Despite their widespread adoption, cellular networks face growing vulnerabilities due to their inherent complexity and the integration of advanced technologies.One of the major threats in this landscape is Voice over IP (VoIP) to GSM gateways, known as SIMBox devices.These devices use multiple SIM cards to route VoIP traffic through cellular networks, enabling international bypass fraud with losses of up to $3.11 billion annually.Beyond financial impact, SIMBox activity degrades network performance, threatens national security, and facilitates eavesdropping on communications.Existing detection methods for SIMBox activity are hindered by evolving fraud techniques and implementation complexities, limiting their practical adoption in operator networks.This paper addresses the limitations of current detection methods by introducing SigN , a novel approach to identifying SIMBox activity at the cellular edge.The proposed method focuses on detecting remote SIM card association, a technique used by SIMBox appliances to mimic human mobility patterns.The method detects latency anomalies between SIMBox and standard devices by analyzing cellular signaling during network attachment.Extensive indoor and outdoor experiments demonstrate that SIMBox devices generate significantly higher attachment latencies, particularly during the authentication phase, where latency is up to 23 times greater than that of standard devices.We attribute part of this overhead to immutable factors such as LTE authentication standards and Internet-based communication protocols.Therefore, our approach offers a robust, scalable, and practical solution to mitigate SIMBox activity risks at the network edge.

Irregular Repetition Slotted ALOHA (IRSA) is an effective grant-free random access scheme that is well-suited for managing the sporadic nature of IoT traffic, particularly in dense environments prone to collisions. In this paper, we evaluate the performance of IRSA under realistic conditions involving imperfect successive interference cancellation (SIC) and non-ideal physical layer environments. Specifically, we investigate the impact of various channel conditions and physical layer impairments on IRSA's performance. Previous studies on IRSA often assume ideal physical layer conditions or use simplified models for SIC errors, which fail to fully capture practical implementation complexities. To address this gap, we propose integration of practical factors, such as channel estimation imperfections, into our model of SIC failures using detailed baseband simulations. Based on that, we employ density evolution analysis to evaluate system throughput and optimize the degree distributions to enhance IRSA performance in the presence of imperfect SIC. Additionally, we analyze the power of the residual interference to assess its impact on decoding performance under realistic conditions. Our results focusing on 5G CIoT demonstrate that optimizing IRSA parameters, while accounting for SIC errors, can significantly improve system performance, resulting in notable throughput gains.

As 5G technology is widely adopted, enterprises seek solutions for automation and rapid service delivery. Network Slicing (NS) leverages 3GPP standards to create multiple, customized network slices on shared infrastructure, serving diverse applications and user groups. This paper focuses on 3GPP 5G Core NS, particularly Release 17, and proposes Adaptive Network Slice Balancing (ANSB) to optimize resource utilization by adjusting User Equipment (UEs) and Protocol Data Unit (PDU) sessions. Extensive experimentation, with 5G OpenAirInterface (OAI) testbed, demonstrates significant improvements in UEs, PDU sessions, and maximize overall data rate consumption.

Efficient random access can be used in scenarios with a massive number of IoT devices. Among modern random access protocols, Irregular Repetition Slotted ALOHA (IRSA) offers excellent asymptotic performance (for large frame sizes), but its finite-frame efficiency is lower and difficult to optimize analytically. In this work, we introduce limited mid-frame feedback to better coordinate users and improve performance: Feedback-Integrated Two-phase IRSA (FIT-IRSA). We formulate IRSA with feedback as a deep reinforcement learning (DRL) problem. Using policy gradient methods, we learn transmission strategies that improve throughput under varying loads, as demonstrated in our simulation results. This provides a practical alternative to classical density-evolution–based optimization, which applies mainly to large frames.

Irregular Repetition Slotted ALOHA (IRSA) is a leading candidate for random access and grant-free communication in future Cellular IoT (CIoT) networks, including those envisioned for 6G and beyond. Classical analyses of IRSA typically assume ideal conditions; however, real deployments are subject to practical impairments. In particular, the capture effect enables packets to be decoded despite collisions when their signal-to-interference ratios exceed certain thresholds, and imperfect successive interference cancellation (SIC), due to channel estimation errors, further complicates decoding dynamics. In this paper, we are the first to develop a unified analytical framework that incorporates both phenomena into the IRSA design. Using a threshold-based capture model and a detailed residual interference analysis, we apply density evolution to derive asymptotic throughput bounds. Our results show that by optimizing the user degree distribution, IRSA can significantly mitigate performance loss under non-ideal SIC conditions. Extensive simulations validate our theoretical findings, revealing that performance improvements are attainable even in high-density CIoT scenarios.

Vision Transformer (ViT) is a deep learning model that plays a significant role in advanced computer vision and pattern recognition tasks but faces challenges in inference due to high computational costs and energy consumption. Split computing, which distributes the load between an edge server and a mobile device, is proposed to address these issues. Unfortunately, most literature focuses on split computing for CNNs, with limited attention to ViT split computing. In this paper, we explore the use of split computing to optimize ViT's inference process by limiting usage of bandwidth. We propose compressing the latent space data by introducing more sparsity in the intermediate features. This sparsity is then exploited through a compression algorithm before transmitting the data through a communication channel. To understand and explain the performance of our approach, we analyze the latent space data using several metrics. Our approach obtains a significant compression ratio without causing a substantial decrease in accuracy. It is computationally efficient and do not require retraining the model.

Reducing the number of bytes transmitted by a low-power wireless device greatly reduces its power consumption. While header compression is a well-studied topic with solutions such as SCHC that are well-established standards, very little work exists on compressing the payload. This is all the stranger that the payload typically contains more bytes than the headers. This demonstration introduces Dixy, a payload compression technique which can be used alongside SCHC. We implement SCHC and Dixy on the nRF52840, a popular micro-controller. We have them compress packets collected from a real-world deployment by startup company Falco. We show how the resulting joint header and payload compression reduces the number of bytes exchanged between two boards by 74%. The demonstration allows visitors to understand SCHC and Dixy, trigger packets being compressed and transmitted, and observe the number of bytes and the charge consumed with enabling header and/or payload compression.

Path planning for search missions carried out by Unmanned Aerial Vehicles (UAVs) is a challenging problem. This is due to UAV limited energy budget and the importance of time for search operations. The objective of this study is to come up with an approach to minimize the total search time required to locate a specific target. To achieve this, we deployed a deep reinforcement learning (DRL) model based on the Proximal Policy Optimization (PPO) algorithm to solve the combinatorial optimization problem of UAV search path planning within a minimized search time. A smart reward formulation is designed to achieve the learning goal, fulfill the search requirement, and encourage the agent to select search paths that minimize search time. In addition, we employed Optuna hyperparameter optimization framework to systematically select optimal parameters for the PPO model. Most importantly, thanks to the state representation we considered, the model is generalized and adaptable to various search environments. The PPO model succeeds to compute an accurate search path to be followed by the UAV searcher. Results of the model are compared with results previously obtained with a linear program. We found that the PPO achieves almost the same expected search time, which proves the great relevance of the reward design and the hyperparameters selection we made.

Livestock farming in agriculture has recently wit-nessed the surge of integrating various information and communication technologies for digital farming that improve the efficiency of resource use and increase the added value of agricul-tural products. In this context, this paper proposes a pioneering secured solution for cattle health monitoring. In particular, we focus on secured tracing of animals that have recently been in the vicinity of an infested one. We propose a new message structure that enables real-time detection based on the signal strength. Secured message exchange is ensured through Elliptic curve cryptography. Further, to avoid potential falsification of epidemic history, we develop a mechanism capable of detecting malicious messages wrongly claiming infection. Performance study shows that the proposed model satisfies all privacy requirements in the context of contact-tracing applications.

Delays in blockchain networks are mainly related to consensus protocols. Among these protocols, we focus on a specific family of protocols, where the mempool's role in the consensus mechanism is explicitly examined. A mempool is a temporary storage area for transactions waiting to be included in a block. This study investigates the round duration of two mempool-based protocols: one requiring a single quorum of messages and another demanding two. We perform the delay analysis with two approaches. First, we elaborate on a Markov chain to determine the distribution of the round durations. Second, we establish an analytical model of message delays while assuming an exponential distribution of message propagation delays. Finally, asymptotic analysis is conducted to estimate the time of quorum formation. We end the paper by comparing the simulation results with the theoretical ones. Results show that both results are very close. This research offers valuable insights into the performance characteristics of mempool-based consensus protocols, aiding in the design and optimization of blockchain systems.

