris<fnref n="13" />, Salah Elayoubi<fnref n="7" />, Jérôme Härri<fnref n="6" />, Mihia Kassi<fnref n="8" />, Xavier Lagrange<fnref n="5" />, Yandi Liu<fnref n="3" />, Valeria Loscri<fnref n="4" />, Valerian Mannoni<fnref n="1" />, Sébastien Maudet<fnref n="13" />, Nathalie Mitton<fnref n="4" />, Amina Mokdad<fnref n="5" />, Emmanuel Moulay<fnref n="3" />, Ali Nadar<fnref n="6" />, Rémi Nahon<fnref n="9" />, Romain Négrier<fnref n="3" />, Van-Tam Nguyen<fnref n="9" />, Alexander Pelov<fnref n="5" />, Clency Perrine<fnref n="3" />, Marc Pierre<fnref n="7" />, Sébastien Pillement<fnref n="13" />, Antony Pottier<fnref n="1" />, Charly Poulliat<fnref n="11" />, Yannis Pousset<fnref n="3" />, Mina Rady<fnref n="2" />, Nourhen Sboui<fnref n="3" />, Patrick Sondi<fnref n="12" />, Laurent Toutain<fnref n="5" />

This work is funded by the French National Research Agency (ANR-22-PEFT-0007) as part of France 2030 and the NF-FITNESS project.
This paper was initiated in November 2024 and finalized in March 2025.
Corresponding author: Nicolas Cassiau (nicolas.cassiau@cea.fr).

**ABSTRACT** The Internet of Things (IoT) has emerged as a transformative force, enabling seamless connectivity and data exchange between diverse devices and networks. However, the realization of a truly interoperable, secure, and energy-efficient IoT ecosystem remains a significant challenge. In this paper, we present the vision and the first key findings of the FITNESS project, a comprehensive research initiative funded by the French Research Agency (ANR) as part of the France 2030 program. Our work aims to develop solutions that enable the IoT's full potential, emphasizing scalability, interoperability, security, and sustainability, and enabling seamless connectivity and efficient data transmission between diverse IoT devices and networks. We focus in this paper on three critical areas: IoT architecture and interoperability, the place of Artificial Intelligence in IoT, and energy efficiency. Additionally, we identify and analyze key use cases that demonstrate the practical applications of our research, highlighting the importance of real-world implementation to validate and refine our solutions. Through our dedicated research efforts, we have made significant advances across several key areas, while also laying the groundwork for further development in others. These contributions support the emergence of a more secure, efficient, and interoperable IoT ecosystem, and provide a foundation for adoption by stakeholders seeking to harness its transformative potential.

**INDEX TERMS** Artificial Intelligence; Energy Efficiency; Future Networks; Industry 4.0; Internet of Things; IoT Architecture; Interoperability; Use Cases

**Acronyms**

**AGV** Automated Guided Vehicle.

**AI** Artificial Intelligence.

**AIaaS** AI-as-a-Service.

**AIoT** AI-of-Things.

**API** Application Programming Interface.

**AR** Augmented Reality.

**CNN** Convolutional Neural Network.

**Cobot** Collaborative Robot.

**DRL** Deep RL.

**FSK** Frequency Shift Keying.

**IAKM** Infrastructure Assisted Knowledge Management.

**IoT** Industrial IoT.

**IoT** Internet of Things.

**LEO** Low Earth Orbit.

**LoRa** Long Range.

**LoRaWAN** LoRa Wide Area Network.

**LPWAN** Low-Power Wide-Area Network.

**MAB** Multi-Armed Bandit.

**MAC** Medium Access Control.

**MEC** Multi-access Edge Computing.

**ML** Machine Learning.

<footnote n="1">¹CEA-Leti, Université Grenoble Alpes, Grenoble, France</footnote>

<footnote n="2">²Université Paris-Saclay, CEA, List, France</footnote>

<footnote n="3">³XLIM (UMR CNRS 7252), Université de Limoges, Université de Poitiers, France</footnote>

<footnote n="4">⁴INRIA, France</footnote>

<footnote n="5">⁵IMT Atlantique (IRISA), Rennes, France</footnote>

<footnote n="6">⁶EURECOM Sophia-Antipolis, France</footnote>

<footnote n="7">⁷CentraleSupélec, Université Paris-Saclay, CNRS, L2S, Gif-sur-Yvette, France</footnote>

<footnote n="8">⁸Mines Saint-Etienne, Université Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, Saint-Etienne, France</footnote>

<footnote n="9">⁹LTCI, Télécom Paris, Institut Polytechnique de Paris, France</footnote>

<footnote n="10">¹⁰INSA Lyon, Inria, CITI, UR3720, 69621 Villeurbanne, France</footnote>

<footnote n="11">¹¹IRIT, Toulouse INP, University of Toulouse, France</footnote>

<footnote n="12">¹²Center for Digital Systems, IMT Nord Europe, France</footnote>

<footnote n="13">¹³Nantes Université, CNRS, IETR, UMR 6164, F-44000 Nantes, France</footnote>
**NB-IoT** Narrowband-IoT.

**NN** Neural Network.

**NOMA** Non Orthogonal Multiple Access.

**NS** Network Slicing.

**NWDAF** NetWork Data Analytics Application Function.

**O-RAN** Open-Radio Access Network.

**OS** Operating System.

**PAPR** Peak to Average Power Ratio.

**PHY** Physical Layer.

**QoS** Quality of Service.

**RIC** RAN Intelligent Controller.

**RL** Reinforcement Learning.

**SCHC** Static Context Header Compression.

**SDN** Software Defined Network.

**TCA** Trust Convergence Accelerator.

**TSN** Time-Sensitive Networking.

**UE** User Equipment.

**UPF** User Plane Function.

**URLLC** Ultra Reliable Low Latency Communications.

**V2V** vehicle-to-vehicle.

**V2X** vehicle-to-everything.

**ViT** Vision Transformers.

**WPAN** Wireless Personal Area Network.

# I. INTRODUCTION

THE Internet of Things (IoT) has evolved significantly over the past decade, transforming from a nascent concept to a tangible reality with growing adoption across various industries. This trajectory is anticipated to persist, with IoT poised to drive unprecedented innovation and integration in the years ahead, see Fig. 1. Today, IoT is already present in our daily lives, with a growing array of devices, sensors, and systems interconnected and exchanging data to create a more efficient, sustainable, and connected world. From smart home appliances and wearables to industrial automation and transportation systems, IoT has found concrete utilizations in numerous sectors. For instance, IoT-enabled sensors and devices are being used in industrial settings to optimize production processes, predict maintenance needs, and enhance overall efficiency. In the realm of transportation, IoT is facilitating the development of smart traffic management systems, autonomous vehicles, and connected mobility solutions. Additionally, IoT is playing a crucial role in the healthcare sector, enabling remote patient monitoring, personalized medicine, and improved disease diagnosis. Furthermore, IoT is also being leveraged in smart cities initiatives, where it is being used to optimize energy consumption, waste management, and public safety.

In recent years, a plethora of reviews and surveys, [1]–[6], have been published, providing a comprehensive overview of the challenges that the IoT must overcome in order to realize its full potential. They cover a wide range of topics, including technical challenges as well as economic and social challenges. By synthesizing the findings of these papers, researchers and practitioners can gain a deeper understanding of the complexities of IoT development and identify key areas

<figure><img src="image_2.png" /><figcaption>FIGURE 1. Global IoT market forecast, in billions of connected IoT devices [From IoT Analytics, May 2023]</figcaption></figure>

that require further research and innovation.

The FITNESS project, launched in mid-2023, is a four-and-a-half-year research initiative funded by the French Research Agency (ANR) as part of the France 2030 program. With a strong commitment to advancing the state-of-the-art in IoT technologies it brings together a diverse team of over 60 researchers, including 16 dedicated Ph.D. students. This collaborative effort aims to address the key challenges and opportunities in IoT, fostering innovation and driving progress in this rapidly evolving field. The project is structured around three distinct pillars, each focusing on a specific aspect of IoT development.

The first pillar, Massive IoT aims to develop efficient and flexible IoT solutions that can coexist with both proprietary and standardized protocols, and seeks to ensure scalability, energy efficiency, security, latency, and heterogeneity. The project addresses these challenges through the design of ultra-low power connected objects and the study of Wireless access for massive access. Additionally, it studies end-to-end performance to ensure efficient data transmission, with the goal of creating IoT systems that are not only efficient and cost-effective but also flexible and adaptable to different application domains.

The second pillar, Industry 4.0, addresses the need for mission-critical connectivity in industrial settings. It also recognizes the importance of the digitalization and wireless connectivity for the manufacturing industry, and seeks to simplify the underlying technology to enable new services and applications, such as predictive maintenance and automated quality control. The goal is to create IoT systems that can provide reliable, secure, and high-performance connectivity, while also enabling new use cases such as digital twins, intelligent products, and connected workers (see Fig. 2).

The third pillar, Vehicular and Connected Transport, focuses on significantly improving the safety of connected mobility. As shown in Fig. 3, connected mobility is a rapidly evolving field, with autonomous cars being developed and tested through numerous field trials around the world. This pillar aims to ensure that connected mobility systems are not only efficient and convenient but also safe and reliable. A key challenge is ensuring reliable vehicle-to-everything (V2X) connectivity while driving, and maintaining accurate
<figure><img src="image_3.png" /><figcaption>FIGURE 2. Examples of Industry 4.0 Applications</figcaption></figure>

localization even in the face of potential resource shortages
such as limited network bandwidth.

<figure><img src="image_4.png" /><figcaption>FIGURE 3. V2X communication modes</figcaption></figure>

In this document, we delve into the core challenges tackled by the FITNESS project and present our vision and initial findings in addressing key areas that cut across the three above-mentioned pillars. The structure of the paper is illustrated in Fig. 4. We first explore the intricacies of designing efficient and interoperable IoT architectures (section II). We then investigate the role of Artificial Intelligence (AI) and Machine Learning (ML) in IoT (section III). In section IV, we examine the energy consumption patterns of IoT devices and networks, developing strategies to optimize power usage and promote sustainable IoT deployments. In section V, we provide a consolidated synthesis table that summarizes all technical contributions of the FITNESS project, highlighting their novelty and current maturity level. Lastly, we present in section VI several IoT use cases that demonstrate the practical applications of our research in the three pillars of the project, with a focus on a heterogeneous smart factory use case within the Industry 4.0 domain.

