Effective Routing in Vehicular Adhoc Network (VANET) using an Bio-inspired Algorithm: Enhanced Deep Reinforcement Learning (EDRL) for Secure Wireless Communication

For improving the performance of city wide-ranging lane networks through the optimized control signal, we proposed a framework in Vehicular Adhoc Network (VANET). Node which reduces the traffic efficiency drastically is identified as critical node, with the help of defined framework. Tripartite graph is used for identifying critical node through vehicle trajectory in the over-all viewpoint. Enhanced Deep Reinforcement Learning (EDRL) method is introduced to control the traffic signal and gives appropriate decision for routing the data from Road Side Unit (RSU) to intermediate or destination node. Various experiments were done with proposed model and the result shows considerable efficiency in delay and travelling time of the node in VANET.

Autonomous Real-Time Speed-Limit Violation Detection and Reporting Systems Based on the Internet of Vehicles (IoV)

Over-/underspeeding is one of the leading causes of road accidents. Traditional systems of detecting and reporting speed-limit violations are not suitable for smart cities. Even the sophisticated conventional systems that use cameras or RFIDs for automating speed-limit violations have several drawbacks, including cost, complexity, reliability, and maintenance. In this paper, we present two systems based on the Internet of Vehicles (IoV) to automatically detect speed-limit violations and autonomously report the committed violations to the authorities. Our systems require no extra hardware or equipment: only the On-Board Unit (OBU), the Road Side Unit (RSU), and the Cloud Server software have to be updated to have a fully functioning system as long as the IoV infrastructure is deployed. One of the systems will be installed on the OBU. A second alternative system design is to use Cloud Servers (CSs) and the IoV beacons that are sent from the vehicles. Additionally, unlike the existing systems installed in specific locations, all roads in the smart cities and highways will be fully monitored. Adaptive fine calculation according to new dynamic policies can be easily integrated into the proposed system. Furthermore, the proposed system can accurately operate in all weather conditions. Moreover, it allows the dynamic adjustment of the speed limits according to the current weather conditions. We have validated the proposed system by building a prototype system that effectively and accurately detects and reports over-/underspeed traffic violations alongside any road.

A Controllable RSU and Vampire Moth to Support the Cluster Stability in VANET

VANET has a dynamic topology that affects cluster formation stability. It influences vehicle’s network quality though supporting this stability requires a fast and small cluster formation process. It is necessary because of the rapidly changing condition of the vehicle's position. Moreover, small cluster groups make network quality more evenly distributed among its members. They are essential components in the formation of good clusters in VANET. In the previous research, the algorithm used for this process is based on moth flame optimization. This study proposes modifications in this algorithm to speed up the convergence process, facilitated by a model of turning angles in the moth. Furthermore, the flame at the destination moth is created dynamically to approach the real conditions in VANET. The use of coefficients is further introduced to reduce cluster size according to the traffic conditions. The cluster management process uses a fully controlled Road Side Unit on the vehicle’s traffic conditions. In the final result, the convergence time is slightly faster, with better results in the throughput at 101.1%, the number of clusters at 58.1%, and the delay at 5.5%, respectively.

Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks

Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.