This article investigates data dissemination delays in a Directed Acyclic Graph (DAG)-based Byzantine Fault Tolerant (BFT) blockchain. We focus particularly on the Narwhal protocol, a mempool-based approach for efficiently disseminating transactions and constructing a DAG. Narwhal is designed to work alongside a BFT consensus protocol like Tusk. Tusk then orders the transaction metadata based on the DAG information. Through an in-depth analysis of the protocol messages, we establish a mathematical model for message propagation delays. We start by considering a specific probability distribution for data network propagation delays: Gaussian Distribution. Then, we consider a general propagation delay distribution. Also, we assume large networks and apply some approximations, i.e., the Central Limit Theorem (CLT). Finally, we develop the Narwhal protocol and demonstrate that the simulated delaysarecompatible with the theoretical ones.

One of the main difficulties to enable the future scaling of IoT networks is the issue of massive connectivity. Recently, Modern Random Access protocols have emerged as a promising solution to provide massive connections for IoT. One main protocol of this family is Irregular Repetition Slotted Aloha (IRSA), which can asymptotically reach the optimal throughput of 1 packet/slot. Despite this, the problem is not yet solved due to lower throughput in non-asymptotic cases with smaller frame sizes. In this paper, we propose a new variant of IRSA protocol named Deep-Learning and Sensing-based IRSA (DS-IRSA) to optimise the performance of IRSA in short frame IoTs, where a sensing phase is added before the transmission phase and users' actions in both phases are managed by a deep reinforcement learning (DRL) method. Our goal is to learn to interact and ultimately to learn a sensing protocol entirely through Deep Learning. In this way, active users can coordinate well with each other and the throughput of the whole system can be well improved. Simulation results show that our proposed scheme convergence quickly towards the optimal performance of almost 1 packet/slot for small frame sizes and with enough minislots and can achieve higher throughput in almost all cases.

As 5G Cellular Vehicle-to-Everything (C-V2X) technology takes the lead in V2X communication, it opens the possibility for telecommunication service providers to offer Vehicle-to-Network (V2N) services using their existing 5G network infrastructure. To enhance the security of 5G V2N services, in this paper we propose a novel collaborative V2X misbehavior detection system. This system would safeguard the V2X application servers (V2X ASs), deployed in the 5G edge network, from any malicious V2X position manipulation attacks. Our proposal includes two enhanced machine learning models. The first model utilizes historical data to conduct On-Road Plausibility Checks (ORPC), while the second model builds upon the first by enabling collaboration among edge detection nodes through the sharing of attack ratios for each vehicle. Our proposed models were tested using extensive 5G core-network emulations, yielding excellent results. The first model achieved a notable accuracy improvement from 73% to 91%, while the second model further enhanced the accuracy to an impressive 95%.

Federated learning provides access to more data which is paramount for constrained LoRaWAN devices with limited memory storage. Learning on a larger data set will reduce the variance of the learned model, hence reducing its error. However, federating the learning process incurs a communication cost among learning devices that must be taken into account. In this paper, we formulate a Cooperative Hedonic game and introduce a new cost function that captures both the learning error and communication cost. LoRaWAN devices engage in the devised game by identifying if they should keep their learning local or federate with other devices in order to reduce both their learning error and communication cost. We compute the optimal size of formed coalitions and assess their stability. Then, we show through extensive simulations that devices have incentive to form learning coalitions depending on the data characteristics at hand and the communication cost in LoRaWAN.

As the main goal of connected autonomous vehicles' communications is to improve the traffic safety and save lives, any design of a resource allocation scheme must consider the stringent requirements of these applications in terms of latency and reliability for a dynamic environment. For this, 5G cellular networks suitably address these challenges. This paper proposes a new mechanism for the telco operator to adapt the physical (PHY) layer configuration for efficient radio resource management in 5G New Radio (NR) based system. To tackle this issue, we propose to adjust the PHY layer numerology configuration by fine-tuning it with a Radio-Aware Adaptive PHY Layer Configuration (RA-APC) algorithm in order to maximize the efficiency of radio resource management by using the Effectively Transmitted Packet (ETP) value. Extensive simulations show that our proposal RA-APC achieves strong improvements in terms of ETP, reliability and latency while considering safety and non-safety traffic scenarios.

