Enhancing Data Collection in Vehicular Network Through Clustering Optimization

International audienceIn this paper, we present a novel approach to enhance data collection in Vehicular Ad-Hoc NETworks (VANETs). VANETs are a growing area of interest due to their unique characteristics and challenges, such as rapidly changing topology and frequent network disruptions. Efficient data collection is a critical issue in vehicular networks and has therefore become a focus of research. To address this challenge, we propose a stable clustering optimization solution based on adaptive multiple metrics. The cluster head selection is done based on both mobility metrics, such as position and relative speed, and Quality of Service (QoS) metrics, such as neighborhood degree and link quality. The proposed solution has been tested and evaluated through simulations using a vehicular mobility simulator in a realistic urban environment. The results show that the proposed approach provides more stable clusters with higher QoS, and allows for the selection of the appropriate cluster head to collect data from the vehicles and forward it to the destination

An IoT scheduling and interference mitigation scheme in TSCH using latin rectangles An IoT Scheduling and Interference Mitigation Scheme in TSCH using Latin Rectangles

International audienceTime Slotted Channel Hopping (TSCH) is one of the most used MAC mechanisms introduced by the new amendment IEEE 802.15.4e. It combines both slotted access with channel hopping technique to allow multiple communications while exploiting the 16 available channels of 2.4GHz band. The channel hopping mechanism of 802.15.4e considers an interference-free environment and does not specify how to build and manage a schedule for communication purpose. In this paper, we propose a new distributed channel hopping scheme that exploits Latin rectangles to avoid interference and collisions. In essence, the scheduling of links is performed by Latin rectangles where rows are channel offsets and columns are slot offsets. Thus, the frequency of communication is derived using Latin rectangles. Consequently, interference and multi-path fading are mitigated with more reliability and robustness. The efficiency of the proposed scheme has been validated by extensive simulation

Performance of topology-based data routing with regard to radio connectivity in VANET

International audienceVehicular Ad hoc NETworks (VANETs) are characterized by the rapidly changing topology and then a frequentnetwork disruption. Hence, connectivity of moving vehicles presents an important challenge that critically influences the data transmission. Furthermore, data delivery ratio depends on routing protocols, applications type as well as environment characteristics. As a matter of fact, real experimentation in vehicular networks are costly and hard to deploy especially on large scale. Consequently, a vehicular mobility simulator is a good compromise to study how efficient are the data transmission mechanisms. In this paper, we comprehensively study the impact of the radio connectivity on data communication in vehicular networks. The analysis were realized based on a vehicular mobility simulator which runs a realistic scenario of mobility traffic in a real urban environment. A simple scenario of a safety application was implemented to examine the behavior of three well-known topology-based routing protocols. For thepurpose of the analysis, we varied the simulation setup such as the density and the data traffic rate to determine the impact of the connectivity. The simulation results show that a realistic modelling of radio propagation has an important role in data transmission

More Insights Into Communication Issues in the Internet of Vehicles

International audienceThe incorporation of information and communication technologies within vehicles has truly revolutionized the way we travel today. Connected vehicles represent the building blocks of the emerging Internet of Vehicles (IoV). They are spurring an array of applications in the area of road safety, traffic efficiency and driver's assistance. Connected vehicles refer to vehicles that can support Vehicle-to-X (V2X) connectivity. The critical challenge is to design good mobility and propagation models. In this paper, we intend to study the relevance of a realistic mobility model and a realistic propagation model. First, we analyze the most common routing protocols performance for MANET, namely OLSR, AODV and DSR thanks to a network simulator. Next, we study the influence of both models on a simple safety service. The major result is highlighting the impact of realistic modeling on the simulation

Optimizing drone deployment for cellular communication coverage during crowded events

International audienceIn case of unexpected or temporary events, cellular networks can become quickly saturated. A promising solution is using unmanned aerial vehicles (UAVs), known as drones, as flying base stations. In this article, we address the issue of anomalous behaviour within cellular networks that occurs during crowded events. The proposed approach consists of two parts: the detection of overloaded cells using machine learning algorithm (LSTM – Long Short-Term Memory) and the deployment of drone-Bss to assist the cellular network by providing wireless coverage. Initially, we use the LSTM algorithm to analyze the impact of extra-data on the network and then detect the peaks of users demands. Then, we formulate an optimization problem for maximizing the number of users to serve when deploying drones taking into account the energy constraints. The proposed approach is validated using real dataset extracted from the CDR of Milan. Simulation results show that the use of drones can satisfy the QoS requirements of the network

Adaptive range-based anomaly detection in drone-assisted cellular networks

International audienceStimulated by the emerging Internet of Things (IoT) applications and their massive generated data, the cellular providers are introducing various IoT functionalities into their networks architecture. They should integrate intelligent and autonomous mechanisms that are able to detect sudden and anomalous behavior issues. In this paper, we present an adaptive anomaly detection approach in cellular networks consisting of two parts: the detection of overloaded base-stations using machine learning algorithm (LSTM -- Long Short-Term Memory) and the deployment of drones as mobile base-stations that support and back up the overloaded cells. The proposed approach is validated using real dataset combined with semi-synthetic eHealth dataset. Initially, The LSTM algorithm analyzes the impact of eHealth applications on cellular networks and identifies cells with peak demands. Then, drones are deployed to collect the requested data from these cells. The obtained results show that the use of drones improves the quality of service and provides a better network performanc

Deep learning approaches for electrical vehicular mobility management: invited paper

International audienceElectrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction