# Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol

Jason Gerard, Juan A Fraire, Sandra Céspedes

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Jason Gerard, Juan A Fraire, Sandra Céspedes. Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol. WF-IoT 2024 - IEEE 10th World Forum on Internet of Things, Nov 2024, Ottawa, France. pp.840-845, 10.1109/WF-IoT62078.2024.10811352. hal-05357391

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# Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol

Jason Gerard*, Juan A. Fraire†, 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.

***Abstract***—Direct-to-satellite Internet of Things networks (DtS-IoT) offer a cost-effective way to establish global communication between IoT devices and low Earth orbit satellites. However, budget-conscious DtS-IoT systems face resource constraints in user, space, and ground segments. An increasing limitation in the performance of satellite IoT networks is observed in the link layer. This stems from the prevalent use of ALOHA-based MAC protocols, leading to constraints in managing larger networks. The requirements of DtS-IoT deployments with extensive network sizes amplify the growing demands for energy-efficient and scalable MAC protocols. In this paper, we formulate a new energy-efficient MAC protocol, Massively Scalable Distributed Queuing (MSDQ), inspired by components of the Distributed Queuing (DQ) protocol and the RESS-IoT protocol. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the performance of MSDQ, DQ, and RESS-IoT. Results indicate that MSDQ outperforms DQ by 2.2 times the throughput and 19 times the node energy efficiency of RESS-IoT. These findings highlight the performance gap of existing MAC protocols for large and geographically distributed DtS-IoT systems.

## I. INTRODUCTION

The Internet of Things (IoT) continues to expand rapidly, necessitating highly scalable and energy-efficient communication systems to support its diverse applications across various sectors, from agriculture to industrial automation [1]. Projections indicate that by 2025, the number of connected devices will reach 80 billion, with an anticipated annual economic impact of up to $1 trillion [2].

The Direct-to-Satellite Internet of Things (DtS-IoT) paradigm has surfaced as a network access solution enabling connectivity in remote or unserved regions [3]. This approach facilitates IoT devices' direct communication with overhead satellites, circumventing the necessity for terrestrial infrastructure. With the ongoing deployment of numerous satellite constellations [4], coupled with proposals to integrate IoT devices into the envisioned 3GPP Earth orbit satellite network [5], the prevalence of DtS-IoT is poised to escalate.

The user segment in DtS-IoT networks consists of hardware-constrained IoT devices equipped with batteries that cannot be easily replaced or recharged [6]. The space segment of low Earth orbit (LEO) constellations utilized for serving Internet of Things (IoT) devices is often comprised of small spacecraft with limited resources, such as CubeSat-like nanosatellites and picosatellites, which have restricted

capabilities for power generation and storage. During a 90-minute orbital period, these CubeSats experience eclipses lasting up to 35 minutes, during which they rely on battery power to maintain operations until solar exposure allows for the recharging of solar panels [7]. These constraints fuel a central DtS-IoT research question: How can high energy efficiency be maintained while simultaneously scaling medium access control (MAC) protocols to accommodate thousands of devices? Researchers have explored ALOHA-based IoT MAC protocols to answer this question [8]. However, the protocols that can meet the performance requirements show high complexity and surpass the limits of IoT devices and spacecraft hardware [9]. DtS-IoT MAC protocols face many operational challenges. These include limited or zero network knowledge while demanding energy efficiency, scalability, and low complexity. The space segment possesses constrained knowledge regarding IoT devices, including visibility, channel conditions, and transmission window durations. At the same time, satellites exhibit limited comprehension of the user's and ground segments' topological configurations within their footprint. New MAC protocols have been proposed to target the DtS-IoT domain and meet its unique requirements. However, their performance concerning channel utilization, throughput, and energy efficiency is still the bottleneck for the overall performance and reliability of the DtS-IoT access network [6], [10].

This work proposes a new DtS-IoT MAC protocol, Massively Scalable Distributed Queuing (MSDQ). It provides a comparative analysis with two other state-of-the-art MAC protocols that have shown promise in handling large-scale IoT scenarios. We pair these protocols with an energy model for a detailed performance comparison for small, medium, and large networks. MSDQ, DQ, and RESS-IoT are all fully implemented in the FLoRaSat simulator to account for realistic conditions, including orbital dynamics and LoRa modulation [11].

The remainder of the paper is organized as follows. In section II, we investigate the conditions assumed in previous works for different MAC protocols and present a high-level overview of RESS-IoT and DQ. In section III, we describe the system model, including the traffic distribution pattern and network sizes. In section IV, we introduce MSDQ, define the energy model, and perform a sensitivity analysis. Next, in
section V, we present the comparative evaluation. Finally, in section VI, we provide the conclusions and future work.