5G Cellular Vehicle-to-Everything (C-V2X) is expected to become the dominant technology to enable Cooperative Intelligent Transport System (C-ITS) applications. In this paper we address the problem of detecting falsified vehicle positions sent by misbehaving vehicles targeting C-ITS application servers over 5G networks. We propose a novel security system as a 5G application function. It is based on machine learning and integrated with the 5G core network to monitor, detect and prevent potential misbehavior. Based on extensive network simulations utilizing 5G network emulator, our proposal achieves very good performances, accurately reported 99% of misbehaving vehicles and scored an 86% detection rate on the messages' level.

This work exploits the potential of two important technologies which are UAVs (Unmanned Aerial Vehicles) and blockchain in the context of Agriculture 4.0. We propose a cattle health monitoring system based on UAVs that collect health measures from IoT devices equipping the animals. The main objectives of our system are twofold. First, the consumer will be aware of the quality of his/her food. Second, the national ecosystem (e.g. agriculture ministry, trade ministry) will get useful information about the quality and the number of cattle that can be put on the market. Thus, smart cattle management strategies could be undertaken afterward. The involved entities in such a system are multiple: the farmer, the veterinaries, the ministry of agriculture, etc… We first start by studying the system’s security by applying the FMEA risk assessment methodology. Our findings motivate us to integrate blockchain technology to manage the data collected as well as the attribution of the UAVs missions via a marketplace. Thanks to its properties, this technology ensures the transparent tracking of cattle status and fairness in the payment of the UAVs managed by private operators. Finally, we develop a proof of concept using the Sui blockchain platform.

The emergence of 5G Cellular Vehicle-to-Everything (C-V2X) has made it the predominant technology for enabling Vehicle-to-Everything (V2X) communications. As a result, this has created an opportunity for telecommunications service providers to leverage their pre-existing 5G network infrastructure, enabling them to provide Vehicle-to-Network (V2N) services. In this paper, we propose a new approach that enhances the security of 5G V2N services through the implementation of a Federated Learning V2X misbehavior detection system within the 5G core network. The proposed system aims to protect V2X application servers (V2X ASs) that are located in 5G edge networks against potential V2X attacks while leveraging the privacy and scalability advantages of Federated Learning. Our proposed model is compared, using extensive emulations, to other centralized and distributed approaches, achieving excellent results, which makes it feasible for deployment. Our proposal achieved a notable accuracy of 98.4%, while scoring an impressive 99.3% precision and 96.9% detection rate.

Multi-hop wireless networks can be optimized using directional antennas, as they allow for in-depth interference management and network topology optimization. This type of optimization involves ensuring high operational guarantees such as instantaneous connectivity, minimum SNRs and SINRs thresholds, and improved QoS. It simplifies tasks of future network layers and allows for more relaxed routing protocols and scheduling. However, attaining optimal performance via network configuration involves selecting an antenna orientation for each node to create a link with another node. This is challenging, especially when the process is carried out in real-time. To tackle this challenge, we present nodes2net, a Deep Neural Network (DNN) that is trained to imitate solved, ideal network instances. This approach uses nodes' positions as inputs and produces a set of links as output. By leveraging learning of patterns and theoretically driven properties, nodes2net can generate reliable network configuration solutions when dealing with new sets of node positions. It utilizes efficient neural network aggregation operators to facilitate and process information about the nodes, to finally produce the final solution as set of links. Our results demonstrate the competitive performance of this method.

Low latency and high-reliability communications for applications' flows is one of the main 5G cellular network objective, which is especially relevant for connected autonomous vehicles. However, efficient wireless resource allocation is a complex. To address this problem in this paper, we propose to adapt the physical (PHY) layer numerology configuration by fine-tuning it with Adaptive PHY Layer Configuration (APC) algorithm in aim to maximize the Effective Transmitted Packet (ETP). Besides, we propose an adaptive scheme to maximize the expected packet serving rate while avoiding the starvation phenomenon of low priority Logical Channels (LC). Based on extensive simulations, results show that our proposal achieves good performance in terms of ETP maximization and starvation minimization of low priority LCs.

