# DtS-IoT Resource Allocation Analysis Framework:
Assessing DQ and RESS-IoT

Jason Gerard, Juan A Fraire, Raydel Ortigueira, Sandra Céspedes

► To cite this version:

Jason Gerard, Juan A Fraire, Raydel Ortigueira, Sandra Céspedes. DtS-IoT Resource Allocation Analysis Framework: Assessing DQ and RESS-IoT. 2024 IEEE 10th World Forum on Internet of Things (WF-IoT), Nov 2024, Ottawa, France. pp.672-677, 10.1109/WF-IoT62078.2024.10811213. hal-05357398

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# DtS-IoT Resource Allocation Analysis Framework: Assessing DQ and RESS-IoT

Jason Gerard*, Juan A. Fraire†, Raydel Ortigueira§, Sandra Céspedes*

*Department of Computer Science & Software Engineering, Concordia University, Montreal, Canada.

†Inria, INSA Lyon, CITI, UR3720, 69621 Villeurbanne, France.

‡CONICET - Universidad Nacional de Córdoba, Córdoba, Argentina.

§Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile.

**Abstract**—The demand for expanding connectivity to remote and isolated areas has intensified the need for global Internet of Things (IoT) solutions. Direct-to-Satellite IoT (DtS-IoT), enabled by advances in nanosatellite technology, allows cost-effective global and direct connectivity between IoT devices and low Earth orbit satellites. However, the most cost-effective DtS-IoT networks face resource limitations in user, ground, and space segments, demanding scalable solutions to fully realize their scalability potential. While several resource allocation protocols have been proposed for DtS-IoT, they have mainly been evaluated using static topologies or simplified simulations, overlooking the time-evolving orbital and channel dynamics crucial to DtS-IoT performance. This work bridges this gap by introducing the DtS-IoT Resource Allocation Analysis Framework (DRAAF), a comparison methodology and toolchain for evaluating resource allocation mechanisms in realistic DtS-IoT scenarios. Our method is framed in scalability, energy efficiency, throughput, delay, fairness, and overhead metrics. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the methodology with a use case investigating two energy-efficient MAC protocols for DtS-IoT: RESS-IoT and Distributed Queuing (DQ). Results indicate that DQ excels in throughput and node energy efficiency, whereas RESS-IoT reduces delay. Both protocols maintain satellite energy efficiency and enhance fairness in uplink transmissions. These findings underscore the value of our approach in evaluating resource allocation within complex DtS-IoT systems.

## I. INTRODUCTION

The ever-expanding Internet of Things (IoT) demands highly scalable and energy-efficient communication systems to support its diverse applications across various sectors, from agriculture to industrial automation [1], [2]. This growth is projected to reach 80 billion devices by 2025, with an economic impact of up to $1 trillion annually [3], [4].

The Direct-to-Satellite IoT (DtS-IoT) model has emerged as an access network solution to enable connectivity in remote or uncovered areas [5]. The technology allows IoT devices to communicate directly to a satellite overhead, eliminating the need for terrestrial infrastructure. With more satellite constellations being deployed [6], along with plans of integrating IoT devices with the proposed 3GPP Earth orbit satellite network [7], DtS-IoT will become increasingly prevalent.

Low Earth orbit (LEO) constellations serving IoT devices often consist of small, resource-limited spacecraft, such as CubeSat-like nano- and picosatellites, which have restricted power generation and storage capacities. In a 90-minute orbit,

there are eclipses lasting up to 35 minutes, where the CubeSat relies on battery power to maintain operation until sunlight allows solar panel recharge [8]. On top of that, the user segment in DtS-IoT mainly comprises constrained IoT devices whose batteries cannot be easily replaced or recharged [9]. These constraints fuel central DtS-IoT research challenges, such as maintaining high energy efficiency and scale medium access control (MAC) protocols to accommodate thousands of devices. Existing evaluations created to help answer this question often only operate on small deployments of a couple of hundred nodes. They lack energy models to evaluate the devices’ energy consumption and efficiency and make unrealistic protocol assumptions that result in inaccurate data. These assumptions include device registration, topology knowledge, and synchronization between nodes and the satellite that doesn’t require the use of the channel to coordinate.