## II. RELATED WORK

Satellite IoT MAC protocols can be grouped into four categories based on their core operating mechanism: ALOHA-based, interference cancellation, reservation and adaptive, and hybrid [9]. Although reservation-based protocols are more complex than random-access protocols because they need to synchronize, they can coordinate collision-free access to the channel, improving the chance of successful transmissions in large networks of thousands of nodes.

Previous research indicates that the RESS-IoT protocol, belonging to the reservation category, exhibits superior scalability in terms of throughput, surpassing 1,000 nodes, whereas ALOHA-based protocols like Unconfirmed Class A LoRaWAN and Enhanced ALOHA MAC protocols experience a drop in throughput below 10 kilobytes/hour [6]. Another reservation-based MAC protocol, Distributed Queuing (DQ), recently gained research attention due to its promise in large IoT networks [12]. DQ has also been extended to work over a LoRa-based physical layer, but only numerical results have been collected so far [13].

a) **RESS-IoT Overview:** RESS-IoT [6], takes the Sift protocol [14] and adds modifications for the specific purpose of the DtS-IoT use case. These include decoupling the data generation of the device and the data transmission and adding a beacon control message to notify ground nodes that a satellite is overhead. The RESS-IoT protocol employs a reservation-based mechanism organized in rounds to coordinate data transmissions. Each round is segregated into two distinct phases: a reservation phase and a transmission phase. During the reservation phase, the satellite broadcasts a beacon frame to the sensor nodes. Any sensor node with data to transmit responds by sending a Request-to-Send (RTS) frame to the satellite, selecting a slot using a reverse-exponential distribution. In the subsequent transmission phase, the satellite broadcasts a Variable-length-Clear-to-Send (VCTS) frame, specifying the channel access sequence for the nodes. Finally, the nodes transmit their data frames to the satellite.

b) **DQ Overview:** Distributed Queuing (DQ) [12] was initially proposed for cable TV distribution [15] but has been adapted for many use cases including IoT environments over the LTE and LoRa physical layers [12], [13]. The DQ Protocol uses a reservation-based approach with rounds and a contention tree algorithm (CTA) to coordinate data transmission. Each DQ round is divided into three distinct phases. First, a contention resolution phase comprising *m* timeslots, determined through a uniform distribution, where each node with data in its buffer transmits an Access-Request-Sequence (ARS) frame to the satellite. Second, a transmission phase with a single collision-free data slot, where the node at the head of the data transmission queue transmits its data. Third, a feedback phase, during which the satellite transmits a Feedback-Packet (FBP) frame containing *2* · *m* bits that

<figure><img src="image_2.png" /><figcaption>Fig. 1: Network Architecture of Telemetry Deployment</figcaption></figure>

indicate the outcomes of the contention slots. Each node utilizes these results as input for the CTA to compute their new positions and lengths in the data transmission queue (DTQ) and contention resolution queue (CRQ).

## III. SYSTEM MODEL

Figure 1 shows the DtS-IoT telemetry deployment with a single LEO satellite orbiting over a region containing $N \in \{10, ..., 5000\}$ IoT sensor nodes that produce telemetry data every $t = [30, 90]$ seconds, with an initial offset $t_0 = [0, 60]$ seconds. The visibility period of the satellite and the duration of the simulation runs are 540 seconds.

The system is designed to emulate a delay-tolerant telemetry network, similar to those employed in a Ring Road Network [16]. The satellites use the store-carry-forward paradigm, where they store data locally until they orbit over a ground station and then offload the data [17]. The sensor nodes and satellites set their radio transceivers between transmit, receive, and sleep modes to conserve energy. The slot duration is calculated as ($d_{trans} + d_{prop} + d_{guard}$). The transmission delay, ($d_{trans}$), represents the time on air (ToA) for a LoRa frame, while the propagation delay, $d_{prop}$, is derived from the satellite's average altitude, and the guard time, ($d_{guard}$), accounts for the variability in actual distances between nodes and the satellite. To maintain a consistent ToA for LoRa frames, fixed-size data fields are utilized: payload (63 B), frame type field (1 B), and a unique ID field (2 B).

