With the rapid advancement of cloud storage and the increasing use of connected devices, uploading data to the cloud results in the loss of physical control by data owners, making confidentiality and integrity entirely dependent on Cloud Service Providers (CSPs). This raises concerns about whether CSPs effectively safeguard outsourced data, as any malicious behavior can lead to data tampering or deviation. Traditional auditing schemes rely on Third Party Authorities (TPAs), which are not always trustworthy. Although various cloud data auditing mechanisms have been proposed, few effectively address the challenge of ensuring data integrity without relying on trusted third parties. To overcome this limitation, we propose a secure and efficient distributed blockchain-based data integrity auditing scheme. Specifically, our approach randomly assigns the audit task to a user selected from among the system participants via blockchain. Blockchain and InterPlanetary File System (IPFS) technologies are leveraged to enforce access control. Furthermore, the proposed scheme supports low-cost batch integrity verification without the need for a TPA. Theoretical analyses confirm that our solution ensures data traceability and auditability, and reduces reliance on third parties. Finally, our simulations show that proof generation remains under 0.7 seconds for 600 data blocks at 256-bit security, while verification costs remain negligible.

SRAM Physically Unclonable Functions (PUFs) derive secret keys from start-up values for inherent security benefits but suffer from reliability issues due to bit flipping. We introduce the Threshold-based Majority Voting Scheme (TMVS), a lightweight method that eliminates noise and mitigates bias in SRAM PUFs while retaining the simplicity of majority voting decoders used by repetition codes, without the significant entropy loss that repetition codes incur under biased responses. TMVS runs entirely in software, requires no cell-level bit-error rate qualification or SRAM redesign, and avoids the complex decoders of heavy error correcting codes. We derive closed-form expressions for decoding-error probability and expected memory, validate them on experimental data, and present a security analysis that provides exact formulas for min-entropy and secrecy leakage due to helper data and bias, identifying conditions under which TMVS achieves zero secrecy leakage. On a large public dataset, TMVS shows near-zero cross-chip secrecy leakage and preserves average conditional min-entropy above 1 bit despite biased, spatially correlated SRAM statistics. Compared with prior work, TMVS offers the smallest decoding complexity at the cost of a larger PUF size. In a representative configuration, TMVS generates a 128-bit key with failure probability 9.15 · 10−6and zero secrecy leakage at a bit-flip probability of 10%, requiring only ∼ 248k clock cycles on a 32-bit ARM Cortex-M0. These results show that TMVS is practical and implementation-friendly for resource-constrained, low-power devices.

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.

Cache Pollution Attacks (CPA) are a growing concern in Named Data Networking (NDN) due to their potential to disrupt network services and compromise data integrity. While several defence mechanisms have been developed, they often struggle to keep up with the evolving nature of such attacks. This paper introduces a cutting-edge approach for detecting and mitigating CPA in NDN, utilizing Deep Reinforcement Learning (DRL). By employing a DRL framework, we leverage the power of deep neural networks to learn complex patterns within network traffic. Our DRL algorithm is designed to analyze the intricate dynamics of NDN environments and make informed decisions about cache management to protect against CPA. The agent’s learning process involves continuous interaction with the network, allowing it to adapt to CPA attack vectors and evolving NDN network conditions. The DRL-based mitigation mechanism is evaluated using the official NDNSim simulation environment. The results show that the DRL agent effectively identifies and mitigates CPA with high accuracy, thereby improving the Cache Hit Ratio, while incurring an acceptable increase in memory usage.

A method to improve dynamic service provision is presented, catering to variable future demands while minimizing energy consumption and waiting times, maximizing customer satisfaction. The approach involves dynamic service dispatch reconfiguration at predefined intervals, addressing optimal solutions through iterative single-variable resolution. We propose a pseudo-optimal minimization problem approximating the optimal solution without requiring demand statistics, offering a simple expression. The research establishes effectiveness via a mathematical model and practical implementation using a Google-provided dataset, illustrating real-world applicability.

Vehicular Ad-Hoc Networks (VANETs) encounter various security threats, such as greedy behaviour attacks, with most existing research focusing on the CSMA/CD protocol. This paper investigates the TDMA protocol, specifically Distributed Time Division Multiple Access (DTMAC). In this paper, we focused on identifying and addressing four novel types of greedy actions that attackers can take advantage of, revealing vulnerabilities that have not been investigated before. To detect these behaviours, we propose a watchdog model designed to analyse network traffic, extract relevant features, and generate datasets at varying levels of network density. We use Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel to identify attackers in the network, employing Grid Search Cross-Validation (GSCV) for optimal results. The effectiveness of our proposed solution is evaluated through in-depth simulations using the NS2 simulator and Python. The results show that the proposed detection method can achieve a high detection rate with an accuracy attaining 95% in low density scenario and 80 % in high density scenario.

Summary Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field.

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.

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.

