International audience

IoT technology is gaining popularity as more affordable, intelligent, and multifunctional IoT devices are developed. The proliferation of these IoT objects has led to their adoption in several areas, including agriculture, livestock farming, health monitoring, etc. The ultimate goal of deploying IoT networks is to collect data for decision-making. Data collection is, therefore, one of the important tasks in IoT networks. The main characteristic of IoT devices is that they are energy-constraints with finite-lifetime batteries. The batteries in IoT devices are not rechargeable, rendering them unusable when they run out. Moreover, for critical applications, the replacement of faulty IoT devices is a tricky operation. Therefore, it is important to control the energy consumption of the IoT devices during data collection in the network to ensure battery longevity. In this paper, we propose the Social Network Search-Based Data Collection Protocol (SNS-DCP), which uses the Social Network Search metaheuristic to create an optimal partition with suitable partition head node selection, thereby optimizing network lifetime and maximizing data collection in IoT networks. The simulation results show that the SNS-DCP protocol outperforms the existing PSO-UFC, PSO-ECHS, and LEACH protocols in terms of network lifetime, number of alive nodes, network energy consumption, and the amount of data collected at the BS. Concisely, the SNS-DCP protocol enhances the data collection by 43%, 68%, and 87% over the PSO-UFC. PSO-ECHS. and LEACH protocols, respectively,

The 5G cellular network provides vital support for enabling fast and dependable communication in dynamic environments, which is crucial for connected autonomous vehicles. To achieve this goal, telecommunication operators must prioritize speedy and efficient radio resource management in 5G New Radio (NR) systems, achieved by dynamically adapting the configuration of the physical (PHY) layer. To address this issue, we introduce a novel method called Decision Tree Adaptive Physical Layer Configuration (DeTrAP), which utilizes machine learning and observational data to real-time fine-tune the PHY layer for efficient radio resource management. Extensive simulations demonstrate that DeTrAP achieves the expected performance for safety and non-safety traffic scenarios, while significantly reducing the convergence time.

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.

The effectiveness of WSNs depends on the data collection scheme since the design of an energy-efficient, long-time WSN has been a challenge for over a decade. To address this issue, we propose a Cluster-tree Data Collection Protocol using a hybrid meta-heuristic algorithm termed Hybrid Bio-Inspired Protocol (HBIP). The idea consists of integrating the Bacterial Foraging optimization (BFO) swarming step into the exploitation phase in the Artificial Bee Colony algorithm (ABC). Our solution efficiently builds clusters and elects the optimal Cluster Heads (CHs). The results of the simulation show that HBIP outperforms the traditional clustering protocol LEACH and meta-heuristics like ABC and BFO in terms of throughput, energy consumption, network lifetime, and data packets received at the BS.

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.

Abstract The main goal behind radio resource management (RRM), in any conventional wireless networks, is to efficiently utilize the available resources. Critical and safety communications have very stringent quality of service requirements on reliability and availability. Consequently, the efficient use of resources becomes more compelling when it deals with this type of application. In this context, cellular vehicle‐to‐everything (C‐V2X) communications, enabled by cellular device‐to‐device (D2D), have recently drawn much attention. This is due to their applicability to road safety and traffic efficiency applications, which require information exchange among road users and road‐side entities. This emerging technology, referred hereinafter as LTE‐V or C‐V2X, supports two different communication modes: 3 and 4. Both of the two modes enable direct vehicle‐to‐vehicle (V2V) communications via PC5 interface. However, they differ on how the radio resources are allocated. Several approaches and algorithms are proposed in the literature to address the resource allocation issue. In this paper, a state of the art of the radio resource allocation strategies was made for LTE‐V technology and a qualitative description was carried out of these different schemes. We proposed a classification of these strategies into different categories. This classification identified the different strengths and weaknesses of each proposal. Up to our knowledge, several surveys concerning resource allocation are published in the context of LTE but very few of them focuses on both RRM and vehicular networks.

Enabling direct Vehicle-to-Vehicle (V2V) communications within LTE-V technology is considered as a key technological breakthrough to further deploy vehicle-to-Everything (V2X) applications. As the main goal of these latter is to improve the traffic safety and save lives, any design of a resource allocation scheme under LTE-V has to take into account the stringent requirements of these applications in terms of reliability and latency. In this context, this paper proposes a novel Priority and guarantEed-bAsed Resource aLlocation approach (PEARL) for cellular V2V communications under a mode 3 configuration. Extensive simulations and comparison to other approaches corroborate the out-performance of our proposal to deliver emergency packets with lower delays and higher packet reception rate without impacting the transmission of non-safety V2X packets.

Future 5G networks, including vehicular networks, will witness the coexistence of different access technologies like IEEE 802.11p and LTE-V. This diversity will encourage vehicles moving through such technologies to select appropriate networks in order to enhance the system performance. However, Handover decision can be critical if it does not take into consideration service specificities especially in V2I (Vehicle- to-Infrastructure) context. Different MADM (Multiple Attribute Decision Making) based algorithms were previously proposed to include various selection criteria in V2I Handovers. They mainly assign various weights to the performance attributes in order to build their Handover decision. In this paper, we show the limitations of the former weighting techniques and propose a new approach to better adapt the weight to the required multimedia service quality. More precisely, we introduce a combination of objective and subjective weighting techniques taking into consideration the required quality of service metrics as well as the vehicle velocity. We especially consider two multimedia services with different quality of service requirements in vehicular communications. We show the effectiveness of our strategy in terms of throughput, packet delay and jitter for each service as compared to former RSS (Received Signal Strength) and MADM based approaches.

Long Term Evolution (LTE) networks are expected to provide enhanced quality of service (QoS) guaranteed services and a high capacity. In this paper, we propose a novel QoS guaranteed cross-layer scheduling algorithm for LTE system. In fact the LTE scheduler is responsible for efficiently allocating the radio resources among mobile users who have different QoS demands. We focus on the resource blocks allocation and the Medium Access Control (MAC) scheduling algorithm. Our study highlights the impact of the Radio Resource Control (RRC) configuration on the overall performance and the Transport Block (TB) filling during the allocation process. Performance results show that the proposed algorithm is effective in enhancing service fairness, throughput and resource optimization. Our proposal aims to find an agreement between fairness, resource availability, the requested rate and service QoS requirements.

This paper focuses on the issue of the 4G cellular systems offload through WiFi network and gives a wireless bandwidth partitioning solution of a multi-Radio Access Technologies (multi-RAT) network. We consider a Complete Partitioning strategy, where the wireless bandwidth capacity of a cell, which is provided by two types of access network LTE-A1and WiFi, is divided into trunks. Each partition is strictly reserved to a particular type of connection. Precisely, we distinguish three service classes: 1, 2 and 3 associated to VoIP, video streaming and file downloading. As we consider an offloading scheme, we also differentiate new calls from offloaded ones. The purpose of the paper is to determine, using an analytical model and a heuristic approach, the nearly-optimal sizes of the partition sizes dedicated to each type of connection, which is characterized by its service class. The solution maintains QoS of offloaded traffic and increases the total provided subscriber capacity and consequently the provider revenue.

This paper addresses the issue of wireless resource allocation of a Mobile WiMAX cell based on OFDMA access technique. We compare two strategies: the MAX SNR which is the most used channel assignment technique and our proposal which is based on channel condition learning. As we distinguish four IEEE 802.16e-2005 service classes: UGS, rtPS, nrtPS and ErtPS, we combine these strategies with a scheduler block in order to satisfy the QoS constraints of these classes. In addition, we take into consideration the Adaptive Modulation and Coding (AMC) scheme, through the selection of the slot which is based on the modulation scheme. The main concern here is to propose a simple method to assign WiMAX physical resources called slot to satisfy QoS constraints of a user's scheduled MPDUs, which can be easily and efficiently used in a realistic Mobile WiMAX network.