To effectively compare the protocols, we use the FLoRaSat network simulator [11], based on the OMNeT++ framework, to perform the evaluation. FLoRaSat implements the LoRa Chirp Spread Spectrum modulation, developed by Semtech, for communication between IoT nodes and the nanosatellite following a star network topology [18]. LoRa signals are modulated with orthogonal Spreading Factors (SF) defined
<figure><img src="image_3.png" /><figcaption>Fig. 2: MSDQ Round Structure</figcaption></figure>

with different bit rates. Previously, it has been shown that SF10 provides the best trade-off between transmission success and energy consumption in a DtS-IoT scenario [6]. One of the main areas of study for the DtS-IoT physical layer has been LoRa, due to its low power consumption for geographically distributed telemetry systems [19], [20].

## IV. MSDQ PROTOCOL

MSDQ uses a reservation-based access scheme based on a reverse-exponential distribution, a contention tree algorithm (CTA), and two distributed logical queues to orchestrate data transmission and collision resolution. Each MSDQ round is split into three phases. First, a contention resolution phase with *m* timeslots, where each node with data to transmit picks one of the *m* slots, using a reverse-exponential distribution, and sends an Access-Request-Sequence (ARS) frame to the satellite in that slot. Second, a transmission phase with *t* collision-free data slots, where the first *t* nodes at the head of the data transmission queue (DTQ) send data. Third, the feedback phase, where the satellite sends a Feedback-Packet (FBP) frame that contains *2*·*m* bits, where each bit pair indicates the results of a contention slot (Success, Collision, or Empty). Each node will use these results as input to the CTA to compute their new DTQ and CRQ positions and lengths. The source code of protocol implementations in FLoRaSat is publicly available<fnref n="1" />.

The main improvements in MSDQ address the limitations of DQ in the context of LoRa-based satellite communication. A reverse-exponential distribution replaces the uniform distribution, improving the probability of successful slot reservation. This mechanism is inspired by Sift [14] and RESS-IoT [6]. Additionally, MSDQ introduces two parameters: *t*, allowing for transmission of multiple data frames per round, inspired by EE-DQRA [21], and *k*, allowing multiple active reservations per node. MSDQ combines these improvements with the contention tree algorithm (CTA) and the two logical distributed queues of DQ.

Both queues are logical queues defined on each device by their index(s) and the total length. The collision resolution queue (CRQ) contains the node IDs that had collisions in previous rounds while contending for a slot. Each round, the node's position in this queue is decremented by 1. When the node's position in the CRQ is 0, it will again contend for a contention slot. The data transmission queue (DTQ) contains the node IDs that have reserved their position to transmit data. This queue is logically defined by a set of *k*+1 numbers on each device: the total length of the queue and the

<figure><img src="image_4.png" /><figcaption>Fig. 3: MSDQ Contention Tree Algorithm</figcaption></figure>

device's, at most, *k* positions. Each round, the node's position in this queue is decremented by the number of transmissions *min(len(DTQ), t)* that round. When the node's position in the DTQ is less than the maximum number of transmissions in the round, *pos(DTQ) < t*, they will transmit their data frame in the corresponding collision-free data slot. The overall MSDQ round structure can be seen in figure 2.

### A. Protocol Operation

a) **Contention Phase:** Each MSDQ round starts with each node evaluating if it satisfies all of the following constraints: the node must have less than *k* positions in the DTQ, the node must either not be in the CRQ or have a CRQ index of 0, and the node must have data to send. If the node fails any constraint, it will go to sleep. If the node meets all the constraints, it will pick a slot from a reverse-exponential distribution with its mean equal to *α*·*T<sub>ARS</sub>*·*m*, then clamp the sample to the start of the closest contention slot. The node will sleep until the start of its chosen contention slot. When the node wakes at the beginning of its time slot, it will broadcast the ARS frame, and then return to sleep.

b) **Transmission Phase:** At the start of the phase, the first *max(len(DTQ), t)* nodes in the DTQ will wake up, compute their collision-free data transmission time slot equal to *pos(DTQ)*·*T<sub>data</sub>*, and return to sleep. When the node wakes at the beginning of its time slot, it will transmit the data frame to the satellite, and then return to sleep.

c) **Feedback Phase:** Every MSDQ round ends with all nodes waking up and setting their radios to receive. The satellite will broadcast the FBP frame, which contains the results of the contention slots. Each node will use the *m* slot results, which can each be a collision, success, or empty, as input to the CTA to compute their new CRQ and DTQ positions and lengths. Figure 3 shows a multi-round sequence

<footnote n="1">¹https://gitlab.inria.fr/jfraire/florasat</footnote>
where the CTA coordinates re-contention transmissions after collisions.

## B. Energy Model

For comparative multi-protocol evaluation, energy models are derived from the radio module operation descriptions by dividing rounds into phases (e.g., contention, transmission, feedback) and calculating energy consumption based on radio state duration (transmit, receive, sleep).