Vehicular Ad hoc Networks (VANETs) offer a promising approach to enhancing road safety. Cooperative Awareness Messages (CAM) is an essential service in VANETs, allowing vehicles to transmit radio beacons containing their positions and velocities. These messages inform nearby vehicles about the traffic situation. This paper focuses on Extended Cooperative Awareness Messages (ECAM), which include additional information about nearby vehicles. ECAM beacons consist of a vehicle's speed, position, and data on the positions and velocities of other vehicles in its vicinity. This comprehensive information enables nearby vehicles to understand the traffic situation and take appropriate actions to prevent potential collisions. Studies demonstrate that ECAM has the potential to significantly improve road safety by providing comprehensive and up-to-date traffic information. This paper uses stochastic geometry to evaluate different versions of ECAM services and compare the results with simple simulations. The evaluation assumes random vehicle placement using a homogeneous Poisson Point Process and models the ECAM service using the Matern Point Process.

In this paper we propose an alternative to the security protocols generally developed for VANETs (Vehicular Ad-hoc NETworks), which often rely on asymmetric keys and PKIs (Public Key Infrastructures). Instead, we propose a solution whose architecture is based on Physical Unclonable Functions (PUFs) inside the vehicles. We design a protocol with these PUF functions to securely spread a secret key by means of a Road Side Unit (RSU). This secret key will be used by the vehicles to warn neigboring vehicles of an emergency situation. The key is regularly changed by the RSU but its value evolves slowly over time in a random way. Thus a message using an old (but not too old) key can be accepted as valid. We define the whole protocol where a vehicle first identifies itself to the Road Side Unit for authentication. Then the RSU sends back a key which will be used to send alerts if an emergency situation occurs on the road. The RSU manages the evolution of this key by just randomly changing one bit at each change. Thus an alert can be accepted if the key used to issue an alert is "close" to the last key issued by the RSU. We investigate the advantages of this new scheme and evaluate the implications in terms of exchanges between the vehicles (and their PUFs) and the network connecting the RSUs.

The autonomous platoon is today one of the key tools for better road utilization. In fact, by optimizing the distance between vehicles, the air drag is reduced, and researchers have shown that 20% of energy can be saved by using this concept. From a network point of view, reducing the distance between vehicles will allow a new point-to-point communication link between the vehicles in front and behind by using Vehicular Visible Light Communication (V-VLC), thus providing an opportunity to have a hybrid communication. Our new radio design based on the AS-DTMAC protocol guarantees a high Quality of Service for real-time applications. However, with a very high density, we can reach the bandwidth dedicated to V2X radio. In the case of a platoon, this scenario can cause dangerous platoon instability. Assisting the radio with another communication vector such as V-VLC can help to maintain the high level of reliability that is necessary for the control of a platoon. In this paper, we first carry out an analytical analysis to investigate the capacity of our new radio technology to support the platoon control use case in terms of the quality of service (QoS) required for this type of application. Secondly, we show through extensive simulations the current level of VVLC technology, compared to radio technology, in terms of packet loss and delay.

Vehicular Ad hoc Networks (VANETs), while promising new approaches to improving road safety, must be protected from a variety of threats. Greedy behavior attacks at the level of the Medium Access (MAC) Layer can have devastating effects on the performance of a VANET. This kind of attack has been extensively studied in contention-based MAC protocols. Hence, in this work, we focus on studying the impact of such an attack on a contention-free MAC protocol called Distributed TDMA-based MAC Protocol DTMAC. We identify new vulnerabilities related to the MAC slot scheduling process that can affect the slot reservation process on the DTMAC protocol and we use simulations to evaluate their impact on network performance. Exploitation of these vulnerabilities would result in a severe waste of channel capacity where up to a third of the free slots could not be reserved in the presence of an attacker. Moreover, multiple attackers could cripple the channel and none could acquire a time slot.

Indoor Localization has attracted interest in both academia and industry for its wide range of applications. In this paper, we propose an indoor localization solution based on Channel State Information (CSI). CSI is a fine-grain measure of the effect of the channel on the transmitted signal. It is computed for each subcarrier and each antenna in the Multiple-Input-Multiple-Output (MIMO) antenna case. It is also becoming a trend for indoor position fingerprinting. By using a K-nearest neighbor learning method a highly accurate indoor positioning is achieved. The input feature is the magnitude component of CSI which is preprocessed to reduce noise and allow for a quicker search. The euclidean distance between CSI is the criteria chosen for measuring the closeness between samples. The method is applied to a CSI dataset estimated at an 8 × 2 MIMO antenna that is published by the organizers of the Communication Theory Workshop Indoor Positioning Competition. The proposed method is compared with three other methods all based on deep learning approaches and tested with the same dataset. The K-nearest neighbor method presented in this paper achieves a Mean Square Error (MSE) of 2.4 cm which outperforms its counterparts.

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.

In VANET networks using TDMA access it is possible to approach the full capacity of the channel when the density of vehicles increases. When this occurs, some vehicles will be unable to communicate with the rest of the network. The use of Visible Light Communication (VLC) for certain vehicles can reduce the load on the radio channel and allow all vehicles to communicate on the network. The idea of our approach is to group vehicles with VLC capacity in platoons where only the lead vehicle would use radio transmission while the other vehicles in the platoon would communicate via the VLC system. We show that if the vehicles are randomly equipped with VLC transmission capabilities (with a probability p), the gain in bandwidth is significant even without rearranging the order of the vehicles. In this paper we estimate the performance gains obtained by using VLC in terms of bandwidth and probability of collision during an initial access.

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.

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.

In this work, we use the energy consumed by one bit of information per meter toward the destination as a local metric to be minimized in channels affected by shadow fading. Given a fixed amount of energy available for transmitting information, the proposed strategy consists in maximizing the amount of information delivered within a given time interval by optimizing both the transmission rate and power.

Due to the emerging demand for Internet of Things (IoT) applications, indoor positioning has become an invaluable task. We propose NDR, a novel lightweight deep learning solution to the indoor positioning problem. NDR is based on Noise and Dimensionality Reduction of Channel State Information (CSI) of a Multiple-Input Multiple-Output (MIMO) antenna. Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP's hyperparameters are empirically tuned to achieve the highest accuracy. NDR is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna used to generate the dataset. NDR yields a mean error 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm.

Enabling Named Data Networking (NDN) in realworld Internet of Things (IoT) deployments becomes essential to benefit from Information Centric Networking (ICN) features in current IoT systems. To design realistic NDN-based communication solutions for IoT, revisiting mainstream technologies such as low-power wireless standards may be the key. In this paper, we explore the NDN forwarding over IEEE 802.15.4 by modeling a broadcast-based forwarding strategy. Based on the observations, we adapt the Carrier-Sense Multiple Access (CSMA) algorithm of 802.15.4 to improve NDN wireless forwarding while reducing broadcast effects in terms of packet redundancy, round-trip time and energy consumption.

In Vehicular Ad hoc NETworks (VANETs) the vehicles moving along roads communicate with each other through ad hoc wireless devices. VANETs have attracted a great deal of attention in the research community in recent years, with the main focus being on their support of safety applications. Time Division Multiple Access (TDMA)- based protocols are advantageous in many aspects of VANETs. They can cope with the hidden- terminal problem, and guarantee a strict Quality- ofService (QoS) to satisfy real-time applications. However, the initial assignment of time-slots to the vehicles can suffer from the access collision problem, which can frequently occur between vehicles trying to access the same time slots. Moreover, a low latency access is not usually possible. That is why we have developed an Active Signaling system (AS-DTMAC : Active Signaling Decentralized Tdma MAC protocol) which operates above the existing DTMAC protocol : a Medium Access Control (MAC) protocol specially devoted to VANETs. AS-DTMAC can drastically reduce the number of access collisions and also offer low latency access. The aim of this article is to provide a complete mathematical analysis of the performance of this scheme, to show its high performances and to validate these results using simulations.

In this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a successful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.

In this paper, we focus on vehicular safety applications based on the Dedicated Short Range Communication (DSRC) standard. We propose a new mechanism to alleviate channel congestion by reducing the beacons load while maintaining an accurate awareness level. Our scheme is based on the collective perception concept which consists in sharing perceived status information collected by vehicles equipped with different types of sensors (radars, lidars, cameras, etc.). To achieve our goal, we propose two main schemes. The first one consists in implementing the collective perception capability on vehicles and adding a new category of status messages to share locally collected sensor data in order to reduce channels load and enhance vehicles' awareness. The second scheme concerns the accuracy level of the received information from the collective perception enabled vehicles by fixing a prior error threshold on the position. The method proposed is validated by simulations and the results obtained are compared to those of an application based on the traditional beaconing scheme of the IEEE802.11p standard. The simulations show that the proposed scheme is able to significantly reduce the load on the control channel incurred by the beacons and the packet error ratio for different network densities and built-in sensors characteristics.

The recent advances in wireless sensors and Unmanned Aerial Vehicles have created new opportunities for environmental control and low cost aerial data gathering. In this paper, we propose to use an Unmanned Aerial Vehicle (UAV) for data gathering. Basically, we have proposed a method for UAV path planning based on virtual forces and potential fields. In addition, and more importantly, we present a new approach to compute the attractive forces of the potential field.

The high densities of network nodes has made spatial reuse an essential characteristic of modern wireless networks. In this paper, we evaluate the maximum throughput of Carrier Sense Multiple Access (CSMA) for Vehicular Ad-hoc Networks (VANETs) when spatial reuse is taken into account. We begin our study by extending a simple stochastic model in order to fit a VANET pattern and to obtain the spatial density of throughput in terms of the main network parameters. This model uses a Matern selection process with a random pattern of nodes distributed as a Poisson Point Process (PPP). Each node of the process receives a random mark and the nodes that have the smallest mark in their neighborhood are elected for transmission. We study both 1D and 2D network cases with an SIR (Signal over Interference Ratio) model. In order to verify the correctness of the model, extensive simulations are carried out using two simulation platforms: the network simulator, ns-3, and a simulator which is dedicated to CSMA systems. Fairly good matching between the results of the model and those obtained from simulators are observed, confirming the reliability of the theoretical model. Although the results did not perfectly match due to the number of assumptions made for the model, the results obtained nonetheless show the potential for a significant improvement in the overall throughput for VANETs and similar distributed networks.

Vehicular Ad hoc NETworks, known as VANETs, are deployed to reduce the risk of road accidents as well as to improve passenger comfort by allowing vehicles to exchange different kinds of data, both between the vehicles themselves and potentially between the vehicles and the infrastructure. One of the major issues in VANETs is the need to improve safety information delivery over long distances. Hence, VANETs require efficient and stable routing protocols that can allow the safety information to be disseminated in a timely manner. We recently proposed TRPM, a TDMA-aware routing protocol for multi-hop communication based on a cross layer approach between the Medium Access Control (MAC) and the routing layers, in which the intermediate vehicles are selected according to their geographic position and the position of their time-slot in the TDMA scheduling frame. The main purpose of this paper is to analyze the efficiency of the TRPM protocol. To do so an analytical model is presented in which expressions are derived to calculate two performance metrics: the delivery delay and packet loss rate. In order to validate the mathematical model and the protocol, a comparison between simulation and analytical results is presented using the network simulator ns-2 and the realistic road traffic simulator MOVE/SUMO.

In this paper we propose an analysis of a slotted based protocol designed for devices of the Internet of Thing (IoT). In contrast to other TDMA-based protocols this scheme uses a random technique to access shared slots which presents similarities with CSMA protocols. In practice the transmissions are scheduled in a given back-off window of slots whose duration allows the transmission of a packet and its acknowledgment. Therefore this protocol can be analyzed according to the methodology introduced by Bianchi for the IEEE 802.11 protocol even if the protocol studied differs in many aspects. The model we use is also particular because we succeed in obtaining a Markov model even if the scheme used to send a packet (in a node) may depend on the transmission of the previous packet. We distinguish two protocols; in the first one, at the initial stage or after a successful transmission, the packets are transmitted without any back-off, whereas in the second protocol each transmission is always preceded by the count down of a random back-off. Extensive simulations validate the models of both protocols and a comparative performance evaluation is carried out.

Maximizing the throughput of point-to-point communication has been the crux of wireless networks. In IEEE 802.11 networks, the first and prominent wireless technology, the model of point-to-point communication is still applicable today: the transmissions are between the wireless nodes and the access point, which usually serves as a gateway to the Internet. But this model is not well suited to more recent wireless systems such as Wireless Sensor Networks (WSNs) and Vehicular Ad Hoc NETworks (VANETs). In such networks, a very significant part of communication is between one node and its neighbors and simultaneous transmissions or, in other words spatial reuse, is required to insure good performance. When we consider communication from one node to its neighbor, an important metric is the density of successful simultaneous transmissions. Several studies such as [1], [2] have shown how this density of transmissions can be improved in Aloha or in CSMA networks. The aim of this paper is to show that the use of directional antennas can greatly improve the performance of the network in our neighbor-to-neighbor communication model because interference is greatly reduced. The model we build here allows a quantitative study of the performance and the improvement obtained with directional antennas to be be achieved. The study of Aloha (slotted and non-slotted) is very easy to accomplish and leads to closed formulas for the density of successful transmissions. The study of CSMA is more complex. We use a Matern selection process to mimic the behavior of CSMA in a random pattern of nodes distributed as a Poisson Point Process (PPP): each node receives a random mark and the nodes that have the smallest mark in their neighborhood are elected for transmission. Previous studies, such as [2], show that in CSMA networks, the density of successful transmissions is greatly influenced by the carrier sense detection threshold, which is one of the main parameters of CSMA. In this study we will assume that the carrier sense detection threshold is optimized to obtain the best performance of the CSMA network and our evaluations are performed under this condition. Our analytical models and our computation show that using directional antennas can lead to an improvement of up to more than 100% in the density of throughput compared to the normal use of unidirectional antennas.

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.

Vehicular Ad-Hoc Networks (VANETs) have become an emerging technology due to the variety of their applications in Intelligent Transportation Systems (ITS). By creating a vehicular network, each vehicle can exchange information to inform drivers in other vehicles about the current status of the traffic flow or a dangerous situation. Multi-hop communications is an effective method that can be used for information exchange over distances greater than the transmission range of the transmitting vehicle. However, it is a great challenge to ensure a stable multi-hop communication link with a low delivery delay due to the high mobility of the vehicles involved. The goal of this paper is to design a TDMA aware Routing Protocol for Multi-hop wireless vehicular ad hoc networks (TRPM) in order to provide the ability to transmit/receive packets over long distances. The proposed routing scheme is based on a medium access control protocol, in which the intermediate vehicles are selected based on the TDMA scheduling. The simulation results reveal that our routing protocol significantly outperforms other protocols in terms of average end-to-end delay, average number of relay vehicles and the average delivery ratio.

Results are presented for optimizing device-to-device communications in cellular networks, while maintaining spectral efficiency of the base-station-to-device downlink channel. We build upon established and tested stochastic geometry models of signal-to-interference ratio in wireless networks based on the Poisson point process, which incorporate random propagation effects such as fading and shadowing. A key result is a simple formula, allowing one to optimize the device-to-device spatial throughput by suitably adjusting the proportion of active devices. These results can lead to further investigation as they can be immediately applied to more sophisticated models such as studying multi-tier network models to address coverage in closed access networks.

In modern wireless networks especially in Machine-to-Machine (M2M) systems and in the Internet of Things (IoT) there is a high densities of users and spatial reuse has become an absolute necessity for telecommunication entities. This paper studies the maximum throughput of Carrier Sense Multiple Access (CSMA) in scenarios with spatial reuse. Instead of running extensive simulation with complex tools which would be somewhat time consuming, we evaluate the spatial throughput of a CSMA network using a simple model which produces closed formulas and give nearly instantaneous values. This simple model allows us to optimize the network easily and study the influence of the main network parameters. The nodes will be deployed as a Poisson Point Process (PPP) of a one or two dimensional space. To model the effect of (CSMA), we give random marks to our nodes and to elect transmitting nodes in the PPP we choose those with the smallest marks in their neighborhood. To describe the signal propagation, we use a signal with power-law decay and we add a random Rayleigh fading. To decide whether or not a transmission is successful, we adopt the Signal-over-Interference Ratio (SIR) model in which a packet is correctly received if its transmission power divided by the interference power is above a capture threshold. We assume that each node in our PPP has a random receiver at a typical distance from the transmitter i.e. the average distance between a node and its closest neighbor. We also assume that all the network nodes always have a pending packet. With all these assumptions, we analytically study the density of throughput of successful transmissions and we show that it can be optimized with regard to the carrier-sense threshold.

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).

A Vehicular Ad-Hoc NETwork (VANET) consists of a set of vehicles moving along roads, which can communicate with each other through ad hoc wireless devices. VANETs have attracted a great deal of attention in the research community in recent years, with the main focus being on their safety applications. One of the major challenges of vehicular networks is designing an efficient Medium Access Control (MAC) protocol which can cope with the hidden node problem, the high speed of the nodes, the frequent changes in topology, the lack of an infrastructure, and various QoS requirements. Motivated by this observation, we present a fully distributed and location-based TDMA scheduling scheme for VANETs, named DTMAC. The main goal of this work is to propose a MAC protocol that can provide a reliable broadcast service with bounded access delay, while reducing access collisions and merging collisions with various vehicle densities without having to use expensive and complex spectrum mechanisms such as CDMA or OFDMA. An analytical model of the average access collision probability has been derived, which can be used to evaluate the performance of DTMAC and validate the simulation results under different traffic conditions. The simulation results reveal that DTMAC significantly outperforms VeMAC in terms of transmission collisions and broadcast coverage.

This article describes a stateless authentication protocol designed for sensor networks. A mutual authentication between a sensor and a sink can be useful in many applications such as the monitoring of electricity meters or surveillance and monitoring of industrial plants. The authentication protocol we propose can counter usual attacks on sensor networks. First, as it is based on a PUF function, it is efficient against physical node capture. An attacker can not get into the hardware of the sensor or the sink node to obtain the secret keys of the system even if the node is captured physically. Secondly, this protocol can also counter replay attacks since the authentication uses a time-stamp, and the time interval during which a replay attack could be launched can be controlled and greatly reduced. Thirdly, this protocol is stateless, and so the sink can be authenticated to many co-located sensors using a single authentication message. Moreover, the same message can combine the authentication with the transmission of encrypted or unencrypted data.

Vehicular Ad hoc NETworks (VANETs) are a major component recently used in the development of Intelligent Transportation Systems (ITSs). VANETs have a highly dynamic and portioned network topology due to the constant and rapid movement of vehicles. Currently, clustering algorithms are widely used as the control schemes to make VANET topology less dynamic for Medium Access Control (MAC), routing and security protocols. An efficient clustering algorithm must take into account all the necessary information related to node mobility. In this paper, we propose an Adaptive Weighted Clustering Protocol (AWCP), specially designed for vehicular networks, which takes the highway ID, direction of vehicles, position, speed and the number of neighboring vehicles into account in order to enhance the stability of the network topology. However, the multiple control parameters of our AWCP, make parameter tuning a nontrivial problem. In order to optimize the protocol, we define a multi-objective problem whose inputs are the AWCP's parameters and whose objectives are: providing stable cluster structures, maximizing data delivery rate, and reducing the clustering overhead. We address this multi-objective problem with the Non-dominated Sorted Genetic Algorithm version 2 (NSGA-II). We evaluate and compare its performance with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Differential Evolution (MODE). The experiments reveal that NSGA-II improves the results of MOPSO and MODE in terms of spacing, spread, ratio of non-dominated solutions, and inverse generational distance, which are the performance metrics used for comparison.

Vehicular ad hoc networks (VANETs) where vehicles act as mobile nodes is an instance of Mobile Ad hoc NET-works (MANETs), which are essentially developed for intelligent transportation systems. A challenging problem when designing communication protocols in VANETs is coping with high vehicle mobility, which causes frequent changes in the network topology and leads to frequent breaks in communication. The clustering technique is being developed to reduce the impact of mobility between neighboring vehicles. In this paper, we propose an Adaptive Weighted Cluster Protocol for VANETs, which is a road map dependent and uses road IDs and movement direction in order to make the clusters structure as stable as possible. The experimental results reveal that AWCP outperforms four other most commonly used clustering protocols in terms of control packet overhead, the packet delivery ratio, and the average cluster lifetime, which are the most usual metrics used for comparing performance.

In our basic model, we study a stationary Poisson pattern of nodes on a line embedded in an independent planar Poisson field of interfering nodes. Assuming slotted Aloha and the signal-to-interference-and-noise ratio capture condition, with the usual power-law path loss model and Rayleigh fading, we explicitly evaluate several local and end-to-end performance characteristics related to the nearest-neighbor packet relaying on this line, and study their dependence on the model parameters (the density of relaying and interfering nodes, Aloha tuning and the external noise power). Our model can be applied in two cases. The first use is for vehicular ad-hoc networks, where vehicles are randomly located on a straight road. The second use is to study a “typical” route traced in a (general) planar ad-hoc network by some routing mechanism. The approach we have chosen allows us to quantify the non-efficiency of long-distance routing in “pure ad-hoc” networks and evaluate a possible remedy for it in the form of additional “fixed” relaying nodes, called road-side units in a vehicular network. It also allows us to consider a more general field of interfering nodes and study the impact of the clustering of its nodes on the routing performance. As a special case of a field with more clustering than the Poison field, we consider a Poisson-line field of interfering nodes, in which all the nodes are randomly located on random straight lines. In this case, our analysis rigorously (in the sense of Palm theory) corresponds to the typical route of this network. The comparison to our basic model reveals a paradox: clustering of interfering nodes decreases the outage probability of a single (typical) transmission on the route, but increases the mean end-to-end delay.

The objective of this study is to analyze a new disruptive deployment of wireless sensors in order to cope with the explosive demand for bandwidth while taking into account energy consumption considerations. The work is grounded on the idea of massive network densification by drastically increasing the number of sensors in a given area in a Time Division Duplex (TDD) mode. Using ideas from the recent Massive MIMO technology (more than 400 antennas, without any modification of the network infrastructure), we transpose the idea to a massive deployment of sensors and show the benefits of such an infrastructure. This research is expected to provide the optimal deployment of massive wireless sensor networks in terms of cost/performance/complexity/energy efficiency trade-off and define the next generation wireless Machine-to-Machine (M2M) communication.

In this paper we propose an optimized broadcasting mechanism which uses very limited signaling overhead. The main objective is to select the most appropriate relay nodes according to a given cost function. Basically, after receiving a broadcast packet each potential relay node computes a binary code according to a given cost function. Then, each node starts a sequence of transmit/listen intervals following this code. In other words, each 0 corresponds to a listening interval and each 1 to a transmit interval. During this active acknowledgment signaling period, each receiver applies the following rule: if it detects a signal during any of its listening intervals, it quits the selection process, since a better relay has also captured the packet. Finally, we split the transmission range into several sectors and we propose that all the nodes within the same sector use the same CDMA orthogonal spreading codes to transmit their signals. The CDMA codes used in two different sectors are orthogonal, which guarantees that the packet is broadcast in all possible directions.

We introduce a new algorithm to achieve a distributed leader election in a broadcast channel that is more efficient than the classic Part-and-Try algorithm. The algorithm has the advantage of having a reduced overhead log logN rather than log N. More importantly, the algorithm has a greatly reduced energy consumption since it requires O(N1=k) burst transmissions instead of O(N=k), per election, k being a parameter depending on the physical properties of the medium of communication. The algorithm has interesting potential applications in cognitive wireless networking.

Nowadays, with the abundance of IEEE 802.11 access points (APs), a mobile user has the flexibility to choose one of several APs, each using a separate channel. Rather than relying on the simplistic standardized algorithm to select the AP, it would be preferable to use optimal algorithms that reduce the user data transfer time. In this paper, the AP selection process is apprehended as an ordinal potential game, which is a class of non-cooperative games known to possess at least one pure Nash Equilibrium (PNE). We put forward a fully decentralized algorithm based on replicator dynamics to attain those PNE. Further, to assess the loss in efficiency of the proposed selfish distributed algorithm, we compare its performances against a centralized optimal approach derived by solving a mixed integer linear program.

In this paper we propose two analytically tractable stochastic models of non-slotted Aloha for Mobile Ad-hoc NETworks (MANETs): one model assumes a static pattern of nodes while the other assumes that the pattern of nodes varies over time. Both models feature transmitters randomly located in the Euclidean plane, according to a Poisson point process with the receivers randomly located at a fixed distance from the emitters. We concentrate on the so-called outage scenario, where a successful transmission requires a Signal-to-Interference-and-Noise Ratio (SINR) larger than a given threshold. With Rayleigh fading and the SINR averaged over the duration of the packet transmission, both models lead to closed form expressions for the probability of successful transmission. We show an excellent matching of these results with simulations. Using our models we compare the performances of non-slotted Aloha to previously studied slotted Aloha. We observe that when the path loss is not very strong both models, when appropriately optimized, exhibit similar performance. For stronger path loss non-slotted Aloha performs worse than slotted Aloha, however when the path loss exponent is equal to 4 its density of successfully received packets is still 75% of that in the slotted scheme. This is still much more than the 50% predicted by the well-known analysis where simultaneous transmissions are never successful. Moreover, in any path loss scenario, both schemes exhibit the same energy efficiency.

Vehicular Ad hoc NETworks (VANETs) aim at increasing safety on our road networks as well as bringing road users new applications and entertainment. Ad hoc networks with a linear topology appear frequently in VANETs stimulating increased interest in the study of linear ad hoc networks. Access in VANETs is usually governed by Carrier Sense Multiple Access (CSMA) techniques. Thus studying the performance of CSMA in linear ad hoc networks can be very beneficial to optimize the design of these new networks: VANETs. In this paper we analyze the performance of CSMA in linear networks. Using a simplified model for the carrier sense where only the nearest interferer is taken into account, we derive an exact model to compute the number of simultaneous transmissions in a linear VANET. We assume that the density of nodes is infinite and that all the nodes have a pending packet to transmit. We are able to extend this model to a great but finite density of nodes. For a more realistic model of CSMA where the whole interference is taken into account, we derive a lower bound for the average number of transmitters whereas the average number of transmitters with only the nearest interferer previously computed is an upper bound. We validate the results predicted by the analytical model with those obtained through simulations. We show that both approaches provide coherent results.

This paper is meant to be an illustration of the use of stochastic geometry for analyzing the performance of routing in large wireless ad hoc (mobile or mesh) networks. In classical routing strategies used in such networks, packets are transmitted on a pre-defined route that is usually obtained by a shortest-path routing protocol. In this paper we review some recent ideas concerning a new routing technique which is opportunistic in the sense that each packet at each hop on its (specific) route from an origin to a destination takes advantage of the actual pattern of nodes that captured its recent (re)transmission in order to choose the next relay. The paper focuses both on the distributed algorithms allowing such a routing technique to work and on the evaluation of the gain in performance it brings compared to classical mechanisms. On the algorithmic side, we show that it is possible to implement this opportunistic technique in such a way that the current transmitter of a given packet does not need to know its next relay a priori, but the nodes that capture this transmission (if any) perform a self-selection procedure to choose the packet relay node and acknowledge the transmitter. We also show that this routing technique works well with various medium access protocols (such as Aloha, CSMA, TDMA). Finally, we show that the above relay self-selection procedure can be optimized in the sense that it is the node that optimizes some given utility criterion (e.g. minimize the remaining distance to the final destination), which is chosen as the relay. The performance evaluation part is based on stochastic geometry and combines simulation as analytical models. The main result is that such opportunistic schemes very significantly outperform classical routing schemes when properly optimized and provided at least a small number of nodes in the network know their geographical positions exactly.

Spatial Aloha is probably the simplest medium access protocol to be used in a large mobile ad hoc network: each station tosses a coin independently of everything else and accesses the channel if it gets heads. In a network where stations are randomly and homogeneously located in the Euclidean plane, there is a way to tune the bias of the coin so as to obtain the best possible compromise between spatial reuse and per transmitter throughput. This paper shows how to address this questions using stochastic geometry and more precisely Poisson shot noise field theory. The theory that is developed is fully computational and leads to new closed form expressions for various kinds of spatial averages (like e.g. outage, throughput or transport). It also allows one to derive general scaling laws that hold for general fading assumptions. We exemplify its flexibility by analyzing a natural variant of Spatial Aloha that we call Opportunistic Aloha and that consists in replacing the coin tossing by an evaluation of the quality of the channel of each station to its receiver and a selection of the stations with good channels (e.g. fading) conditions. We show how to adapt the general machinery to this variant and how to optimize and implement it. We show that when properly tuned, Opportunistic Aloha very significantly outperforms Spatial Aloha, with e.g. a mean throughput per unit area twice higher for Rayleigh fading scenarios with typical parameters.