### A. Related Work

Despite the potential performance of DtS-IoT resource allocation mechanisms in modern access protocols, direct comparison based on existing literature is not feasible due to the lack of a standardized evaluation platform. This issue extends to other access schemes, including popular ALOHA-based MAC protocols. Evaluation frameworks for DtS-IoT access networks must account for the system’s physical constraints using advanced tooling that takes into account orbital mechanics, low-level details about the physical layer modulation schemes, and channel conditions like visibility periods and uneven propagation delays. This tooling can then be paired with a comprehensive suite of performance metrics that accurately describe the test scenario results. To our knowledge, there is no comprehensive evaluation framework for a realistic comparative analysis of resource allocation in DtS-IoT. Existing work includes a simulation-based LEO mega-constellation evaluation framework that focuses on system-level KPIs but cannot be used to evaluate low-level resource allocation with LPWAN technologies at the physical layer [10]. In [11], the authors present a constellation-based uplink performance evaluation framework for ALOHA and scheduled access protocols. While detailed concerning satellite constellation configurations, the framework lacks the realistic benchmark scenarios that define many DtS-IoT deployments, such as thousands of nodes under the
satellite's footprint, a standardized GUI-enabled simulator for rapid development and integration of new protocols, a clean interface for energy model definitions, and a repeatable evaluation pipeline for experimenting and comparison.

## B. Contributions

This work proposes the DtS-IoT Resource Allocation Analysis Framework (DRAAF). DRAAF integrates dynamic modeling of satellite orbits, IoT device behavior, and radio resource management to accurately reflect the operational realities of DtS-IoT systems. This integration allows for more realistic and relevant assessments and comparison of resource allocation strategies. To evaluate DRAAF, we study two state-of-the-art MAC protocols applicable to DtS-IoT scenarios: REserve-and-Send Sift-IoT (RESS-IoT) [9] and Distributed Queuing (DQ) [12]. The DRAAF pipeline and these first protocols are fully implemented, using the FLoRaSat simulator to account for realistic conditions, including orbital dynamics and LoRa modulation [13]. The DRAAF pipeline, FLoRaSat, and the protocol implementations are all fully open-source, allowing other practitioners in the field to build off this work. This comparison enabled the optimization of DQ and RESS-IoT parameters, leading to enhanced models for both protocols.

The remainder of the paper is organized as follows. In section II, we describe the details of DRAAF. In section III, we discuss the motivation for the selected protocols and the steps required to onboard existing protocols so they can be used in DRAAF. Next, in section IV, we present the comparative evaluation and discuss the relevant performance metrics. Finally, in section V, we provide the conclusions and future work.

## II. DRAAF FRAMEWORK

Our DtS-IoT Resource Allocation Analysis Framework (DRAAF) is composed of three parts: i) a set of performance metrics, which make up the evaluation scheme; ii) a set of benchmark scenarios, which are the control variables that define the use cases; and iii) a toolchain, which includes the FLoRaSat DtS-IoT simulator, protocol implementations, energy model definitions, and the DRAAF pipeline. The pipeline has extract, transform, and load (ETL) and data analysis components that can execute data ingestion, metric evaluation, and graph generation jobs for protocol comparison and sensitivity analysis experiments. Each part is explained as follows.

### A. Performance Metrics

* **Scalability (N).** Defined as the maximum number of nodes inside the satellite footprint the system can support before the throughput reaches 0 kb/hour. This acts as a lower bound on the throughput.

* **Energy Efficiency (ξ).** Defined as $\frac{K}{E}$, the ratio of the number of data bits successfully received by the satellite, K, to the total energy spent to achieve K, E. K does not consider control information, such as bits from RTS, VCTS,

beacon, ARS, and FBP frames. ξ is measured at three levels: network, satellite, and node. On the network level, K counts all data bits successfully received by the satellite, and E is the total energy consumed by all nodes and the satellite. On the satellite level, $K_{sat}$ are data bits successfully received by the satellite, and $E_{sat}$ is the total energy consumed by the satellite. On the node level, $K_n$ are data bits transmitted by node n and successfully received by the satellite, and $E_n$ is the total energy consumed by node n.

* **Throughput (S).** Defined as $\frac{K}{T}$, where T is the period that the satellite receives the K bits of data. Thus, DRAAF measures the aggregated throughput.

* **Uplink Delay ($U_p$).** Measured as the time difference between the moment a data frame is first generated and the time it is received by the satellite. The metric indicates how delay-tolerant the IoT application must be to account for delays caused by the access layer.

* **Jain's Fairness Index.** Measured as the capability of a protocol to allocate resources to each node in the footprint fairly. This study's resource corresponds to collision-free channel access for uplink transmissions.

* **Overhead.** Measured as the control-related bytes exchanged by nodes and the satellite.