The energy consumption of the satellite and the n-th node during the contention phase are defined in (1) and (2), respectively, as:

$$E_{sat}^{CP} = P_{rx} \cdot T_{ARS} \cdot m, \quad (1)$$

$$E_{n}^{CP_{r}} = B_{n,r}^{ARS} \cdot P_{tx} \cdot T_{ARS}, \quad (2)$$

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 (3) and (4), respectively, as follows:

$$E_{sat}^{TP_{r}} = \min(len(DTQ), t) \cdot P_{rx} \cdot T_{data}, \quad (3)$$

$$E_{n}^{TP_{r}} = B_{n,r}^{data} \cdot P_{tx} \cdot T_{data}, \quad (4)$$

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 (5) and (6), respectively, as:

$$E_{sat}^{FP} = P_{tx} \cdot T_{FBP}, \quad (5)$$

$$E_{n}^{FP} = P_{rx} \cdot T_{FBP}, \quad (6)$$

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

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

$$E = E_{sat} + E_{N}, \quad (7)$$

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

$$E_{sat} = R \cdot E_{sat}^{CP} + \sum_{r=1}^{R} E_{sat}^{TP_{r}} + R \cdot E_{sat}^{FP}, \quad (8)$$

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

$$E_{n} = \sum_{r=1}^{R} (E_{n}^{CP_{r}} + E_{n}^{TP_{r}}) + R \cdot E_{n}^{FP}, \quad (9)$$

$$E_{N} = \sum_{n=1}^{N} E_{n}. \quad (10)$$

## C. Sensitivity Analysis

We performed a sensitivity analysis to determine which parameters work well together and which have the most significant positive or negative effect on the selected performance metrics. The parameters studied for MSDQ are $m \in \{3, 5, 7, 9, 12, 15, 18, 21, 24, 31, 41\}$, the number of contention slots each round. The larger m is, the more opportunities there are to send an ARS frame without colliding with another node, but it also increases the round duration and overhead. The parameter $\alpha \in \{0.15, ..., 0.35\}$ is used to compute the mean of the reverse-exponential distribution used for the reservation. The parameter $t \in \{1, 3, 5, 7, ..., m\}$ is the maximum number of transmissions per round by the first t nodes in the DTQ. The parameter $k \in \{1, 3, 5\}$ is the maximum number of positions each node can hold in the DTQ.

The most critical parameter is m, where setting $m > 9$ showed a significant increase in performance. Setting $m = 21$ performed the best for throughput, fairness, overhead, energy efficiency at the node and satellite, and delay. Increasing m past 21 shows no increase in throughput but a higher delay due to longer rounds. A $\alpha = 0.3$ value had the best overall performance for most values of m. $\alpha > 0.3$ values showed no change, and $\alpha < 0.3$ values performed better for values of m < 9. Through experimentation, we found an excellent heuristic, $t = m$, allowing every data frame successfully reserved to be sent the following round. Our findings show that k has no impact when $t = m$ but may be beneficial when $t < m$. Based on this analysis, the best-performing parameters selected for MSDQ are $m = 21$, distribution = exponential, $\alpha = 0.3$, $t = m$, and $k = 1$. The protocol generalizes well to different scenarios, but the parameters should still be tuned to maximize performance for a particular scenario. If parts of the system model change, for example, the data generation rates of the devices, the parameters should be re-evaluated.

# V. COMPARATIVE EVALUATION

The network parameters follow the system model defined in section III, and the LoRa parameters for the nodes and satellite are configured with the same values defined in [6]. Based on the Semtech LR1110 transceiver data sheet, the power consumption for reception is 25.74 mW 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°. The parameters for MSDQ are found through a sensitivity analysis performed in IV-C. The best-performing parameters, also found through a sensitivity analysis, for RESS-IoT are $N_{slot} = 100$, $T_{slot} = 60$ms, and $\alpha = 0.125$, and for DQ, $m = 9$.

**Throughput (S):** Defined as $K/T$, where T is the period that the satellite receives the K bits of data, is a performance metric where MSDQ noticeably outperforms the competition at high node counts. Figure 4a shows that MSDQ has a throughput of 85 kB/hr at 5,000 nodes, over 2.2 times the
<figure><img src="image_5.png" /><figcaption>Fig. 4: Performance Metric Comparison for up to 5,000 Nodes.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5: Energy and Overhead Metric Comparison for up to 5,000 Nodes.</figcaption></figure>

throughput of RESS-IoT and DQ. MSDQ takes advantage of the CTA and the reverse-exponential distribution reservation scheme to provide steady and high throughput for all node counts. Unlike RESS-IoT, MSDQ does not get overwhelmed as the number of nodes increases because the CTA acts as a back-off mechanism, decreasing the number of simultaneous transmissions.