Unmanned Aerial Vehicles (UAVs) are being extensively deployed in numerous Livestock Management applications such as cattle disease diagnosis, counting and behavioral monitoring from videos and images captured by drones. The paper focuses on one increasingly important family of applications, UAV-assisted cattle monitoring applications where the objective is to remotely acquire some health state information from IoT nodes attached to the herding cattle. Such livestock data acquisition applications have many challenges. One of these challenges, which is the focus of this paper, is the problem that the target cattle position may not be known precisely, and might be defined with a large area. To address this issue, we design a formulation of this UAV-cattle search path problem as a mathematical optimization problem and show how it can be derived from other well-known formulation and related literature. A Mixed-Integer linear Programming (MILP) formulation is introduced to minimize the expected search time while covering all the search area to efficiently locate the animal. This formulation exploits a cattle position probability distribution map. The results show that the suggested approach yields excellent results using the existing MILP solvers.

Unmanned Aerial Vehicles (UAVs) and blockchain Technologies are relevant systems that have a significant performance in numerous sectors. In particular, applying these emerging technologies will affect positively the agricultural ecosystem. In this paper, we investigate the opportunities offered by UAV s and blockchain (BC) in the agricultural sector. We review recent research efforts in the subject with a synthesis illustrated by a classification table. Finally, open challenges and future directions for IoT-based agriculture applications are discussed.

The Internet of Things (IoT) aims to connect billions of devices, most of which are power and memory-constrained. Such constraints require efficient network access. “Irregular Repetition Slotted Aloha” (IRSA) meets such requirements. In this paper, we optimize IRSA using Deep Reinforcement Learning to obtain Deep-IRSA, and introduce variants that allow retransmission and user priority classes. We observe the learned degree distribution and throughput, showing that Deep-IRSA performs excellently, is generic, and could well replace known approaches for smaller frame sizes and IRSA variants.

In recent developments in machine learning, a trend has emerged where larger models achieve better performance. At the same time, deploying these models in real-life scenarios is difficult due to the parallel trend of pushing them on end-users or IoT devices with strong resource limitations. In this work, we develop a novel technique for executing parts of a single model successively through multiple devices (IoT, edge, cloud) while respecting each device’s resource limitations. For that, we introduce a new offloading mechanism where, during computation, a decision can be made to offload work, together with the ability to exit early in the computation with intermediate results. The decision itself is tuned through Deep Q-Learning.

A modern method of random access for packet networks, named “Irregular Repetition Slotted Aloha (IRSA)”, had been proposed: it is based repeating transmitted packets, and on the use of successive interference cancellation at the receiver. In classical idealized settings of slotted random access protocols (where slotted ALOHA achieves 1/e), it has been shown that IRSA could asymptotically achieve the maximal throughput of 1 packet per slot. Additionally, IRSA had previously been studied for many different variants and settings, including the case where the receiver is equipped with “multiple-packet reception” (MPR) capability. In this article, we extensively revisit the case of IRSA with MPR. First, one of our major results is the proof that K-IRSA cannot reach the natural bound of throughput, and we prove a new, lower bound for its performance. Second, we give a simple expression for its excellent loss rate at lower loads. Third, we show how to formulate the search for the appropriate parameters of IRSA as an optimization problem, and how to solve it efficiently. By doing that for a comprehensive set of parameters, and by providing this work with simulations, we give numerical results that shed light on the performance of IRSA with MPR.

Irregular Repetition Slotted Aloha (IRSA) is one candidate member of a family of random access protocols to provide solutions for massive parallel connections in the Internet of Things (IoT) networks. The key features of this protocol are repeating the transmitted packets several times and using Successive Interference Cancellation (SIC) at the decoder to resolve the collisions, which dramatically increases the performance of Slotted ALOHA. Motivated by multiple previous studies of IRSA performance in different settings, we focus on the scenario of an IoT network where the packets of different nodes are received with different powers at the base station, either per design due to different transmission power, or induced by the fact that the nodes are at different distances from the base station. In such a scenario, the capture effect emerges at the receiver, which in turn enhances the protocol performance. We analyze the protocol behavior using a new density evolution which is based on dividing nodes into classes with different powers. By computing the probability to decode a packet in the presence of the interference, we explore the achievable throughput and its associated gain and show the excellent performance of Multi-Power IRSA.

This paper introduces a new formal mathematical problem initially motivated by an application of Network Coding (NC) to Information Centric Networks (ICN). It is of more limited scope but is remotely inspired by the well-known index coding problem. It is presented as follows: "given a vector space, can one construct several subsets of vectors, such that when drawing arbitrarily one vector from each subset, the selected vectors would be always linearly independent?". Answering this question is a step to construct an ICN efficient scheme with NC. We prove that our previously introduced construction is the only possible solution for a large family of constructions. This is an important result by itself. It also implies that any alternate solutions are outside this family and we propose one example.

Nowadays, many telecommunication systems (wifi, cable systems and 4G, 5G cellular networks) use Orthogonal Frequency Division Multiplexing (OFDM) as the physical layer standard. The design of efficient OFDM signal detection algorithms is very important to provide reliable systems, and this is particularly true for Vehicular Adhoc Networks (VANETs) involving autonomous vehicles, where missing a signal or detecting a fake one may cause a dangerous situation. The performance of these algorithms is generally evaluated in terms of their robustness against noise. In this paper, we evaluate the probability of error in signal detection in order to establish the minimum length of preamble needed for the active signaling process. This mechanism is used in AS-DTMAC (active signaling fully distributed TDMA-based MAC protocol) to reduce access collisions. Thus, by reducing the length of the preamble, greater time is given for the payload part of the packet, resulting in increased throughput.

For a seamless deployment of the Internet of Things (IoT), self- managing solutions are needed to overcome the challenges of IoT, including massively dense networks and careful management of constrained resources in terms of calculation, memory, and battery. Leveraging on artificial intelligence will enable IoT devices to operate autonomously by using inherently distributed learning techniques. Fully distributed resource management will free devices from draining their limited energy by constantly communicating with a centralized controller. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via ALOHA-type access and spread spectrum technology. Concurrent transmissions on different spreading factors increase the network capacity. However, the bottleneck is inevitable with the expected massive deployment of LoRa devices. To address this issue, we resort to the popular EXP3 (Exponential Weights for Exploration and Exploitation) algorithm to steer autonomously the decision of LoRa devices towards the least solicited spreading factors. Furthermore, the spreading factor selection is cast as a proportional fair optimization problem used as a benchmark for the learning-based algorithm. Extensive simulations were run in a realistic environment taking into account physical phenomena in LoRaWAN such as the capture effect and inter- spreading factor collision, as well as non- uniform device distribution. In such a realistic setting, we evaluate the performances of the EXP3.S algorithm, an efficient variant of the EXP3 algorithm, and show its relevance against the fair centralized solution and basic heuristics.

This paper considers the problem of data collection in a sensor network using network coding (NC). It proposes a decoding approach, called HAND, which does not require source packets to be supplemented with NC headers (with encoding vectors), classically used to decode the network-coded packets. HAND exploits the structure imposed by the communication protocol on the packet headers to estimate the original source packets from the received network-coded packets. Network-decoded packets are obtained as the solution of systems of linear equations. The decoding complexity is only one order of magnitude larger than that of classical network decoding.

In the future, IoT devices will be part of the robotics ecosystem, and the border between IoT and robotics will blur. Already today, we observe converging trends between low-end IoT devices and minibots (i.e. tiny, cheap robots) concerning their hardware, and open source software. In this paper, we explore the potential of programming minibots with the open source robotics software framework ROS2, running on top of the IoT operating system RIOT; we call the fruitful association of both elements RIOT-ROS2. In this article, the emphasis is particularly on the networking layer: using an information-centric networking (ICN) paradigm, we design and implement the communication primitives for RIOT-ROS2. We further evaluate the performance of our design on prototype minibots based on cheap, off-the-shelf hardware elements. We show that RIOT-ROS2 fits on low-end robotics hardware such as a System-on-Chip costing under $2, based on an ARM Cortex-M0+ microcontroller. Our experiments also show that the latency incurred with our information-centric approach is acceptable for minibot control, even on a low-throughput IEEE 802.15.4 radio.