This paper positions itself as an academic white paper, designed to present the strategic vision and multidisciplinary advances of the FITNESS project. By highlighting major technical challenges in the IoT domain our objective is to showcase the research directions pursued and the solutions currently under development, while encouraging dialogue with both academic and industrial communities. In this context, the document covers a broad set of technological building blocks explored within the project. It is important to emphasize that not all studies presented are at the same level of maturity: some have already led to advanced implementations or simulations, while others are still in the exploratory phase. This diversity reflects the collaborative research dynamic at the heart of FITNESS, aimed at addressing cross-cutting and systemic challenges. The level of maturity of each study is indicated in Fig. 4 by a color code.

<figure><img src="image_5.png" /><figcaption>FIGURE 4. Structure of the paper and maturity of the studies</figcaption></figure>

## II. ARCHITECTURE & INTEROPERABILITY

This chapter outlines the comprehensive vision of the FIT-
NESS project for advancing architecture and interoperability
in IoT ecosystems, supported by key research initiatives that
collectively aim to create a seamless, efficient, and secure
IoT environment. Through our work on header compres-
sion, enhanced network analytics, time-sensitive networking,
adaptive communication protocols, trust mechanisms, and
blockchain-based solutions, we aim to establish a unified
framework that ensures interoperability across diverse de-
vices and networks while maintaining the highest standards
of performance and security. This integrated approach reflects
our commitment to building an IoT ecosystem that is not only
highly functional but also resilient against emerging threats.

### A. Header compression for Interoperable, Efficient and Secure IoT

Worldwide development and deployment of IoT is a long
process, which includes both clean-slate approaches and evo-
lutions of multiple legacy systems, especially related to in-
dustrial and critical infrastructures. In certain situations, the
requirements are so strict that the only way to move forward
seems to be sacrificing interoperability, efficiency, or security
to reach a particular objective. The main example for this
is the approach taken by Low-Power Wide-Area Network
(LPWAN) networks, such as LoRa Wide Area Network (Lo-
RaWAN) or Sigfox. Their foundational differences, such as
being standalone platforms that are not interoperable with IP,
made them difficult to integrate with existing IoT/Information
Technology/Operational Technology. systems. However, a
major innovation has enabled the use of interoperable IoT
stacks on these technologies—the Static Context Header
Compression (Static Context Header Compression (SCHC)).

SCHC is an international standard developed by IETF [7], which enables the use of LPWAN — compatible with both 5G Standalone or Non-Standalone architectures — for the operation of historical protocols, which are found to be deployed in hundreds of millions of critical infrastructure devices, such as Smart Electricity, Water, and Gas meters — DLMS/COSEM/IEC 62056 (see IEC 62056-8-12). SCHC operates in a unique manner, which makes it uniquely positioned as a universal optimizer for IoT traffic — any pattern or regularly formatted traffic can be compressed to the absolute minimal useful information, a notion closely related to information entropy. The principle of operation is simple — the IoT device and the core network share a common context — a dictionary — that stores all stable information. The protocol SCHC removes that information and sends over the air only bits that carry useful information. In this regard, SCHC enables the most energy efficient operations of IoT devices, but also reduces the transmission latency for Ultra Reliable Low Latency Communications (URLLC) [8] communications. For example, SCHC is considered for inclusion for the future ambient IoT devices currently being discussed at the 3GPP and Wi-Fi Alliance.

The FITNESS project focuses on the fine integration of SCHC in the 5G data plane, introducing the necessary signaling elements to enable devices to natively use SCHC compression within the network. The architecture of the integration solution is explained in [9] and illustrated in Fig. 5. A new SCHC User Plane Function (UPF), named S-UPF, is introduced. It handles the compression and decompression processes while maintaining full IP compatibility between the device and the application server.

B. Enhancement of NWDAF

Native integration of AI is expected to revolutionize 5G net-
work operations, optimization and management beyond what
we know today (e.g. real-time, zero-touch, intend-based con-
trol and optimization). The success receipt might lie in a tight
integration between Open-Radio Access Network (O-RAN)
RAN Intelligent Controller (RIC) (notably *dApps*) and 3GPP
NetWork Data Analytics Application Function (NWDAF).
At first view, both might have competitive objectives with
vendor specific solutions and network domain silos. Located

<figure><img src="image_6.png" /><figcaption>FIGURE 5. Integrating Static Context Header Compression (SCHC) into 5G Networks [9]</figcaption></figure>

in the 5G Core Network System, the 3GPP NWDAF bene-
fits from native quasi-real-time analytics. Yet, O-RAN RICs
has Application Programming Interfaces (APIs) available for
non-3GPP AI/ML usages. Accordingly 3GPP in recent re-
leases is setting course for NWDAF not only to use AI for
data analytics but to be the cogwheel of AI operations for O-
RAN compliant network optimizations.

The FITNESS project focuses on extending the NWDAF with AI-as-a-Service (AIaaS) functionalities, introducing the required APIs for the NWDAF to identify, locate and retrieve the necessary AI/ML model for a specific network optimization. The architecture of the extended NWDAF is described in [10] and illustrated in Fig. 6, where the three main services provided by 3GPP for network data analytics are depicted. While [10] focused on AI/ML model provision services for AI-powered analytics required by other Network Functions, future work will extend it with open APIs to connect to O-RAN RICs and other Multi-access Edge Computing (MEC) functions for closed-loop network automation.

<figure><img src="image_7.png" /><figcaption>FIGURE 6. AlaaS-NWDAF architecture overview [10]</figcaption></figure>

C. Time-sensitive Networking

A critical challenge arises from the increasing heterogeneity
of industrial networks, encompassing legacy systems with
varying communication protocols and modern wireless tech-
nologies like 5G, which presents significant integration chal-
lenges. These systems must now seamlessly integrate diverse
communication technologies, such as time-slotted Wireless Personal Area Networks (WPANs), Time-Sensitive Networking (TSN) Ethernet, and 5G cellular networks. Ensuring end-to-end latency management across these heterogeneous network segments is essential, particularly for mission-critical and time-sensitive industrial applications that require deterministic data flows. In fact, the increasing reliance on robotics for critical tasks in industrial settings, such as remote operations and real-time control, demands communication networks that can deliver high reliability and ultra-low latency. TSN, a set of standards designed to enhance real-time communication capabilities through time synchronization, traffic scheduling, and resource reservation, is essential for these applications.

The integration of TSN and 5G offers the potential to extend the benefits of TSN (such as its ability to achieve deterministic communication) over wireless links, enabling greater flexibility and mobility in industrial setting. This is a promising research direction, as highlighted by several standardization bodies such as the 3GPP [11] and the 5G Alliance for Connected Industries and Au-tomation [12]. This integration addresses the need for low-latency and deterministic communication for wireless cellular networks, serving applications such as smart factories and autonomous vehicles. One of the main challenges remains ensuring that the deterministic communication of TSN is not degraded when TSN traffic traverses 5G wireless links.

The FITNESS project focuses on developing a Software Defined Network (SDN)-based reconfiguration tool to facilitate the configuration and management of end-to-end TSN mechanisms across the heterogeneous network. In particular, it considers the special case of 5G-TSN integration as specified in 3GPP release 17 (see Fig. 7). The project implements interfacing modules (also called TSN translators) between the 5G system and the TSN Ethernet networks, as well as the different APIs that need to be exposed to the SDN reconfiguration tool. These translators — Network-side and Device-side TSN Translators [11], [13], [14] — translate TSN control frames and data packets into the appropriate 5G formats, while preserving timing information and QoS parameters, enabling the entire 5G system (including the 5G modem (User Equipment (UE)), the access network (gNodeB) and the core network (5G control plane and UPF)) to operate as a transparent virtual TSN switch. This transparency ensures that the two TSN Ethernet domains separated by this 5G network are synchronized to the nanosecond and that the Quality of Service (QoS) and constraints of the critical flows coming from a TSN Ethernet domain are well respected.

## D. Trust convergence Accelerator

The growing complexity of Industrial IoT (IIoT) systems — characterized by heterogeneous devices, fluctuating network conditions, and resource constraints — introduces significant challenges for achieving secure and interoperable architectures. One of the foundational components in addressing these challenges is trust management, which is based

on the continuous monitoring of nodes' behaviors and interactions to evaluate their trustworthiness over time. This process ensures seamless collaboration and secure interactions between devices, particularly in dynamic environments where traditional static approaches often fail [15]. A key issue in trust management is trust convergence, which refers to the time required for the system to accurately evaluate and stabilize the trust levels of network nodes in the presence of dynamic conditions. In IIoT environments, factors such as varying signal quality, interference, and latency can delay convergence and compromise the reliability of the system.

To address these challenges, the FITNESS project is developing the Trust Convergence Accelerator (TCA), a ML-based approach designed to predict and accelerate trust convergence under diverse network conditions. The TCA leverages a novel network condition parameter, *netC*, which aggregates real-time metrics such as the Signal to Noise Ratio, Signal to Interference plus Noise Ratio, packet loss, jitter, latency, and throughput. By dynamically adjusting the trust evaluation process, the TCA enhances trust accuracy and accelerates convergence even in challenging conditions. This approach integrates well with advanced wireless technologies such as Wi-Fi 6, which utilizes features such as Orthogonal Frequency Division Multiple Access and Multi-User Multiple Input Multiple Output to optimize performance in dense and heterogeneous networks [16], [17]. Preliminary simulations suggest that the TCA framework offers significant potential to improve trust management in IIoT systems, aligning directly with the FITNESS project's objectives of enhancing security, scalability, and reliability.

Efforts within the FITNESS project extend beyond trust management in IIoT environments. For instance our research on reputation-based trustworthiness degrees [18] highlights the complexities of maintaining trust in interference-variable vehicular networks and underscores the importance of adaptive solutions across diverse IoT scenarios.