### B. Benchmark Scenario

In this DRAAF scenario, a single LEO satellite, moving at 8 km/s with an average orbital altitude of 600 km [14], passes over a region with a radius of 15 km. The satellite follows a polar orbit from south to north, maintaining line-of-sight with all nodes for the 540-second simulation. The user segment has $N \in \{10, ..., 5000\}$ IoT sensor nodes randomly placed within the region; they produce telemetry data every $t = [30, 90]$ seconds, with an initial offset $t_0 = [0, 60]$ seconds.

The system is modeled after a delay-tolerant telemetry system, like one that can be used in a Ring Road Network [15]. Nodes and satellites follow a store-carry-forward scheme, meaning they store data in onboard storage until they can transmit it to a satellite or ground station due to orbital movements [16]. Sensor nodes and satellites switch radios between transmitting, receiving, and sleeping states to minimize power consumption. This scenario uses LoRa technology, as it has been increasingly popular for IoT use cases and is one of the main areas of study for the DtS-IoT physical layer [14], [17]. In DtS-IoT environments, setting the LoRa orthogonal Spreading Factor (SF) to 10 achieves the optimal balance between successful data transmission and energy usage [9].

The slot duration is computed as ($d_{trans} + d_{prop} + d_{guard}$). The transmission delay, ($d_{trans}$), is the time on air (ToA) of a LoRa frame, and the propagation delay, $d_{prop}$, is calculated with the average altitude of the satellite, and the guard time, ($d_{guard}$), accounts for the variability of the actual distances between nodes and the satellite. Since the ToA of a LoRa frame is dependent on the size, the fields follow the exact fix-size structure: payload (63 B), frame type field (1 B), and a unique ID field (2 B). The satellite and nodes in this scenario
use the Semtech LR1110 transceiver, which consumes 25.74 mW of power for reception and 389.4 mW for transmission. The transmission power is set to 14 dBm, and the antenna gain is 12 dB with a beam width of 120°.

### C. Toolchain

DRAAF uses the FLoRaSat network simulator [13], based on the OMNeT++ framework, to perform the simulations. The choice of FLoRaSat is backed by existing literature showcasing its support for LoRa modulation, orbital mechanics, and large-scale network generation [18]. Before using the DRAAF toolchain, the protocol must be onboarded to the framework with the following three steps. First, implement the protocol in the simulator following the defined interfaces. Second, the energy model derived from the radio module operation is defined, and the energy consumption is calculated based on the radio's duration in different states (transmit, receive, sleep, idle). Third, define the parameters and the set of values each parameter can assume. FLoRaSat currently implements the LoRa Chirp Spread Spectrum modulation, developed by Semtech, for communication between IoT nodes and the nanosatellite following a star network topology [19]. However, the simulator's modularity allows for future extensions to support other LPWAN technologies, such as NB-IoT.

The DRAAF pipeline component of the toolchain enables rapid iteration and testing for protocol comparison and massive grid search sensitivity analysis. The pipeline initiates the simulation runs for a protocol comparison or sensitivity analysis input payload. The simulator pulls the benchmark scenario configuration and protocol implementations to set up and execute the environment. The output is transformed and filtered, then passed to the data warehouse. An isolated workspace can then be created to pull data sources from the data warehouse and execute the ETL pipeline. It utilizes the protocol energy model definitions and the metric engine, defined by the performance metrics, to transform the raw data and load it as structured performance metric data. Finally, the data analysis portion of the pipeline uses the graph manager and the structured data to build graphs to evaluate the experiment's results. Source code of FLoRaSat with the protocol implementations<fnref n="1" /> and the DRAAF pipeline<fnref n="2" /> are publicly available.

## III. CASE STUDY PREPARATION

In this section, we will first present the motivation for the selection of protocols used in the comparative evaluation. Then we will walk through the DRAAF onboarding process for both of them as defined in section II-C.

### A. Protocol Selection

Comprehensive surveys have found reservation-based MAC protocols more complex than random-access protocols in satellite IoT. They must synchronize before transmission involving coordination over the channel between nodes and

the satellite [20]. However, new studies have shown the reservation category can scale in terms of throughput past 1,000 nodes and maintain 40 kb/hour, where random-access protocols such as Unconfirmed Class A LoRaWAN and Enhanced Aloha MAC protocols fall below 10 kb/hour [9]. Therefore, we have selected two reservation-based MAC protocols to compare head-to-head under the same DtS-IoT scenario for this comparative study: RESS-IoT and DQ. The specific selection is motivated by RESS-IoT being one of the top-performing DtS-IoT purpose-built MAC protocols, which makes it an excellent baseline candidate to demonstrate the framework. DQ was selected because it has shown promise in large IoT networks using LTE [12] and LoRa [21], with simulation and analytical results, but has not yet been evaluated under the requirements and constraints of a DtS-IoT deployment.

