Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

Vehicle Ad hoc Networks(VANETs)have high mobility and a rando-mized connection structure,resulting in extremely dynamic behavior.Several challenges,such as frequent connection failures,sustainability,multi-hop data transfer,and data loss,affect the effectiveness of Transmission Control Protocols(TCP)on such wireless ad hoc networks.To avoid the problem,in this paper,mobility-aware zone-based routing in VANET is proposed.To achieve this con-cept,in this paper hybrid optimization algorithm is presented.The hybrid algo-rithm is a combination of Ant colony optimization(ACO)and artificial bee colony optimization(ABC).The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another.The optimal routing process is used to avoid traffic and link failure.Thefitness function is designed based on Link stability and Residual energy.The validation of the proposed algorithm takes solution encoding,fitness calculation,and updat-ing functions.To perform simulation experiments,NS2 simulator software is used.The performance of the proposed approach is analyzed based on different metrics namely,delivery ratio,delay time,throughput,and overhead.The effec-tiveness of the proposed method compared with different algorithms.Compared to other existing VANET algorithms,the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay.

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.