## E. Data-driven adaptive communication for dense IoT networks

In recent years, the field of IoT communications has focused primarily on optimizing traditional network performance metrics such as throughput, energy consumption, and packet delivery rates. However, the informativeness of the transmitted data, which remains the primary objective in many appli-

<figure><img src="image_8.png" /><figcaption>FIGURE 7. Target 5G-TSN integration architecture</figcaption></figure>
cations, has received relatively little attention. The emerging concept of goal-oriented communications aims to address this gap by designing communication protocols that prioritize the informativeness of received data over conventional network metrics [19]. In the context of dense sensor networks, where massive access and adaptive data transmission are critical, existing approaches tend to treat network challenges and data informativeness separately. While network research focuses on improving generic communication protocols, data-driven methodologies often overlook the transmission dynamics, packet losses, etc.

The FITNESS project develops adaptive communication solutions that are explicitly guided by application objectives, with a strong emphasis on maximizing data informativeness while operating under resource constraints and dynamic environmental conditions. This could be particularly relevant for various IoT applications where spatial, temporal, and spatio-temporal correlations may exist, such as mapping physical phenomena [20] and some industrial applications [21]. Our approach focuses on designing novel Medium Access Control (MAC) and routing protocols capable of dynamically adapting to environmental and network fluctuations. By leveraging recent advances in reinforcement learning, we aim to develop decentralized communication strategies that can learn and evolve in real-time, optimizing both network efficiency and data-driven decision-making. Moreover, integrating distributed learning mechanisms across the IoT-edge-cloud continuum is crucial to enhancing network scalability and adaptability.

## F. Leveraging blockchain for authentication and audit in IoT architectures

Besides the aforementioned investigations in section II-D to enhance trust in IIoT, there is a need for fast and efficient authentication schemes that can be supported by the large variety of IoT devices. Major trends in the research works focus on introducing light certificate solution which better fits to the constrained capacities of IoT devices [22], on shifting the authentication efforts on the infrastructure side [23] or on combining light certificate and blockchain at the protocol level, in order to build a solution which is less dependent from the infrastructure management [24].

The FITNESS project aims to develop a solution that incorporates the above-mentioned features, with the advantage of operating at all levels — IoT application servers, communication protocols and IoT devices. Indeed, once a blockchain based approach is adopted for authentication, the underlying technology can be used for its other functionalities: (i) immutability, which allows to trace any event, verify every data, and therefore ensure a full auditability of the IoT application, (ii) intrinsic distributed nature of the blockchain, which guarantees both the management and the storage of the events and data soundly among the IoT application servers and the IoT devices.

The proposed approach should address the specific case of data exchanged through vehicle-to-vehicle (V2V) commu-

nications, outside the infrastructure coverage area, and the merging of these local data with the global infrastructure (Fig. 8). Moreover, the cryptographic functionalities offered by the blockchain allow the use of pseudonym-based certificate which only the issuer may provide the real identity of the owner. In this way, it is possible to authorize the anonymous participation of the IoT devices owned by users that value their privacy. In case of infringement, local legal rules should allow recovering the real identity from the certificate issuer. In developing such approaches, The FITNESS project aims to improve trust and favor the participation of more devices in various IoT cooperative applications.

<figure><img src="image_9.png" /><figcaption>FIGURE 8. Blockchain based approach for authentication and audit in IoT/IoV infrastructure</figcaption></figure>

## III. THE PLACE OF AI

In the realm of the IoT, the integration of AI has emerged as a transformative force, unlocking new possibilities for real-time decision-making, predictive analytics, and autonomous systems. Recognizing the immense potential of AI in revolutionizing IoT, the FITNESS project has dedicated significant efforts to exploring the intersection of these two domains. Our vision is to empower IoT devices and networks with intelligent capabilities, enabling them to adapt, learn, and optimize their behavior in real-time. To achieve this, we focus on key research areas that address the unique challenges and constraints of IoT, such as limited computational resources, energy efficiency, and the need for robust and secure AI models. In this section, we present our findings and contributions in the realm of AI for IoT, showcasing the potential of AI as a driving force behind the next generation of intelligent, interconnected, and autonomous systems.

### A. Model compression

Recent advances in model compression have demonstrated multiple paths toward efficient Neural Networks (NNs) for IoT deployment. In [25], we investigated whether unstructured pruning (i.e. parameters removal) could effectively reduce network depth (i.e. remove entire layers). Through entropy-guided pruning, this work demonstrated that it's possible to identify and remove entire layers while maintaining model performance. The proposed EGP algorithm showed particular promise on popular architectures like ResNet-18 and Swin-T, offering a new direction for model compression.

In [26], we proposed a novel approach for improving model efficiency by identifying and removing spurious correlations
(known as biases) from NN representations through Voronoi cell analysis. This method detects undesirable feature dependencies automatically, without requiring explicit bias labels, enabling more efficient and robust models. Building upon these insights about feature dependencies and bias, [27] made a significant discovery that goes beyond traditional pruning approaches: not only can we extract efficient subnetworks from pretrained models, but these subnetworks can simultaneously achieve both model compression and bias removal.

While conventional pruning methods focus solely on maintaining task performance, our approach revealed that unbiased subnetworks naturally exist within biased networks, requiring no additional training to extract them. This dual benefit —achieving model sparsification while eliminating unwanted biases— represents a significant advancement for IoT applications. The ability to deploy both smaller and more reliable models without retraining is particularly valuable in IoT contexts, where computational resources for model adaptation are limited and trustworthiness is crucial. Current work is aiming at improving the compression rates by applying similar methods during training to more efficiently guide more efficiently the network towards a sparser architecture while preventing specific information from being used.

## B. Dynamic neuron selection

Developing efficient algorithms for resource-constrained systems requires addressing the significant computational and memory costs of training NNs. Two recent works from the FITNESS project have made important contributions in this direction by challenging static approaches to network updates. Both works share a core insight: not all neurons need to be updated at every training step, and the selection of which neurons to update can be dynamically determined during training. This idea manifests in complementary ways in the two approaches. In [28], SCoTTi introduces an adaptive framework that optimizes a threshold parameter to determine which neurons require updates based on their 'velocity'— a measure of how quickly their output is changing. Meanwhile, [29] applies a similar velocity-based principle specifically under extreme memory constraints, demonstrating that dynamic selection can outperform static approaches even with severe resource limitations. Both works leverage the concept of neuron velocity, though with different objectives: SCoTTi aims to minimize overall computational cost while maintaining performance, while [29] focuses on maximizing performance within a fixed memory budget.

These approaches demonstrate that dynamic neuron selection is a powerful paradigm for efficient training in resource-constrained environments. By moving away from static update strategies and towards adaptive approaches that respond to the network's learning dynamics, both works achieve significant efficiency gains while maintaining model performance. Their success suggests that dynamic selection strategies may be key to enabling efficient on-device learning for IoT applications, where both computational and memory resources are limited.

## C. AI as a service

Whereas AI has been one of the most impacting innovation in the past decade, it has been developed in silos (network domain, operator, stakeholders). A total control of the overall AI development and usage allowed to rapidly propose tailored-designed AI functions, yet at the cost of significant energy consumption, slow model training due to lack of pertinent data, or AI duplications in different silos.

The next decade is expected to focus on AI ubiquity, enabling devices to be independent actors of a global AI ecosystem interconnected as an AI-of-Things (AIoT). A central entity toward this goal is an AIaaS architecture capable of IoT-powered smart agent discovery, on-boarding, orchestration, and model sharing in a fully heterogeneous communication-sensing-computing triptych critical to future Composite AI systems.

The FITNESS project introduces such AIaaS platform in [30], depicted in Fig. 9. In a nutshell, it consists of an AlaaS server orchestrating AI training and provisioning, as well as multiple AIaaS clients exposing AI capabilities of potential AI actors. The AIaaS platform remains transparent to AI actors through open Representational State Transfer APIs to register, train or use AI developed by other entities.

To validate the effectiveness of the approach, we conducted a real experiment using the CARLA driving simulator, specifically focusing on ML support in an AI-driven junction risk assessment scenario. The platform is provided as open-source, the full code and video for the Infrastructure Assisted Knowledge Management (IAKM) implementation and its integration with CARLA in a concrete junction management scenario is available on EURECOM gitlab.

In the next steps, this architecture will be extended to be decentralized.

<figure><img src="image_10.png" /><figcaption>FIGURE 9. AlaaS architecture for future AIoT.</figcaption></figure>

## D. Split Computing for Vision Transformers

One standard method for running AI models that exploit data from IoT devices is to use Edge or Cloud Computing. This approach, known as AI edge/cloud offloading, reduces
the computational load on resource-constrained devices by shifting the workload to more powerful servers [31]. However, offloading data to remote servers increases the risks related to privacy and security [32]. A second approach is model compression, which seeks to reduce the complexity and size of learning models while preserving performance as closely as possible to the original model [33]–[35]. A final approach is related to distributed inference and learning. It consists of splitting the model into two distinct parts: a first part (referred to as the head) executed on the end-device and a second part (referred to as the tail) executed on a remote server (Edge or Cloud). This model division can be exploited for training (learning) and inference (computing) tasks. This flexible approach leverages the benefits of split learning for collaborative training and split computing for efficient inference.

Naturally, these approaches have been applied to various model architectures, particularly Convolutional Neural Network (CNN) variants for image and vision tasks. While transformer-based models such as Vision Transformers (ViT) [36] are gaining traction in these domains, most studies on Split Computing still focus on CNNs due to their efficiency in reducing inference delay and bandwidth consumption [37]–[40]. A common strategy in these works is compressing intermediate features before transmission, which effectively minimizes data transfer between the device and server.

Only a few studies in the literature have focused on applying Split Learning to ViT models [41], [42]. These works adopt a strategy where only the embedding layer and the classification layer are placed on the end device, which limits the capabilities of the model splitting. They do not address the full potential of Split Learning, where the key challenge is to find the optimal point that reduces the learning time. Both works are closer to data offloading than true Split Learning.