Classical routing strategies for mobile ad hoc networks operate in a hop by hop "push mode" basis: packets are forwarded on pre-determined relay nodes, according to previously and independently established link performance metrics (e.g., using hellos or route discovery messages). Conversely, recent research has highlighted the interest in developing opportunistic routing schemes, operating in "pull mode": the next relay can be selected dynamically for each packet and each hop, on the basis of the actual network performance. This allows each packet to take advantage of the local pattern of transmissions at any time. The objective of such opportunistic routing schemes is to minimize the end-to-end delay required to carry a packet from the source to the destination. In this paper, we provide upper bounds on the packet propagation speed for opportunistic routing, in a realistic network model where link conditions are variable. We analyze the performance of various opportunistic routing strategies and we compare them with classical routing schemes. The analysis and the simulations show that opportunistic routing performs significantly better. We also investigate the effects of mobility and of random fading. Finally, we present numerical simulations that confirm the accuracy of our bounds.

Classical routing strategies for mobile ad hoc networks forward packets on a pre-defined route (typically obtained by a shortest path routing protocol). Research has high-lighted the interest in developing opportunistic routing schemes, where the next relay is selected dynamically for each packet and each hop. This allows each packet to take advantage of the local pattern of transmissions at any time. The objective of such opportunistic routing schemes is to minimize the end-to-end delay required to carry a packet from the source to the destination. In this paper, we provide upper bounds on the packet propagation speed for opportunistic routing, in a realistic network model where link conditions are variable. We analyze the performance of various opportunistic routing strategies and we compare them with classical routing schemes. The analysis and simulations show that opportunistic routing performs significantly better. We also investigate the effects of mobility. Finally, we present numerical simulations that confirm the accuracy of our bounds.

In this paper we study the channel occupation induced by the OLSR proactive routing protocol used in a linear Vehicular Ad hoc Network (VANET). Unlike previous studies which usually use simulations to evaluate the overhead of routing protocols, we derive a simple analytical model to carry out this evaluation. Moreover, we do not evaluate the total overhead induced by the routing protocol as is usually proposed, but for a given node we compute the channel occupation induced by the routing protocol. This paper provides a quantitative approach to evaluating the cost of a proactive routing protocol in a linear vehicular ad hoc network as a function of several parameters such as the frequency of the control messages, the density of the vehicles, the propagation range of the control messages and the carrier sense area.

An Aloha-type access control mechanism for large mobile, multihop, wireless networks is defined and analyzed. This access scheme is designed for the multihop context, where it is important to find a compromise between the spatial density of communications and the range of each transmission. More precisely, the analysis aims at optimizing the product of the number of simultaneously successful transmissions per unit of space (spatial reuse) by the average range of each transmission. The optimization is obtained via an averaging over all Poisson configurations for the location of interfering mobiles, where an exact evaluation of signal over noise ratio is possible. The main mathematical tools stem from stochastic geometry and are spatial versions of the so-called additive and max shot noise processes. The resulting medium access control (MAC) protocol exhibits some interesting properties. First, it can be implemented in a decentralized way provided some local geographic information is available to the mobiles. In addition, its transport capacity is proportional to the square root of the density of mobiles which is the upper bound of Gupta and Kumar. Finally, this protocol is self-adapting to the node density and it does not require prior knowledge of this density.

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.

The collision detection (CD) is an interesting feature which provides optimal performance to radio LANs such to make the latter comparable to wired LANs (e.g. Ethernet). The European HIPERLAN 20 Mbps standard is the natural environment for this implementation, since this standard involves the most powerful physical base and the most multivalent architecture for radio LANs. We describe one possible way to detect collision in a radio LAN, based on the so-called Comb strategy which is very similar to collision detection. This technique is developed and implemented in LAURA Esprit project.

Flow control algorithms specially designed for high speed networks are introduced. They are based on a new queuing model called the La Palice queue. An algorithm that is simply an extrapolation of the classic flow control algorithm with a request to the destination and an answer to the source is presented. It is shown that the overflow occurrence is lowered to a certain probability by the application of the algorithm. An intermediate algorithm is presented that cancels overflow occurrence, but allows repetition of requests with a certain probability per multipacket message.>

In high-speed networks, the ratio of the end-to-end propagation delay to the transmission delay may exceed one. Under these conditions, neither token-passing nor conventional CSMA/CD schemes can work efficiently. Previous work on access protocols for high-speed networks is generally based on round-robin service and can only operate on unidirectional media. This paper introduces new high-speed access protocols which can work on various LAN's topologies (star, tree, unidirectional media) and also on satellite networks. Based on a tree algorithm with deferred collision detection, this protocol includes mechanisms which reduce collisions and increase throughput. The maximum channel utilization can reach 100% whatever the propagation delay and the total connected population may be.