**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. Figure 4b shows that before 2,000 nodes, all protocols are equally fair. After 2,000 nodes, MSDQ and DQ are the most fair. We identified that the number of slots and the CRQ are responsible for maintaining fairness with large networks because nodes will be placed in smaller contention pools with more opportunities to reserve, increasing the probability of reserving channel access.

**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. One of the most significant issues with DQ and MSDQ is the high delay caused by the CTA, which splits colliding nodes into a balanced tree. For these two protocols, deviations between delays will be minor, but the overall delay will be significant. RESS-IoT has the opposite situation. More significant deviations occur between delays but an overall lower average delay. Figure 4c

shows this relationship, where MSDQ and DQ, for large networks, have about the same average delay of 275 seconds, and RESS-IoT has a lower delay, around 230 seconds, which doesn't change concerning the number of nodes. Other MSDQ configurations, with a reduced number of contention slots, achieve a lower average delay than RESS-IoT, 200 seconds at 5,000 nodes. However, those configurations also have a lower throughput as a consequence.

**Satellite Energy Efficiency ($\xi_{sat}$):** Defined as $\frac{K_{sat}}{E_{sat}}$, where $K_{sat}$ is the number of data bits successfully received by the satellite, and $E_{sat}$ is the total energy spent by the satellite. $K$ does not consider control information, such as bits from RTS, VCTS, beacon, ARS, and FBP frames. The energy efficiency of the satellite, shown in figure 5a, represents the space segment of the system. 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. MSDQ has the lowest energy consumption and the highest throughput, producing the highest satellite energy efficiency. MSDQ can achieve 600 bytes/joule, whereas RESS-IoT and DQ are between 200 - 250 bytes/joule. This is a massive improvement for MSDQ over the state-of-the-art, which results in satellites consuming much less energy to send the data required by the deployment. This will also allow MSDQ satellites to pass through eclipse periods with less difficulty of a power outage halting operation.

**Average Energy Efficiency per Node ($\xi_N$):** Defined as $\frac{K_{sat}}{E_N}$,
where $K_{sat}$ is the number of data bits successfully received by the satellite, and $E_N$ is the total energy spent by the nodes. $K$ does not consider control information, such as bits from RTS, VCTS, beacon, ARS, and FBP frames. The average energy efficiency per node, shown in figure 5b, represents the user segment of the system. MSDQ achieved 3.3 bytes/joule at 5,000 nodes, 19 times more efficient than RESS-IoT with 0.175 bytes/joule. Nodes in MSDQ and DQ do not contend each round but wait in a queue until their turn, reducing the number of transmissions in the reservation phases and minimizing wasted energy. 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 MSDQ compared to the number of RTS frames sent in RESS-IoT, which is primarily responsible for the difference in energy efficiency.

**Overhead:** Measured as the control-related bits exchanged by nodes and the satellite. This includes bits from RTS, VCTS, beacon, ARS, and FBP frames. Figure 5c shows that RESS-IoT generates exponentially more overhead than MSDQ and DQ. The most significant factor contributing to the high overhead of RESS-IoT is that there are 10 times the number of RTS frames sent in RESS-IoT than ARS frames in MSDQ. RESS-IoT also defines one additional control frame. The CTA is responsible for the low overhead of MSDQ and DQ, which allows the nodes to send minimal control frames, letting the two queues do the work while the nodes turn off their radios and wait to re-contend when the contention pool size is smaller.

## VI. CONCLUSION

Direct-to-Satellite IoT (DtS-IoT) has emerged as a viable approach for applications with diverse network requirements. A significant research focus in this domain is the adaptation of IoT access protocols to accommodate thousands of nodes. Until now, little research has been done on designing access protocols that can effectively optimize key DtS-IoT metrics while meeting the challenging constraints of DtS-IoT. We have designed and evaluated a new DtS-IoT MAC protocol, MSDQ, whose properties and parameters were derived from DQ and RESS-IoT. The experiment results show that MSDQ has better throughput, node energy, and satellite energy efficiency. The design of MSDQ allows it to meet the QoS needs of network operators who require DtS-IoT access networks by leveraging the reverse-exponential distribution and the CTA to provide contention-free channel access in networks of thousands of nodes. In future work, we will investigate adapting MSDQ to NB-IoT and evaluating it under different IoT traffic patterns in real-world deployments.

## ACKNOWLEDGMENTS

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

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