Research within the FITNESS project focuses on split computing specifically designed for ViTs and the development of adaptive compression methods. Our goal is to reduce inference time, optimize resource usage, and enhance the feasibility of deploying AI models in IoT environments, where bandwidth and computational power are often limited. An initial approach to latent space compression and split point selection has been proposed.

## E. Fast converging reinforcement learning for latency-critical IoT

While a previous section explored efficient training of NNs on the resource constrained IoT devices, this section explores how Reinforcement Learning (RL) algorithms can be designed for quickly converging to optimal solutions for latency critical IoT services. The radio resource allocation problem for latency critical services has been addressed in the 5G literature from the lens of URLLC services. [43] proposed a queuing model that computes the delay outage probability for URLLC packets and determined the amount of resources that would be allocated for the system for reaching the reliability

target. In a more dynamic setting where resource allocation is updated on the fly, [44] proposed a heuristic that selects a resource allocation policy with low energy consumption that reaches the reliability target. However, there is a lack for a provably optimal solution with a low regret.

The FITNESS project developed on a generic framework for optimizing the service rate adaptation policy. We exploit the structure of the problem for restricting the search space of policies to a limited, threshold-based subset that includes the optimal policy, even when the distribution of traffic rivals is unknown. The focus of the project is to adapt this generic framework to different IoT use cases, including delay-constrained and energy-constrained scenarios.

## F. Deep RL in 5G NOMA-MEC-NS for IIoT

Future Generation Wireless Networks enable unprecedented opportunities for applications like predictive maintenance, real-time asset tracking, smart energy management, and industrial automation in IIoT as shown in Fig. 2. These advancements raise the need for diverse QoS [45] requirements. 5G networks leverage advanced technologies like Network Slicing (NS) [46], MEC [47], and dynamic resource allocation to ensure efficient and tailored service delivery for various use cases. The integration of MEC with NS is especially critical in IIoT, where heterogeneous tasks require highly efficient and flexible resource management.

As it is illustrated in Fig. 10 NS addresses this by creating multiple logical networks on a shared physical infrastructure, each tailored to specific QoS demands. MEC complements this by bringing computation closer to users, minimizing latency for delay-sensitive applications and reducing energy consumption through task offloading. Additionally, in the critical context of IIoT, efficient resource utilization is of paramount importance. This can be achieved by enabling multiple users to share the same resources (e.g. frequency and time). Non Orthogonal Multiple Access (NOMA) [48] plays a key role in enhancing spectral efficiency and optimizing resource allocation, making it an essential technology for addressing the high connectivity demands of IIoT applications. However, optimizing resource allocation in this MEC-NOMA-NS framework presents significant challenges, particularly in IIoT applications, where diverse tasks impose non-linear, heterogeneous constraints, resulting in complex, non-convex optimization problems. This necessitates the development of novel approaches to manage and optimize resource allocation effectively, ensuring heterogeneous QoS requirements are maintained while addressing the intricacies of this dynamic and complex environment.

The FITNESS project proposes a novel deep learning approach based on a new Deep RL (DRL) architecture that ensures low complexity and high reliability. This approach will be combined with an appropriate system model to address resource allocation optimization in 5G NOMA-MEC-NS, considering QoS heterogeneity, user mobility and the challenges of a complex, dynamic and realistic environment in the context of IIoT.
<figure><img src="image_11.png" /><figcaption>FIGURE 10. Enabling IIoT with NOMA-Enhanced MEC and Network Slicing</figcaption></figure>

## IV. ENERGY EFFICIENCY

As the IoT continues to grow in scale and complexity, ensuring the energy efficiency of IoT devices and networks has become a critical challenge. The FITNESS project aims at enabling IoT devices and networks to operate with minimal energy consumption, extending battery life, reducing environmental impact, and lowering operational costs. In this section, we present our findings and contributions, showcasing innovative approaches to optimize power usage, enhance resource allocation, and design low-power IoT systems. By focusing on these key research areas, we strive to pave the way for a more sustainable and eco-friendly IoT future.

### A. Evaluation and Analysis of Energy Consumption

The deployment of IoT network implies numerous dissemi-nated nodes for which energy supply and autonomy must be questioned. IoT objects, are often powered by limited and/or intermittent energy sources. To correctly estimate and optimize the lifetime of a sensor node, accurate energy consumption models derived from measurements must be established for various wireless transmission technologies [49], [50]. These models must take into account the circuit energy consumption but also propagation channels and network aspects. Refined energy consumption models, taking into account the environment of the sensor nodes through the network density and the geographical distribution of the nodes, have been proposed in the literature, particularly for LoRaWAN [50] and — in the context of the FITNESS project — Wi-Fi HaLow [51] technologies. By considering the error and collision probabilities and thus the number of retransmissions, these models allow us to estimate the lifetime of the nodes in realistic environments and applications.

The maturity of energy harvesting technologies and low-power microcontrollers opens the prospect of developing batteryless computing for IoT networks and applications. One of the critical challenges in IoT deployment is ensuring the ability to collect and transmit data over the Internet at low energy cost and using sustainable solutions. We thus expect

the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Wireless Power Transmission is a promising solution for some applications. Its efficiency has been evaluated in realistic indoor environment in [52]. Intermittently operating devices present numerous technological challenges in various domains (electronics, Information Technology, etc.).

As part of the FITNESS project, we address these challenges through a multi-pronged approach that includes: predictive modeling of harvested energy, fine-grained estimation of consumption across all components of the IoT stack (sensor, microcontroller, radio), and protocol optimization tailored to energy intermittency. By combining these elements, FITNESS aims to enable energy-aware design and adaptive operation of IoT devices, particularly in scenarios where energy availability is scarce, variable, or unpredictable. This work contributes to the long-term goal of achieving self-sustaining, maintenance-free IoT deployments capable of making intelligent trade-offs between sensing fidelity, data freshness, and longevity.

### B. Low power Multi-Armed Bandits

IoT has revolutionized modern industries by interconnecting devices that can sense, communicate, and process data to perform specific tasks autonomously or cooperatively. Achieving this requires a seamless integration of automatic control systems and communication technologies to support various application areas, such as intelligent traffic control and automatic warehouse management systems [53]. A key challenge in such systems is to efficiently control the remote devices in order to achieve desired objectives in dynamic and uncertain environments. This necessitates real-time decisions-making based on limited information (generated by the device itself or from the surrounding environment), while also considering the trade-offs between exploration and exploitation of different control strategies. Multi-Armed Bandit (MAB) algorithms offer a powerful framework for addressing control problems in such settings [54]. However, MAB algorithms are usually employed in cases in which the controller and the actuator are placed in the same entity, and not connected via wireless links.

In [55] the FITNESS project explores the application of MAB algorithms to control remote devices, which faces considerable challenges primarily represented by latency and reliability. Specifically, we analyze the performance of MABs operating in environments where the action feedback from controlled devices is transmitted over an unreliable communication channel and stored in a Geo/Geo/1 queue. We investigate the impact of different queue sampling strategies on MAB performance, showing how queueing delay can strongly affect the performance of MAB algorithms such as Upper Confidence Bound or Thompson Sampling. Motivated by this fact, we propose a novel stochastic approach. Its performance in terms of regret is evaluated against established algorithms in the literature. Furthermore, as the energy consumption naturally demands high attention in any sustainable
development effort, especially in the presence of resource
poor devices [56], we study in [55] the trade-off between
maximizing rewards and minimizing energy consumption.

The FITNESS project bridges the gap between decision-
making algorithms, unreliable communication environments,
and energy consumption, providing key contributions to the
design of robust and energy-efficient control strategies. These
findings can enhance the reliability and performance in IoT-
based systems in diverse domains such as industrial automa-
tion, smart cities, and beyond.

C. **NTN physical Layer**

The potential of Low Earth Orbit (LEO) satellite constella-
tions to extend the reach of IoT networks has generated sig-
nificant attention, with Long Range (LoRa) and Narrowband-
IoT (NB-IoT) emerging as the most widely studied candidates
for such deployments. Existing works have demonstrated
their feasibility under realistic Doppler and link budget condi-
tions, making them strong contenders for early satellite-based
IoT services.

However, these technologies exhibit inherent limitations when viewed through the lens of scalability, energy efficiency, and Physical Layer (PHY) interoperability. LoRa, while energy-efficient, suffers from low spectral efficiency and limited adaptability. NB-IoT offers better throughput but imposes higher energy and hardware complexity. Both rely on protocol and waveform designs originally optimized for terrestrial use, which may prove sub-optimal under the stringent constraints of satellite backhauls involving high round-trip times and massive device densities.

Rather than reiterating existing performance analyses, FIT-
NESS aims to explore an alternative PHY-layer design tai-
lored to future IoT scenarios in space. The project investigates
a flexible, low-complexity waveform architecture based on
orthogonal modulation schemes using Hadamard codes, com-
bined with low-Peak to Average Power Ratio (PAPR) Fre-
quency Shift Keying (FSK) variants and convolutional cod-
ing. This approach seeks to combine the best of two worlds:
the resilience and simplicity of spread-spectrum techniques,
and the higher spectral efficiency of structured orthogonal
codes.

A key innovation lies in the design of a digital receiver with turbo-processing capabilities, intended to approach near-Shannon performance in highly constrained IoT settings. The envisioned architecture targets not only satellite uplinks, but also aims to provide a PHY unification layer that can bridge disparate terrestrial and non-terrestrial access schemes, including LoRa, Orthogonal Chirp Division Multiplexing, and custom spread-spectrum protocols.

These developments are currently in the conceptual and
simulation design phase. Future work will focus on evaluating
these waveform candidates in realistic LEO channel models
and assessing their performance in terms of bit error rate,
energy per bit, synchronization robustness, and receiver com-
plexity.

D. Energy-Efficient Cross-Layer Design for LoRaWAN

LoRaWAN is a flagship technology in the LPWAN landscape,
widely adopted for its low energy consumption and extended
communication range. However, most current optimizations
follow a layer-by-layer approach based on the traditional OSI
model [57]. This compartmentalization limits overall effi-
ciency, particularly in long-term deployments where nodes
are difficult to access.

