An efficient cluster-based service model for vehicular ad-hoc networks on motorways

Vehicular Ad-Hoc Networks (VANET) can, but not limited to provide users with useful traffic and environmental information services to improve travelling efficiency and road safety. The communications systems used in VANET include vehicle-to-vehicle communications (V2V) and vehicle-to-infrastructure communications (V2I). The transmission delay and the energy consumption cost for maintaining good-quality communications vary depending on the transmission distance and transmission power, especially on motorways where vehicles are moving at higher speeds. In addition, in modern transportation systems, electric vehicles are becoming more and more popular, which require a more efficient battery management, this also call for an efficient way of vehicular transmission. In this project, a cluster-based two-way data service model to provide real-time data services for vehicles on motorways is designed. The design promotes efficient cooperation between V2V and V2I, or namely V2X, with the objective of improving both service and energy performance for vehicular networks with traffic in the same direction. Clustering is an effective way of applying V2X in VANET systems, where the cluster head will take the main responsibility of exchanging data with Road Side Units (RSU) and other cluster members. The model includes local service data collection, data aggregation, and service data downloading. We use SUMO and OMNET++ to simulate the traffic scenarios and the network communications. Two different models (V2X and V2I) are compared to evaluate the performance of the proposed model under different flow speeds. From the results, we conclude that the cluster-based service model outperforms the non-clustered model in terms of service successful ratio, network throughput and energy consumption

2-hop Clustering to accomplish semi-static structure in

Most researches today trend to clustering in ad hoc networks for building hierarchies to solve management problems in flat architectures. Clustering aims to elect suitable nodes as representatives to lead the network, called Cluster Heads (CHs). Frequent topology changes occur due to nodes mobility and failure. Although re-clustering is invoked to maintain the clusters, many cases involve destroying the cluster when the CH moves to another region or fails and hence building new cluster/s is needed which negatively affects the stability of the network and its ability to provide services. In this research, I developed a 2-hop clustering solution to accomplish a semi-static structure. This is accomplished by reassigning the CHs according to the number of 1-hop neighbors. The node that has the highest number of 1-hop neighbors that are in the 1-hop range of the CH has the highest connectivity with the members and hence it is the best node to replace the CH when moves or fails. Simulation results show accomplishing semi-static structure and enhancing the performance of the ad hoc network. Keywords: Ad hoc networks, Cluster, Connectivity Numbe

Assessing the Performance of a Particle Swarm Optimization Mobility Algorithm in a Hybrid Wi-Fi/LoRa Flying Ad Hoc Network

Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes. One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is computing the optimal position of UAVs to provide connectivity with ground nodes while maximizing geographic span. To achieve optimal positioning of FANET nodes, an adaptation of the Particle Swarm Optimization (PSO) algorithm is proposed. A 3D mobility model is defined by adapting the original PSO algorithm and combining it with a fixed-trajectory initial flight. A Long Range (LoRa) mesh network is used for air-to-air communication, while a Wi-Fi network provides air-to-ground communication to several ground nodes with unknown positions. The optimization problem has two objectives: maximizing coverage to ground nodes and maintaining an end-to-end communication path to a control station, through the UAV mesh. The results show that the hybrid mobility approach performs similarly to the fixed trajectory flight regarding coverage, and outperforms fixed trajectory and PSO-only algorithms in both path maintenance and overall network efficiency, while using fewer UAVs

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios

Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication

Delimitated Anti Jammer Scheme for Internet of Vehicle:Machine Learning based Security Approach

Recently, Internet of vehicles (IoV) has witnessed significant research and development attention in both academia and industries due to the potential towards addressing traffic incidences and supporting green mobility. With the growing vehicular network density, jamming signal centric security issues have become challenging task for IoV network designers and traffic applications developers. Global positioning system (GPS) and roadside unit (RSU) centric related literature on location-based security approaches lacks signal characteristics consideration for identifying vehicular network intruders or jammers. In this context, this paper proposes a machine learning oriented as Delimitated Anti Jamming protocol for vehicular traffic environments. It focuses on jamming vehicle's discriminated signal detection and filtration for revealing precise location of jamming effected vehicles. In particular, a vehicular jamming system model is presented focusing on localization of vehicles in delimitated jamming environments. A foster rationalizer is employed to examine the frequency changes caused in signal strength due to the jamming or external attacks. A machine learning open-sourced algorithm namely, CatBoost has been utilized focusing on decision tree relied algorithm to predict the locations of jamming vehicle. The performance of the proposed anti jammer scheme is comparatively evaluated with the state of the art techniques. The evaluation attests the resistive characteristics of the anti-jammer technique considering precision, recall, F1 score and delivery accuracy metrics