The first protocol selected, RESS-IoT [9], is inspired by Sift [22] and uses reservations based on rounds to orchestrate data transmissions. Each RESS-IoT round is divided into two phases: a reservation phase and a transmission phase. In the reservation phase, the satellite broadcasts a beacon frame to the sensor nodes. Each sensor node that has data transmits a Request-to-Send (RTS) frame to the satellite, using a reverse-exponential distribution to pick a slot. In the transmission phase, the satellite broadcasts a Variable-length-Clear-to-Send (VCTS) frame indicating the order of channel access to the nodes. Once received, the nodes send their data frames to the satellite.

The second protocol selected, Distributed Queuing [12], was initially proposed for cable TV distribution [23] but has been adapted for many modern use cases. The DQ Protocol uses reservations based on rounds and a contention tree algorithm (CTA) to orchestrate data transmission. Each DQ round is split into three phases. First, a contention resolution phase with *m* timeslots, selected using a uniform distribution, where each node with data to transmit sends an Access-Request-Sequence (ARS) frame to the satellite. Second, a transmission phase with one collision-free data slot, where the node at the head of the data transmission queue sends its data. Third, the feedback phase, where the satellite sends a Feedback-Packet (FBP) frame containing *2*·*m* bits that indicate the results of the *m* contention slots. Each node uses these results as input to the CTA to compute their new data transmission queue (DTQ) and contention resolution queue (CRQ) positions and lengths.

### B. Energy Models

The energy consumption model of RESS-IoT is defined in [9]. In our analysis, we modified the calculation of the energy consumption of the *n*-th node $E_n^{RP_r}$ (see Eq. (9) in [9]) to reflect that not all nodes send an RTS frame in each round. The new equation considers the reception power ($P_{rx}$) consumed during the reception of a beacon ($T_{beacon}$) plus the transmission power ($P_{tx}$) consumed during the transmission of an RTS ($T_{RTS}$). $E_n^{RP_r}$ is re-defined as:

<footnote n="1">¹https://gitlab.inria.fr/jfraire/florasat</footnote>

<footnote n="2">²https://github.com/jason-gerard/DRAAF-pipeline</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1: Performance comparison for up to 5,000 nodes.</figcaption></figure>

$$E_{n}^{RP_{r}} = P_{rx} \cdot T_{beacon} + B_{n,r}^{RTS} \cdot P_{tx} \cdot T_{RTS}, \quad (1)$$

where $B_{n,r}^{RTS}$ is an indicator variable that takes a binary value (0,1) to indicate if the n-th node on the r-th round transmitted an RTS frame. Same as in [9], the total energy consumption of the network ($E_{sat}^{RESS}$) is defined as the sum of the total energy consumed by the satellite ($E_{sat}^{RESS}$) and the total energy consumed by N nodes $E_{N}^{RESS}$.

$E_{sat}^{RESS}$ accounts for the power consumption during R rounds consisting of a reservation (RP) and a transmission (TP) phase. $E_{sat}^{RESS}$ is defined as:

$$E_{sat}^{RESS} = R \cdot E_{sat}^{RP} + \sum_{r=1}^{R} E_{sat}^{TP_{r}}. \quad (2)$$

Then, the total energy consumed by the n-th node $E_{n}^{RESS}$ and by N nodes $E_{N}^{RESS}$ are respectively defined as:

$$E_{n}^{RESS} = \sum_{r=1}^{R} (E_{n}^{RP_{r}} + E_{n}^{TP_{r}}) \quad (3)$$

$$E_{N}^{RESS} = \sum_{n=1}^{N} E_{n}^{RESS}. \quad (4)$$

Following a similar methodology, we derived a new energy consumption model for DQ. The energy consumption of the satellite and the n-th node during the contention phase are defined in (5) and (6), respectively, as:

$$E_{sat}^{CP} = P_{rx} \cdot T_{ARS} \cdot m, \quad (5)$$

$$E_{n}^{CP_{r}} = B_{n,r}^{ARS} \cdot P_{tx} \cdot T_{ARS}, \quad (6)$$

where $P_{rx}$ is the power consumed during reception and $T_{ARS}$ is the ToA of the ARS frame. The contention window duration is given by $T_{ARS} \cdot m$. $P_{tx}$ is the power consumed during transmission, and $B_{n,r}^{ARS}$ is an indicator variable that takes a binary value (0,1) to indicate if the n-th node on the r-th round transmitted an ARS frame.