FITNESS explores an integrated approach that combines cross-layer design, [58], dynamic slicing, and intelligent local scheduling to optimize resource usage on IoT nodes. Dawaliby et al. [59] conducted a pioneering study on adaptive dynamic network slicing in LoRa networks, proposing a dynamic inter-slicing algorithm based on Maximum Likelihood Estimation and an intra-slicing strategy to optimize resource allocation. Their research demonstrated the effectiveness of network slicing in improving QoS and isolation between slices in LoRa networks. However, energy efficiency trade-offs and lack of real-world validation remain open challenges for practical deployment.

To achieve coordination of decision-making across the
physical, MAC, and application layers, taking into account
both traffic priorities and energy constraints, the FITNESS
project builds upon RIOT Operating System (OS), an open-
source, real-time OS that is lightweight and modular—ideal
for constrained devices. RIOT already supports fine-grained
scheduling, energy management, and integrated network pro-
tocols. FITNESS aims to extend it with an adaptive slicing
layer that can isolate different traffic types (critical, periodic,
best-effort) and dynamically schedule the radio module’s ac-
tivity and sleep periods.

Ultimately, this architecture will be tested in representa-
tive deployments such as smart agriculture or environmental
monitoring. The objective is to maximize node longevity
while ensuring stable and differentiated network performance
according to application requirements.

E. Resource deactivation policy for energy efficiency

While the energy consumption of resource-constrained IoT
devices is essential, as explained above, reducing the energy
consumption on the network side is, on the other hand, also
of high importance. The intermittent nature of IoT traffic
which may also be latency-critical, puts a high pressure on
the network for ensuring a high availability. This results in
a high energy cost, as the network resources have to remain
active to serve incoming traffic. On the flow level, when sleep
mode is implemented on the scale of seconds or minutes,
[60] considers activating/deactivating non-overlapping fem-
tocells within a macro base station coverage, using complete
partial, or delayed information about user localization and
traffic load. [61] investigates sleep mode levels with different
power values and transition times. For sleep mode operating
at smaller time scales (e.g. packet level), [62] proposed a
resource activation/deactivation scheme in a virtualized 5G
network setting. Here, the policy is optimized at a small
tile scale to enable advanced sleep mode while taking into
account activation/deactivation delays of resources. However, these algorithms typically assume prior knowledge of the incoming traffic distribution to derive the optimal policy.

As part of the FITNESS project, we are designing a novel learning-based framework that addresses this challenge by enabling real-time, distributed control of energy-hungry infrastructure elements based on minimal observations and without requiring explicit knowledge of traffic dynamics. Our approach leverages a low-regret DRL strategy capable of autonomously identifying energy-saving opportunities while maintaining service quality. This work lays the foundation for a new class of intelligent, self-adaptive IoT infrastructures, where energy efficiency is not statically configured, but dynamically learned and enforced at runtime across heterogeneous network segments. Initial simulations are ongoing, with deployment in emulated edge environments planned in the next phase of the project.

## V. SYNTHESIS

The FITNESS project addresses a wide range of research challenges related to the deployment of scalable, secure, and energy-efficient IoT systems. These challenges span multiple domains — from protocol design and AI for constrained devices to cross-layer architectures and energy-aware communications. To provide a consolidated view of the work conducted within the project, Table 1 summarizes the main contributions presented in this document.

Each row corresponds to a technical study or design effort described in the paper. For each, we highlight the addressed limitations in the state of the art, the specific innovation proposed within FITNESS and the current level of maturity (with the same color code than in Fig. 4). This table aims to offer a high-level perspective on how these efforts align with FITNESS's overarching vision and where they currently stand in terms of development and validation.

## VI. USE CASES

Defining IoT use cases is a critical step in the development and deployment of IoT systems, as it enables stakeholders to understand the specific requirements, constraints, and expected outcomes of IoT applications. By identifying and analyzing these use cases, we can ensure that IoT technologies developed within the FITNESS project are tailored to meet the unique needs of different industries, applications, and environments.

### A. FITNESS, a large scope of addressed challenges

The FITNESS project addresses a wide range of challenges arising from real-world IoT deployments across multiple domains, including Industry 4.0, massive IoT and Vehicular IoT. Each use case highlights specific requirements in terms of network performance, energy efficiency, security, and system adaptability. Tables 2 to 4 summarize the key requirements identified for each use case, the FITNESS contributions that are designed to address them, and the expected impacts. While quantitative evaluation is still ongoing, the table out-

lines the technological directions and improvements that FITNESS aims to deliver, supporting more scalable, efficient, and trustworthy IoT infrastructures across diverse application scenarios.

### B. Mapping Application Needs to IoT Solutions: An Industry 4.0 Focus

The use case “Upgrading an existing production system” has been selected within the “Industry 4.0” domain for its alignment with the FITNESS project’s objectives. It illustrates the complexity of integrating diverse technologies, systems, and protocols in heterogeneous industrial environments combining legacy machinery and modern IoT devices.

In this case, a heavy materials manufacturer faces several challenges. Its Ethernet-based infrastructure limits flexibility and scalability. The manual handling of heavier goods has become impractical as demand increases, while maintaining production speed now requires more operators for repetitive precision tasks. In parallel, training the workforce to adapt to new technologies has emerged as a significant obstacle. To address these issues, potential solutions include the introduction of Automated Guided Vehicles (AGVs) to streamline material transport, Collaborative Robots (Cobots) to assist in precision tasks and reduce physical strain, and Augmented Reality (AR) systems to accelerate operator training and minimize errors. These technologies, however, bring additional demands on the communication infrastructure.

The FITNESS project proposes a decision-support methodology, helping industrial stakeholders map application needs to technological options, and make sustainable, informed choices for their Industry 4.0 transformations: Table 5 summarizes the throughput and latency requirements associated with their deployment, underlining the critical role of robust and responsive networks for ensuring monitoring, predictive maintenance, and remote operation.

While existing technologies such as 5G, 4G, and Wi-Fi, can technically support these needs, FITNESS recognizes that network selection should not rely solely on performance metrics. Economic, social, and environmental factors must also be taken into account; our research continues in this direction.

## VII. CONCLUSION AND NEXT STEPS

In this paper, we have explored the transformative potential of integrating the advanced technologies explored in the FITNESS project into IoT ecosystems. Our analysis demonstrates how these solutions can effectively map to diverse IoT use cases, addressing the multifaceted challenges inherent in modern IoT deployments.

By examining architectural innovations, the integration of AI, and strategies for energy efficiency, the FITNESS project has delivered a set of promising solutions — some already implemented, others currently under development — to address key constraints in IoT systems. This work enhances the scalability, sustainability, and adaptability of future IoT deployments in an increasingly dynamic technological land-
<table><caption>TABLE 1. Synthesis of FITNESS contributions, their novelty, and maturity</caption><thead><tr><td>Section</td><td>State of the Art</td><td>FITNESS contribution</td><td>Maturity</td></tr></thead><tbody><tr><td>II.A Header Compression for Interoperable, Efficient and Secure IoT</td><td>SCHC used in LPWAN; not integrated in cellular cores</td><td>Embedding SCHC in 5G UPF for URLLC traffic with minimal overhead</td><td>Architecture validated</td></tr><tr><td>II.B NWDAF Enhancement through AIaaS</td><td>NWDAF offers basic analytics; lacks integration with AI services</td><td>AIaaS provisioning model integrated with NWDAF for smart analytics</td><td>Architecture validated</td></tr><tr><td>II.C Time-Sensitive Networking and 5G Integration</td><td>No end-to-end TSN mechanisms across the heterogeneous 5G network</td><td>SDN-based orchestration + DS-TT/NWTT translators across domains</td><td>Modular components prototyped</td></tr><tr><td>II.D Trust Convergence Accelerator</td><td>Static convergence; lacks responsiveness to network dynamics</td><td>ML-based “netC” indicator for fast trust adjustment in wireless links</td><td>Preliminary simulations</td></tr><tr><td>II.E Data-Driven Adaptive Communication</td><td>Static protocol selection; little integration of data semantics</td><td>Maximizing data informativeness under resource constraints and dynamic environmental conditions, relying on RL</td><td>Initial framework</td></tr><tr><td>II.F Leveraging Blockchain for Authentication and Audit</td><td>Need for fast and efficient authentication schemes that can be supported by the large variety of IoT devices</td><td>Blockchain for authentication, management, tracing and storage of events and data</td><td>Initial framework</td></tr><tr><td>III.A Model Compression for Edge AI</td><td>Pruning & quantization widely used; bias-aware pruning less explored</td><td>Dual-purpose pruning that compresses and removes representational bias</td><td>Research result</td></tr><tr><td>III.B Dynamic neuron selection</td><td>Static neuron updates; not adaptive</td><td>Neuron velocity-based update strategies (SCoTTi) for efficient learning</td><td>Published research</td></tr><tr><td>III.C AI as a Service for Edge IoT</td><td>Edge-cloud AI orchestration fragmented; lacks portability</td><td>IoT-powered smart agent discovery, onboarding, orchestration, and model sharing</td><td>Real experiment</td></tr><tr><td>III.D Split Learning for ViT</td><td>Split learning used mostly for CNNs; few ViT studies</td><td>Partitioning & compressing ViT for constrained IoT inference</td><td>Proof-of-concept</td></tr><tr><td>III.E Fast-Converging Reinforcement Learning for IoT</td><td>Convergence often slow; poor real-time behavior</td><td>DRL with fast regret bounds + reduced action spaces for IoT (delay and energy constrained scenarios)</td><td>Initial framework</td></tr><tr><td>III.F DRL in 5G NOMA-MEC for IIoT</td><td>Complex, non-convex resource allocation optimization problems</td><td>DRL for joint resource, user & QoS orchestration with MEC/NOMA</td><td>Initial framework</td></tr><tr><td>IV.A Evaluation and Analysis of Energy Consumption</td><td>Models often limited to PHY layer or protocol-level</td><td>Cross-layer consumption modeling + harvesting prediction</td><td>Measurement-based</td></tr><tr><td>IV.B Low-Power Multi-Armed Bandits</td><td>Bandits used in scheduling; rarely combined with energy models</td><td>MAB adapted for low-power devices + contextual inputs</td><td>Published research</td></tr><tr><td>IV.C Physical Layer Explorations for Satellite IoT</td><td>LoRa/NB-IoT studied in LEO; modulation innovation lacking</td><td>Hadamard/FSK waveform + turbo decoding in LEO context</td><td>Early modeling</td></tr><tr><td>IV.D Energy-Efficient Cross-Layer Design for LoRaWAN</td><td>OSI model dominates; few real-time cross-layer schedulers</td><td>Joint MAC/PHY/app coordination + slicing in RIOT OS</td><td>Under development</td></tr><tr><td>IV.E Resource Deactivation Policy for Energy Efficiency</td><td>Sleep control static or heuristic; no learning</td><td>DRL strategy for autonomous node deactivation decisions</td><td>Initial framework</td></tr></tbody></table>