Motivated by scenario requirements for 5G cellular networks, we study one of the candidate protocols for massive random access: the family of random access methods known as Coded Slotted ALOHA (CSA). A recent trend in research has explored aspects of such methods in various contexts, but one aspect has not been fully taken into account: the impact of path loss, which is a major design constraint in long-range wireless networks. In this article, we explore the behavior of CSA, by focusing on the path loss component correlated to the distance to the base station. Path loss provides opportunities for capture, improving the performance of CSA. We revise methods for estimating CSA behavior, provide bounds of performance, and then, focusing on the achievable throughput, we extensively explore the key parameters, and their associated gain (experimentally). Our results shed light on the behavior of the optimal distribution of repetitions in actual wireless networks.

Wireless Sensor Networks (WSNs) become one of the most common technologies that can be deployed in various domains. However, the networks suffer from many problems caused by the limited sensor resources and the harsh environments where these networks are deployed. Many algorithms have been proposed to manage data routing while respecting the specificity of such networks. One of the known proposed routing protocols is RPL (IPv6 Routing Protocol for Low power and Lossy networks). Nevertheless this protocol is designed with consideration of the limited sensor energy, it cannot be integrated in may WSN applications. In fact, RPL, as it has been proposed, assume that the sensor nodes are static and don't manage any type of mobility. In this paper, we propose a new approach, called BMP-RPL (Bayesian model Mobility Prediction RPL for wireless sensor networks), that aims to adapt native RPL to nodes' mobility scenarios. This approach is based on nodes' identification and velocity prediction as well as the estimation of the link duration. Thus, we introduce a new metric which constructs routes according to the node status and within information lost. The performance of our approach is proved through simulation.

In this paper, we propose, design and experiment an energy efficient protocol for Wireless Sensor Networks (WSNs) named Opportunistic Duty cYcle based routing protocol for wirelesS Sensor nEtworks (ODYSSE). The main key innovation of ODYSSE is that it judiciously makes use of three mechanisms. The first one is duty cycling which consists in randomly switching on/off transceivers to save energy. The second one is opportunistic routing in which the next hop is not rigidly fixed: any node closer to the destination might become a relay. The third one, is source coding using LDPC, Low-Density Parity-Check codes. With asynchronous duty cycling as a starting point, the above techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes. ODYSSE is implemented and installed in an experimental testbed composed of 45 Arduino nodes communicating with IEEE 802.15.4 (XBee) modules deployed in the large-scale platform FIT IoT-LAB. Results show that the performance obtained is very satisfying in both following scenarios: high load (images) and light load (reporting of infrequent event).

The universal proliferation of mobiles devices, and specifically of smartphones with rich sensing capabilities, has given rise to a new fast-growing paradigm of sensing: mobile crowdsensing. Mobile crowdsensing (MCS) takes advantage of the ubiquity of the devices to process and collect information through voluntary sensing.

The demonstration presents a network coding broadcast protocol experiment running on a remote open testbed, IoT-LAB. The emphasis is on both parts, that are ideally fitting and complementary: the use of testbed IoT-LAB, and a protocol for broadcast with network coding. IoT-LAB is a very large scale testbed, remotely accessible, and includes a total of 2728 nodes (in 6 sites), the nodes are mostly of type "wireless sensor nodes" with one wireless radio transceiver, and are well suited to perform wireless protocol experiments. On the other hand, network coding is a technique perfectly fitted to multi-hop wireless networks with lossy links, in some cases and conditions, it may even outperform any scheme using routing (non-coding): we had designed a generic broadcast protocol, called DRAGONCAST based on network coding for such networks, and which minimizes the assumptions made of the networks. A variant of this protocol was run on IoT-LAB: some results were presented previously. The demonstration is a live demonstration of the protocol on the newer nodes of IoT-LAB.