The energy consumption of the satellite and the n-th node during the transmission phase are defined in (7) and (8), respectively, as follows:

$$E_{sat}^{TP_{r}} = B_{n,r}^{data} \cdot P_{rx} \cdot T_{data}, \quad (7)$$

$$E_{n}^{TP_{r}} = B_{n,r}^{data} \cdot P_{tx} \cdot T_{data}, \quad (8)$$

where $T_{data}$ is the ToA of the data frame and $B_{n,r}^{data}$ is an indicator variable that takes a binary value (0,1) to indicate if the n-th node on the r-th round transmitted a data frame.

The energy consumption during the feedback phase for the satellite and the n-th node are defined in (9) and (10), respectively, as:

$$E_{sat}^{FP} = P_{tx} \cdot T_{FBP}, \quad (9)$$

$$E_{n}^{FP} = P_{rx} \cdot T_{FBP}, \quad (10)$$

where $T_{FBP}$ is the ToA of the FBP frame.

The total energy consumption of a network using the DQ protocol is defined as:

$$E^{DQ} = E_{sat}^{DQ} + E_{N}^{DQ}, \quad (11)$$

where $E_{sat}^{DQ}$ is the total energy consumed by the satellite and $E_{N}^{DQ}$ is the total energy consumed by N nodes. $E_{sat}^{DQ}$ is defined as:

$$E_{sat}^{DQ} = R \cdot E_{sat}^{CP} + \sum_{r=1}^{R} E_{sat}^{TP_{r}} + R \cdot E_{sat}^{FP}, \quad (12)$$

The total energy consumed by the n-th node $E_{n}^{DQ}$ and the total energy consumed by N nodes $E_{N}^{DQ}$ are defined as:

$$E_{n}^{DQ} = \sum_{r=1}^{R} (E_{n}^{CP_{r}} + E_{n}^{TP_{r}}) + R \cdot E_{n}^{FP}, \quad (13)$$

$$E_{N}^{DQ} = \sum_{n=1}^{N} E_{n}^{DQ}. \quad (14)$$

### C. Sensitivity Analysis

The parameters studied in RESS-IoT are $N_{slot} \in \{100, 200\}$, which indicates the number of slots a node can pick from when reserving, $T_{slot} \in \{40, 50, 60, 70, 80\}$, which is the duration of each of the slots in milliseconds, and $\alpha \in \{0.1, 0.125, 0.15, 0.175, 0.2\}$, which is used to compute the mean for the reverse-exponential distribution. The parameter under study for DQ is $m \in \{3, 5, 7, 9, 12, 15, 18, 21\}$, which is the number of contention slots each node chooses from to send the ARS frame. Each of the m contention slots in
<figure><img src="image_3.png" /><figcaption>Fig. 2: Comparison of energy efficiency with up to 5,000 nodes.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3: Overhead and data</figcaption></figure>

DQ is equal to $T_{ARS}$, unlike in RESS-IoT, where each slot is shorter than $T_{RTS}$. After running 21 iterations of the scenario described in Section II-B, the results indicate that the most sensitive parameters for RESS-IoT are $N_{slot}$ and $T_{slot}$, whose product gives the duration of the contention window. Based on this analysis, the best-performing parameters selected for RESS-IoT are $N_{slot} = 100$, $T_{slot} = 60$ms, and $\alpha = 0.125$. For DQ, $m = 9$.

## IV. CASE STUDY RESULTS

This section uses the DRAAF performance metrics to compare RESS-IoT and DQ. The DRAAF toolchain allows us to test with 5,000 nodes under the satellite's footprint. Figure 1a shows that, for all network sizes, DQ has up to 1.3 times the throughput of RESS-IoT because the CTA can resolve collisions quickly. As the number of contending nodes grows, the CTA's time to resolve collisions increases, lowering DQ's throughput but still performing better than RESS-IoT, which slows down to under 40 kb/hour for large networks for 5,000 nodes. DQ and RESS-IoT demonstrate stable behavior, scaling independently of the number of nodes due to the CTA and the reverse-exponential distribution, respectively.

Figure 1b shows that under 2,000 nodes, DQ and RESS-IoT are similarly fair. After 5,000 nodes, DQ becomes fairer than RESS-IoT due to the transmission queue in the DQ protocol, which places nodes with a reservation in the DTQ ahead of their time of transmission. This means nodes that secure a reservation must still wait for the length of the queue

before their collision-free transmission slot. These results are highlighted by the data generation pattern selected for the DRAAF scenario.