<table><caption>TABLE 2. Selection of Industry 4.0 use cases and FITNESS studied solutions</caption><thead><tr><td>Use Case</td><td>Key requirement</td><td>FITNESS contribution</td><td>Expected impact</td></tr></thead><tbody><tr><td>Upgrading an existing production system</td><td>Selecting the appropriate network technologies</td><td>Decision-support grid based on application constraints*</td><td>Support informed, multi-criteria selection of network technologies for optimized Industry 4.0 deployments</td></tr><tr><td>Remote control of devices over wireless channels</td><td>Reliable and energy-efficient remote control over constrained wireless networks</td><td>Multi-Armed Bandits; Time-sensitive networking</td><td rowspan="3">Improved factory efficiency by reduced delays, minimized downtime and enabled seamless coordination between devices, leading to faster production and lower operational costs</td></tr><tr><td>Heterogeneous devices management</td><td>Maintaining secure and trustworthy communications across diverse devices and changing network conditions</td><td>Trust convergence accelerator</td></tr><tr><td>AI-based managing system for factory devices</td><td>Reliable communication, fast response times, efficient resource management, ability to adapt to changes in device needs and movements</td><td>Novel Deep Reinforcement Learning approach</td></tr></tbody></table>

<footnote>*Details in section VI-B</footnote>
<table><caption>TABLE 3. Selection of Massive IoT use cases and FITNESS studied solutions</caption><thead><tr><th>Use Case</th><th>Key requirement</th><th>FITNESS contribution</th><th>Expected impact</th></tr></thead><tbody><tr><td rowspan="3">Environment monitoring, smart agriculture</td><td>Constrained battery power, hard-to-access environments</td><td>Crosslayer design (including slicing) + RIOT OS-based edge devices</td><td>Reduced operational costs, faster data processing for real-time decision-making, and enhanced reliability in monitoring systems, which result in more sustainable and cost-effective agricultural practices</td></tr><tr><td>Energy efficiency, latency</td><td>Integration of SCHC in the 5G data plane</td><td></td></tr><tr><td>Informativeness of transmitted data</td><td>Data-driven adaptive communication protocols</td><td></td></tr><tr><td>Non-Terrestrial applications</td><td>Low device energy consumption, high Doppler resilience</td><td>New Physical Layer, advanced access mechanisms</td><td>More stable and reliable connections in areas with weak or no traditional network coverage</td></tr><tr><td>Smart city</td><td>Low network energy consumption</td><td>RL schemes that converge rapidly to the optimal sleep mode policy</td><td>More sustainable, eco-friendly urban environment with improved city services</td></tr></tbody></table>

<table><caption>TABLE 4. Selection of Vehicular IoT use cases and FITNESS studied solutions</caption><thead><tr><th>Use Case</th><th>Key requirement</th><th>FITNESS contribution</th><th>Expected impact</th></tr></thead><tbody><tr><td rowspan="3">Dynamic and Mobile IoT for Connected Vehicles</td><td>Autonomous mapping of communication capacity</td><td>Autonomous mobile robotics and SLAM† approach.</td><td rowspan="3">Seamless connectivity, better route optimization, and enhanced traffic management</td></tr><tr><td>Deployment of IoT considering mobility and dynamic topologies</td><td>Modeling IoT networks as dynamic graphs for optimized decision-making</td></tr><tr><td>Low-latency reactions to traffic conditions</td><td>Fast-converging RL algorithms for quick adaptation</td></tr><tr><td rowspan="2">Securing V2X Communications</td><td>Authentication and trust establishment both in and out of infrastructure coverage</td><td>Blockchain-based modeling of V2X communications with non-repudiable transaction management</td><td rowspan="2">Enhanced autonomous driving, reduced accidents, and increased public trust in connected transportation systems</td></tr><tr><td>Safe and efficient navigation through complex intersections</td><td>Alaas assisting automated driving for real-time decision-making</td></tr></tbody></table>

†Simultaneous Localization And Mapping

scape. Our contributions also highlight the essential role of interdisciplinary collaboration in tackling these complex challenges.

Looking ahead, the FITNESS project will continue research and development in these areas to fully realize the potential of IoT. Future work focus on refining these technologies, exploring new applications, and addressing challenges such as security, hardware simplification, privacy, and interoperability.

By addressing key challenges and demonstrating practical applications, this work not only contributes to the academic discourse but also provides actionable insights for industry practitioners.

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<table><caption>TABLE 5. Example of FITNESS decision grid for the Industry 4.0</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Network requirement</th><th rowspan="2">Recommended network Technologies for the Pack (AGV+Cobot+AR)</th></tr><tr><th>Exchanged data</th><th>Data rate</th><th>E2E latency</th></tr></thead><tbody><tr><td rowspan="10">Industry 4.0 technologies</td><td rowspan="5">AGV</td><td rowspan="2">Uplink</td><td>Location data</td><td>≤ 10 Mb/s</td><td>≤ 200 ms</td><td rowspan="2">5G Network Technology</td></tr><tr><td>Operational status</td><td>≤ 100 kb/s</td><td>≤ 1s</td></tr><tr><td rowspan="3">Downlink</td><td>Load, motor temperature and energy consumption</td><td>≤ 10 Mb/s</td><td>≤ seconds</td><td rowspan="3">Enables mobility (for AGVs)<br>Data rates: 1-10 Gbps<br>Suitable latency: &lt;1 ms<br>High connection capacity</td></tr><tr><td>Video stream</td><td>≤ 100 Mb/s</td><td>&lt;500 ms</td></tr><tr><td>Navigation instructions</td><td>≤ 10 Mb/s</td><td>≤ seconds</td></tr><tr><td rowspan="5">Cobot</td><td rowspan="2">Uplink</td><td>Software reconfigurations/updates</td><td>≤ 100 Mb/s</td><td>Non-critical, high tolerance</td><td rowspan="2">4G Network Technology</td></tr><tr><td>Joints location and speed</td><td>≤ 10 Mb/s</td><td>≤ 100 ms</td></tr><tr><td rowspan="3">Downlink</td><td>Operational status</td><td>≤ 100 kb/s</td><td>≤ 1s</td><td rowspan="3">Enables Mobility<br>Data rates: 300-600<br>Mbps<br>Suitable Latency: 20-50 ms<br>Reliable connection</td></tr><tr><td>Load and Force data on end-effector</td><td>≤ 10 Mb/s</td><td>≤ 100 ms</td></tr><tr><td>Motor temperature, Energy consumption</td><td>≤ 10 Mb/s</td><td>≤ seconds</td></tr><tr><td rowspan="5">AR</td><td rowspan="2">Uplink</td><td>Motion and security instructions</td><td>≤ 10 Mb/s</td><td>≤ 50ms</td><td rowspan="2">Wi-Fi</td></tr><tr><td>Software reconfigurations/updates</td><td>≤ 100 Mb/s</td><td>Non-critical, high tolerance</td></tr><tr><td rowspan="3">Downlink</td><td>User Position and Orientation</td><td>≤ 100 kb/s</td><td>≤ 20 ms</td><td rowspan="3">Enables Mobility<br>Data rates: 11 Mbps-10 Gbps<br>Latency: 7-25ms<br>Frequencies exposure (certain norms)</td></tr><tr><td>Operational status</td><td>≤ 100 kb/s</td><td>≤ 1s</td></tr><tr><td>Environmental Image flow</td><td>≤ 250 Mb/s</td><td>&lt;150 ms</td></tr><tr><td rowspan="2">Downlink</td><td rowspan="2">Uplink</td><td>Image flow (Immersion)</td><td>≤ 250 Mb/s</td><td>&lt;150 ms</td><td rowspan="2"></td></tr><tr><td>Software reconfigurations/updates</td><td>≤ 100 Mb/s</td><td>Non-critical, high tolerance</td></tr></tbody></table>

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NICOLAS CASSIAU received the M.S. degree in Signal Processing in 2001 from Polytech'Nantes, France. Since then he is a Research Engineer and Project Manager at CEA-Leti in Grenoble, France. His fields of interest are digital wireless communications and algorithmics, focusing particularly on physical layer design. He is currently the coordinator of the FITNESS project — France 2030 PEPR-NF program — and the deputy coordinator of the European-Korean SNS project 6GARROW.

NADJIB ACHIR joined INRIA Saclay as a researcher in September 2024 and is a permanent member of the TRIBE team. Prior to this, he was an associate professor at the University Sorbonne Paris Nord. He received a Ph.D. in computer networks from Pierre & Marie Curie University, Paris 6, France, in 2003 and his HDR in 2014 from the University Sorbonne Paris Nord. His research interests include Mobile-Edge Computing and Networking, Vehicular Networks, Unmanned Aerial Vehicle Networks, Wireless/Mobile Networks, and Privacy-Aware Passive Sniffing. He is currently involved in several research projects, including the FITNESS project, where he serves as a co-pilot. As part of his professional activities within the computer networking research community, he has served on the organizing committees of numerous conferences.