In this article, we study the issue of delay optimization and energy efficiency in grid wireless sensor networks (WSNs). We focus on STDMA (Spatial reuse - Time Division Multiple Access) scheduling, where a predefined cycle is repeated, and where each node has fixed transmission opportunities during specific slots of the cycle (defined by colors). We assume a STDMA algorithm that takes advantage of the regularity of grid topology to also provide a spatially periodic coloring ("tiling" of the same color pattern). In this setting, the key challenges are: 1) minimizing the average routing delay by ordering the slots in the cycle 2) being energy efficient. The solution we propose is called ORCHID. It proceeds in two steps. In the first step, ORCHID starts from a colored grid and builds a hierarchical routing based on these colors. In the second step, ORCHID builds a color ordering by considering jointly both routing and scheduling so as to ensure that any node will reach a sink in a single STDMA cycle. Simulation results show the excellent performance of ORCHID in terms of delays and energy compared to shortest-delay path routing.

Graph coloring is used in wireless ad hoc and sensor networks to optimize network resources: bandwidth and energy. Nodes access the medium according to their color. It is the responsibility of the coloring algorithm to ensure that interfering nodes do not have the same color. In this paper, we focus on wireless ad hoc and sensor networks with grid topologies. How does a coloring algorithm take advantage of the regularity of grid topology to provide an optimal periodic coloring, that is a coloring with the minimum number of colors? We propose the Vector-Based Coloring Method, denoted VCM, a new method that is able to provide an optimal periodic coloring for any radio transmission range and for any h-hop coloring, he1. In h-hop coloring, no nodes that are p-hop away, with 1 d p d h use the same color. This method consists in determining where a color can be reproduced in the grid without creating interferences while minimizing the number of colors used. We compare the number of colors provided by VCM with the number of colors obtained by a distributed coloring algorithm with line and column priority assignments. Finally, we discuss the applicability of this method to a real wireless network.

In this article, we describe experiments with the multicast protocol MOST (Multicast Overlay minimum Spanning Tree) on a real testbed. The testbed is a wireless multi-hop network with nodes running a MANET protocol, the OLSRv2 routing protocol, and with IEEE 802.11g hardware. We present our specification and implementation of MOST for OLSRv2. Its main feature is to build an (unicast) overlay tree covering members of a group. Through different scenarios and applications such as voice and video, we evaluate MOST performance in term of average packet delivery ratio, jitter and pay a particular attention to the MAC layer statistics. We also evaluate qualitatively and quantitatively the improvements brought by MOST compared with MPR flooding (an optimization of a pure flooding proposed and used in OLSR).

In this paper, we investigate structural properties of multicast trees that give rise to the so-called multicast power law. The law asserts that the ratio R(n) of the average number of links in a multicast tree connecting the source to n destinations to the average number of links in a unicast path, satisfies asymptotically R(n)/spl ap/cn/sup /spl phi//, 0</spl phi/<1. In order to obtain a better insight, we first analyze some simple multicast tree topologies, which under appropriately chosen parameters give rise to the multicast power law. The asymptotic analysis of R(n) in this case indicates that it is very difficult to infer the validity of power law by observing graphs of R(n) alone. Next we introduce a new metric, "reachability degree," which is easy to measure and applicable to general networks where multicast trees are constructed as subtrees of a given spanning tree which we call Global Multicast Tree. The reachability degree is indicative of the structure of the Global Multicast Tree. We show that this metric provides a more reliable means for inferring the validity of the power law. Finally, we perform experiments on real and simulated networks to demonstrate the use of the new metric.

Mobile ad hoc networks (MANETs) are infrastructure-free, highly dynamic wireless networks, where central administration or configuration by the user is very difficult. One of the MANET protocols which have been recently promoted to experimental RFC is the OLSR routing protocol (Jacquet et al., 2003; Jacquet et al., 2001), on which this article focuses. This article aims at complementing the OLSR routing protocol specifications to handle autoconfiguration. The corner stone of this autoconfiguration protocol is an advanced duplicate address detection algorithm.

This paper proposes a simple but effective admission control algorithm for integrated services packet networks. The admission control scheme, based on stochastic control, aims at ensuring user discrimination, by enforcing different call blocking probabilities. The queueing behavior of reservations is analytically characterized in the absence of admission control. From this, call blocking probabilities and analytical estimates of the performance of admission control are derived, allowing proper tuning of the algorithm. Simulations illustrate the effectiveness of the algorithm.

We present different aspects of quality of service that should be adapted to the BRAIN architecture. Several parameters and policies of QoS are depicted. Also, the paper shows the dynamic adaptation of these parameters in the context of BRAIN.