Figure 1c depicts the results for the average frame delay. DQ has a 15% higher average delay than RESS-IoT for a network of more than 1,000 nodes. The difference in delay is because, in RESS-IoT, all nodes can transmit data in the same round as their successful reservation, whereas in DQ, only one node can transmit data per round. Furthermore, DRAAF shows that the delay of RESS-IoT and DQ is independent of the number of nodes after a threshold of 1,000 nodes. Out of the DRAAF performance metrics, DQ struggles the most with delay.

Figure 2a depicts the energy efficiency of the satellite. The energy consumption of the satellite is independent of the number of nodes in the system. Therefore, the energy efficiency roughly follows the same trend as the throughput curve for both protocols. Although DQ's satellite energy efficiency lags behind RESS-IoT for medium-sized networks, up to 3,000 nodes, DQ consumes only one-third of the energy, which can be explained by the extra overhead in RESS-IoT.

Figure 2b shows that RESS-IoT nodes have only 20% the energy efficiency of DQ nodes for all counts of nodes. Nodes in DQ do not contend each round but wait in a queue until their turn, reducing the number of transmissions, which allows them to achieve an average energy efficiency of 1 byte / Joule. Nodes in RESS-IoT must re-contend each round if they collided in the previous round. This results in one-tenth of the number of ARS frames sent in DQ compared to the number of RTS frames sent in RESS-IoT. DRAAF shows the average energy efficiency per node is RESS-IoT's biggest weakness.

Figure 3 shows that RESS-IoT generates more overhead than DQ while sending the same data. Although DQ rounds are as little as 20% the duration of RESS-IoT rounds, depending on the value of *m*, there are 10 times the number of RTS frames sent in RESS-IoT than ARS frames in DQ. RESS-IoT also defines one additional control frame. The high overhead for RESS-IoT is reflected in the lower energy efficiency and is the protocol's second most significant weakness.

One of the critical insights we observed using the DRAAF performance metrics is that in DQ, due to the uniform distri-
bution used to reserve a slot, every reservation attempt results
in multiple collisions over multiple rounds before successfully
reserving a slot in the DTQ. The long transmission delays
from LoRa make resolving the queue take much longer than
was previously measured in terrestrial networks, resulting
in lower throughput and higher delay [12]. Another key
finding is that despite the high overhead of RESS-IoT, the
protocol can still maintain throughput performance due to
the reverse-exponential distribution nodes used to reserve a
timeslot. This is a fundamentally different approach than
what most ALOHA-based protocols use. Through DRAAF,
we have identified it as a critical contributor to maintaining
performance at large network sizes.

V. CONCLUSION

Direct-to-Satellite IoT (DtS-IoT) has been a proven so-
lution to many applications with diverse network require-
ments. In this context, one of the major research areas is
scaling the IoT access protocols to work for thousands of
nodes. Until now, there has been no systematic solution for
comparing, choosing, and designing radio access technolo-
gies that can effectively optimize key DtS-IoT metrics. To
evaluate emerging resource allocation mechanisms in DtS-
IoT, this work introduces the DtS-IoT Resource Allocation
Analysis Framework (DRAAF), a systematic methodology,
and a dedicated simulation toolchain to compute performance
metrics. To evaluate DRAAF, we have tested two of the most
promising protocols leveraging the LoRa technology in a DtS-
IoT scenario: Distributed Queuing (DQ) and RESS-IoT. The
results show that DQ has better throughput and energy effi-
ciency at the node level because of the contention resolution
strategy and reduced control message overhead. RESS-IoT
outperforms DQ in average delay and matches DQ in fairness
and satellite energy efficiency. DRAAF allowed us to compare
these protocols and identify potential improvements for DQ
and RESS-IoT.

In future work, we will extend DRAAF with more perfor-
mance metrics, including ones for lifetime modeling and a
standard intuitive graphical representation of the results that
can facilitate consistent scoring between protocols. Finally, to
improve DRAAF's scalability, we will implement the pipeline
in a virtualized cloud environment.

ACKNOWLEDGMENTS

The authors would like to acknowledge the support from
Concordia's PhD Fellowship Award, NSERC Canada Discov-
ery Grant RGPIN-2024-05730, ANID Basal Project FB0008,
and the French National Research Agency (ANR) under the
project ANR-22-CE25-0014-01.

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