CÉDRIC ADJIH is a researcher at Inria since 2001. He earned his Ph.D. in 1998. His research is centered on IoT networks, encompassing both embedded systems (constrained devices) and telecommunications aspects (wireless communications and network protocols). Some of his recent interests include modern random access protocols, particularly the IRSA family and their applications in 5G and 6G networks. Additionally, his work involves machine learning techniques for IoT devices, focusing on TinyML and EdgeAI to optimize AI processing at the edge. He is also exploring AI for network problems, specifically Neural Combinatorial Optimization, and Semantic Communications.

GUILLAUME ANDRIEUX graduated in electrical engineering from Polytech Nantes and received the M.S. degree in Telecommunications from the University of Nantes in 2000. He completed a Ph.D. in electrical engineering and the Habilitation à Diriger des Recherches degree from the University of Nantes, in 2004 and 2018, respectively. He is currently a Full Professor at the Networks and Telecommunications Department, Nantes University. His current research interests are digital communications, antenna processing, wireless sensor networks and energy efficiency in wireless networks.

WALID BECHKIT is an associate professor within INSA-Lyon, France. He is also a member of the Inria Agora team of the CITI laboratory. He obtained his Ph.D. in System and Information Technology from the Compiegne University of Technology (U.T.C.), France in 2012 and his engineering degree in Computer Science from the "Ecole nationale Supérieure d'Informatique," Algiers in 2009. His main research interests include Wireless Sensor Networks, IoT applications, data analysis, machine and deep learning, LPWAN technologies, and security.

SIWAR BEN HADJ SAID is a Research Engineer at the French institute CEA-List. She received her Ph.D. in Informatics from Telecom Bretagne in 2014, and subsequently worked as a Temporary Assistant Professor (ATER) for one year. Her research interests lie at the intersection of several cutting-edge networking technologies. She is particularly passionate about Time Sensitive Networking (TSN), Software-Defined Networking (SDN), Digital Twins for 6G networks and the use of AI techniques for network optimization.

OLIVIER BOISSIER is full professor of computer science at Mines Saint-Etienne (France). He obtained his HDR in computer science, Université Jean Monnet, Saint-Étienne, France (2003) and his Ph.D. in computer science from INP Grenoble, France (1993). He is a member of LIMOS UMR CNRS 8156. He is active in the research and development of multi-agent systems. His main research contributions contribute to the definition of multi-agent models, tools and methodologies for the multi-agent oriented engineering of software applications on the Web or IoT, in the smart industry and smart city domains.
**AYMEN BOUFERROUM** received an M.S. degree in Communicating Computer Systems from the University of Avignon, France, and an M.S. degree in Networks and Distributed Systems from the University of Con- stantine, Algeria. He is currently a Ph.D. student at Inria Lille, France, under the supervision of Valeria Loscri and Abderrahim Benslimane. His research focuses on developing resilient IIoT frameworks using AI-driven approaches to enhance trust management, cybersecurity, interoperability, and wireless communications.

**RICHARD COMBES** is currently an assistant professor in CentraleSup- elec. He received the Engineering Degree from Telecom Paristech (2008), the Master's Degree in Mathematics from University of Paris VII (2009) and the Ph.D. degree in Mathematics from university of Paris VI (2013). He was a visiting scientist at INRIA (2012) and a post-doc in KTH (2013). He received the best paper award at SIGMETRICS 2019 and CNSM 2011. His current research interests are machine learning, networks and probability.

**FABIEN COURREGES** received the B.S degree in fundamental physics of the University of Orleans (France) in 1999, the M.S degree in robotics of the University of Poitiers (France) in 2000 and the Ph.D. degree in signal processing, robotics and control of the University of Orléans in 2003. He is tenured assistant professor with the Intelligent Systems and Networks department of the XLIM research institute from the University of Limoges (France) since 2007. His main research concern is about systems and data modeling and processing for enabling design and control of smart and autonomous plants and systems.

**HIBA DAKDOUK** got her telecommunication engineering degree from the Lebanese university and the Master's degree in Architecture and Engineering for IoT from IMT Atlantique, in Brest, France in 2018. She Completed her Ph.D. in "optimizing IoT networks using Reinforcement Learning" in 2022, at Orange Labs and IMT Atlantique in France. She has been working as a Research Engineer at CEA-Leti in Grenoble, France since 2022. Her research interests include communication networks and machine learning.

**AMIRA DHAOUADI** received her degree in Engineering from the École Polytechnique de Tunisie (EPT). Currently, she is pursuing a Ph.D. in Dis- tributed Machine Learning at Inria, France. Her research is centered around Artificial Intelligence, exploring innovative techniques for distributing ma- chine learning models to enhance their efficiency and performance within the Internet of Things (IoT) ecosystem.

**JEAN-FRANÇOIS DIOURIS** received the Ph.D. degree from University of Rennes I, France, in 1991. He is currently a Full Professor with Polytech Nantes, University of Nantes, France. His current research interests are digital communications, antenna processing, and energy efficient commu- nications.

**SALAH EDDINE ELAYOUBI** (Senior Member, IEEE) received the M.S. degree in telecommunications from the National Polytechnic Institute of Toulouse, Toulouse, France, in 2001, and the Ph.D. and Habilitation degrees in computer science from the University of Paris VI, Paris, France, in 2004 and 2009, respectively. From 2004 to 2013, he was with Orange Labs, France. Since January 2018, he has been a Full Professor with CentraleSupélec, France. He holds the Sustainable 6G chair funded by Orange. His research interests include modeling, performance evaluation, and optimization of mobile networks.

**JÉRÔME HÄRRI** received a M.Sc. degree and a Dr. ès sc. (Ph.D.) degree in telecommunication from the Swiss Institute of Technology (EPFL), Lausanne. He is currently Professor at the Communication Systems Department, EURECOM, Sophia Antipolis, France, where he leads the Connected Auto- tomated Transport System (CATS) team. His research is related to Internet of Things (IoT) and Smart Network Systems for Connected Cooperative Au- tomated Mobility (CCAM). He is a Board Member of the OpenAirInterface Software Alliance (OSA).

**MIHIA KASSI** received her master's degree in Networking and Telecom- munications from the African Higher School of Information and Commu- nication Technologies (ESATIC), Abidjan, Côte d'Ivoire, in 2020, and her Ph.D. from the Higher School of Communications of Tunis (SUP'COM), Ariana, Tunisia, in 2024. She is currently a postdoctoral researcher at Mines Saint-Etienne in LIMOS UMR CNRS 8156, France. Her research interests focus on network virtualization in 5G networks and the study of network implications of Industry 4.0 technologies.

**XAVIER LAGRANGE** received the Ph.D. degree from TELECOM Paris in 1998. Since 2002, he has been professor in IMT Atlantique. He leads a research group at IRISA (Institut de Recherche en Informatique et Systemes Aléatoires). His domain of interest includes resource allocation, medium access control and performance analysis for 5G and 6G cellular networks.

**YANDI LIU** received his M.S. degree in Computer Science from KU Leu- ven, Belgium, in 2022, with a thesis titled "LoRa-Based Walkie-Talkie." He is currently a Ph.D. student at the XLIM Research Institute at the University of Limoges, France, under the supervision of Romain Négrier and Raymond Quéré. His research focuses on optimizing energy efficiency in wireless sensor networks.

**VALERIA LOSCRI** (M'03, SM'17) is research director at Inria Lille (France). She joined Inria since Oct. 2013. From Dec. 2006 to Sept. 2013, she was Research Fellow in the TITAN Lab of the University of Calabria, Italy. She received her MSc and Ph.D. degrees in Computer Science in 2003 and 2007, respectively, from the University of Calabria and her HDR (Habilita- tion à diriger des recherches) in 2018 from Université de Lille (France). Her research interests focus on emerging technologies for new communication paradigms such as Visible Light Communication (VLC), mmWave, cyber security in wireless networks and cooperation and coexistence of wireless heterogeneous devices. She is involved in the activity of several European Projects (Horizon Europe MLSysOps, H2020 CyberSANE, FP7 EU project VITAL, etc.) and National Projects such as the project ASTRID DEPOSIA, ANR NEMIoT.

In 2021 she has been nominated Women Stars in Computer Networking and Communications by the IEEE Communication Society. She has been shortlisted as Cyber Researcher by European Women Cyber Day - ECWD in 2024. She is Action Chair and Scientific Holder of BEING-WISE COST Action (since 2023). She has been European Partnership Scientific Delegate for Inria Lille since (Oct. 2016 – Sept. 2019). She has been appointed as member of the Technological Transfer Committee at Inria Lille (Oct. 2016 – Sept. 2018).

Dr. Loscri is currently chair of the expert panel for fundamental research W&T5 Computer Science & Information Technology FWO. Since 2019, she is Scientific International Delegate for Inria Lille.

**VALÉRIAN MANNONI** received the Ph.D. degree in electrical and com- puter engineering from the University of Reims Champagne-Ardenne, France, in 2004. After his Ph.D. thesis on the channel coding for the mul- ticarrier communications, he followed with a post-doctoral position on the joint source-channel coding at France Telecom Research and Development, France. In 2006, he joined Dibcom, as a Telecommunication Engineer, and developed demodulators for terrestrial/satellite for mobile digital TV. He has also contributed to the standardization of DVB-T2. He then pursued his career at the French Ministry of Defence in order to investigate the non-cooperative wireless communication systems. Since 2015, he has been working at CEA-LETI, Wireless Communications Department, as a Research Engineer, where he contributes and leads projects on wireless communi- cations (physical and MAC layers) and in particular for the Internet of Things and for the V2X connectivity. He has authored over 40 articles in international journals and conference proceedings and holds three patents.
**SÉBASTIEN MAUDET** obtained his Agrégation in Electrical Engineering from the École Normale Supérieure de Rennes in 2005. In 2015, he joined the Networks and Telecommunications department at the University of Nantes, where he prepared and defended a Ph.D. in electrical engineering in 2024. His research focuses on the metrics and energy consumption of IoT sensor networks, in particular for 802.11 and LoRa/LoRaWAN technologies.

**NATHALIE MITTON** received the M.S. and Ph.D. degrees in Computer Science from INSA Lyon in 2003 and 2006 respectively. She received her Habilitation à diriger des recherches (HDR) in 2011 from Université Lille 1. She is currently an Inria full researcher since 2006 and from 2012, she is the scientific head of the Inria FUN team which designs protocols for wireless communications in constrained networks. Her research interests focus on self-organization from PHY to routing for wireless constrained dynamic and mobile networks. She has been nominated as one of the 10 women stars in computer Science in 2020 by the IEEE Communication Society. She has published her research in more than 40 international revues and more than 120 international conferences. She coordinates the Horizon Europe SLICES-PP project, participates in different Horizon Europe projects (CyberSANE, NEPHELE) and in several program and organization committees.

**AMINA MOKDAD** is a Ph.D. student at IMT Atlantique, specializing in interoperability and header compression for Massive IoT. Her research focuses on integrating Static Context Header Compression (SCHC) into the 5G framework to improve efficiency and enable seamless communication for constrained IoT devices. She holds an engineering degree and a Master's degree in Computer Science from the Higher National School of Computer Science (ESI ex ENI) in Algeria, both earned with distinction. She previously completed a research internship at IRISA, Rennes, where she developed strategies for service placement in the cloud-edge continuum to accommodate user mobility. Amina has also contributed to projects in networking and distributed systems, such as deploying Kubernetes clusters and designing VPN/MPLS platforms. Ms. Mokdad is a Student Member of IEEE.

**EMMANUEL MOULAY** received his M.S. degree in mathematics from the University of Lille, France, in 2002. He obtained his Ph.D. degree in automatic control in 2005 from the Ecole Centrale de Lille and his habilitation from the University of Poitiers in 2014. He joined the CNRS as a research scientist at the Ecole Centrale de Nantes in 2006 and moved at the XLIM institute of the University of Poitiers in 2009. He is also adjunct professor in the department of Electrical and Computer Engineering at the University of Nebraska-Lincoln, USA, since 2020. His main interests are in control theory and its practical applications.

**ALI NADAR** is a research engineer at EURECOM since 2022. He received his M.S. degree in Software engineering from Antonine University in Beirut and his Post-Master degree in Communications for Intelligent Transport Systems from EURECOM, Sophia Antipolis, France. His research activities is related to AI of Things (AIoT), Named Data Networking (NDN) and Autonomous Driving (AD) in vehicular domains.

**RÉMI NAHON** is a Ph.D. student at LTCI, Télécom Paris, Institut Polytechnique de Paris since 2022. His research focuses on efficient deep learning, with particular emphasis on debiasing neural networks and privacy-preserving machine learning for EEG data. He has published papers at major computer vision conferences including ICCV and ECCV, contributing novel methods for bias mitigation in deep neural networks. His current work explores the intersection of resource-efficient AI and cognitive science, developing techniques for on-device learning while ensuring data privacy. He is involved in the NF-FITNESS project, investigating energy and memory efficient AI solutions for privacy-reserving ML applications.

**ROMAIN NÉGRIER** received a Ph.D. in telecommunications engineering from the University of Limoges, France, in 2016. He is currently Associate Professor with the XLIM Research Institute, University of Limoges, France. His research interests include development of energy efficient wireless sensor networks.

**VAN-TAM NGUYEN** received the Diplôme d'Ingenieur from CentraleSupelec, the M.Sc. degree from University Paris-Saclay, the graduation degree in image processing from EPFL, Switzerland, in 2000, the Ph.D. degree from Telecom Paris in 2004, and the H.D.R. degree from University Sorbonne in 2016. He has been at Telecom Paris since 2005, where he is currently Full Professor, Head of the computer science and networks department (INFRES) and the communications and electronic department (COMELEC) and a member of the Board of Directors. He is also Director of the ICMS (Intelligent Cybersecurity for Mobility Systems) Chair and Deputy Director of the LTCI laboratory. At IP Paris, He is currently Head of Computer Science, Data and AI Department and member of Teaching and Research Committee. He was a NICT's Rank A Guest Researcher, Japan (2012-2013). He has held visiting positions at UC Berkeley (2013-2016) and Stanford (2016-2017). He was director of Intek Institute (2018) and CEO of TAM-AI Co. Ltd (2019-2021). His research interests includes Continual Learning, Adaptive AI, AI for Cybersecurity and AI for Cognitive Science and Neuroscience. He is the author or co-author of over 100 publications, five patents and one technology transfer. He received a Senior Marie Curie Fellowship from the European Commission in 2015.

**ALEXANDER PELOV** is an Associate Professor at IMT Atlantique, France, with over a decade of experience in next-generation wireless technologies for the Internet of Things. He leads research on low-power wide-area networks and their IP integration, and as co-inventor of the SCHC compression/fragmentation framework and CORECONF device management stack, he plays a key role in their IETF standardization. Previously, he co-founded Acklio, a startup offering reference SCHC solutions. Alexander holds a PhD in Computer Science from the University of Strasbourg, France.

**CLENCY PERRINE** received his M.S. degree in telecommunications in 2002 and his Ph.D. in telecommunications and signal processing in 2005 from the University of Rennes 1. He obtained his habilitation from the University of Poitiers in 2024. In 2006, he joined the XLIM laboratory in Poitiers as an associate professor. His research interests include digital communication systems and resource allocation strategies for image transmission over non-stationary wireless channels.

**MARC PIERRE** was born in 1999, in France. He received the M.S degree in applied mathematics from Université Paris Dauphine and the M.S degree in machine learning from ENS Paris-Saclay. He is currently a Ph.D. student studying reinforcement learning and queuing systems under the supervision of Salah Eddine Elayoubi and Richard Combes.

**SÉBASTIEN PILLEMENT** (M'00) is full Professor in Polytech'Nantes, France since 2012. He was an associate professor at the University of Rennes 1, during 13 years. He is currently a research member of the ASIC Research Team of the IETR Lab. (Research Institute in Electronic and Telecommunication). He received a Ph.D. degree and Habilitation degrees in Computer Science from the University of Montpellier II and the University of Rennes 1 respectively. His research interests include dynamically reconfigurable architectures, smart system on chips, design methodology and NoC (Network on Chip) based circuits. He focuses his research on designing intelligent, flexible, and efficient architectures managed in real-time and on the use of these architectures to increase the reliability of systems, focusing on embedded AI.
**ANTONY POTTIER** received the M.S. degree in electrical engineering from the École Nationale Supérieure des Sciences Appliquées et de Technologie, Lannion, France, in 2013 and the Ph.D. degree from IMT Atlantique (formerly known as Telecom Bretagne), Brest, France, in 2018. From 2014 to 2015, he was a Research Engineer at Telecom Bretagne, Brest, France. He joined the Institut Supérieur de l'Electronique et du Numérique (ISEN), Brest, France as an Assistant Professor from 2018 to 2022 where he worked on underwater acoustic communications. He is now a research engineer at CEA Leti, Grenoble, France, where his work focuses on wireless communications. His research interests are in the fields of signal processing and wireless communications.

**CHARLY POULLIAT** received the M.Sc.Eng. degree in electrical engineering from the Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), Cergy-Pontoise, France, in 2001, and the M.S. degree in image and signal processing, the Ph.D. degree in electrical and computer engineering, and the Habilitation degree from the University of Cergy-Pontoise, France, in 2001, 2004, and 2010, respectively. From 2004 to 2005, he was a Post-Doctoral Researcher with the UH Coding Group, University of Hawaii, Manoa, HI, USA, supervised by Prof. M. Fossorier. In 2005, he joined the Signal and Telecommunications Department, Engineering School ENSEA, as an Assistant Professor. Since 2011, he has been a Professor with the National Polytechnic Institute of Toulouse (Toulouse INP-ENSEEIHT), University of Toulouse. He is also with the Signal and Communications Group, CNRS IRIT Laboratory. His research interests include signal processing for digital communications, waveform design, channel coding, and iterative system design and optimization.

**YANNIS POUSSET** was born in 1971. He received the Ph.D. degree in mobile radio communication from the University of Poitiers, in 1998. Since 2012, he is professor at the University of Poitiers in the department of electrical engineering. He develops its research activities in the XLIM CNRS laboratory. His research interests include the study of adaptive links related to the optimal transmission of data over wireless spatio-temporal radio channel.

**MINA RADY** is a Research Engineer at the French institute CEA in the area of Robotic Wireless Networks for industrial and military applications. Previously, he was post-doctoral researcher at INSA Lyon in wireless communications for robotics in naval inspections. He received his Ph.D. in Computer Science from Sorbonne University, Paris, France in 2022. He held a simultaneous research and development engineer position at Orange Labs, France (former France Telecom). His experience includes: development of experimental evaluation of Wi-Fi for robotic systems, development of the IETF standard 6TiSCH protocol stack for Industrial Internet of Things, distributed routing in RPL, and optimization solutions for low power wide area networks resource allocation.

**NOURHEN SBOUI** received her degree in Information and Communication Technologies (ICT) Engineering from the École Supérieure des Communications de Tunis (SUP'COM), University of Carthage, in 2024. She is currently pursuing a Ph.D. in Automation, Signal Processing, and Robotics at the University of Poitiers, France, with the XLIM laboratory. Her research focuses on artificial intelligence applications, particularly in Industry 4.0, autonomous vehicles, LEO satellites, and 5G communication.

**PATRICK SONDI** received the Ph.D. degree in computer science from the University of Valenciennes, in 2010. He is a Professor at IMT Nord Europe. His areas of interest include communication network protocol design, performance evaluation and security.

**LAURENT TOUTAIN** is a Professor at IMT Atlantique in France and a contributor to the development of Static Context Header Compression (SCHC). Prof. Toutain has worked on the design and standardization of SCHC through the Internet Engineering Task Force (IETF), where this technology enables IPv6 and upper-layer protocols over constrained networks. His research on SCHC addresses challenges of transmitting IP packets over Low Power Wide Area Networks (LPWAN) with bandwidth limitations. Prof. Toutain has authored RFCs related to SCHC framework and fragmentation mechanisms, and continues to work on this field through international collaborations and research projects focused on IoT protocol optimization and networking for constrained devices.