The NETWORK overhead and multiple NETWORKs disconnection faults are the main challenges of anonymous servers implemented in VANETs. The block chain technology has been entered into the wide range of preserving privacy. The robust anonymity mechanism existence and the traceability of all transactions are the main advantages of this technology. The primary model of the block chain was able to complete the process with the anonymity stored data. In distributed models, the authentication, storage and retrieval of transactions are applied by all user’ s consensus. The asymmetric cryptography, preserves the identity anonymity and aggregating transactions of different users into a block which is ready to send, preserves the path anonymity. The proposed method is aimed to ensure anonymity by mounting the block chain on VANETs. Before delivering any transaction to the block chain, the risk of user’ s privacy is high. To achieve low risk, we combine the graph processing methods with Silent Period, Cloaking-Region and Dummy Node methods. The block chain simulation on VANET is driven by python and the anonymity risks are simulated with ARX. The results suggest that the block chain is stabled and the optimal risk reduction is achieved on the VANET.

Efficient message delivery in city environment is required to ensure driver’ s safety and passenger’ s comfortability. In cities of developed nations, routing of data in VEHICULAR Ad HOC NETWORK (VANET) faces many challenges such as radio obstacles, mobility constraints and uneven nodes distribution. These factors primarily makes communication between vehicles complex. To overcome and transmit data traffic effectively in city environment in the presence of abovementioned challenges, evaluation of some NETWORK parameters conducted. The selected metrics are packet delivery ratio (PDR), end-to-end delay and routing overhead. These are based on three performance of position-based routing protocols: Anchor– based Street and Traffic Awareness Routing (A-STAR), Greedy Perimeter Coordinator Routing (GPCR) and Contention Based Forwarding (CBF) with the help of Simulation of Urban Mobility (SUMO) to generates vehicle mobility in city environment and NETWORK simulator (OMNeT++) to creates and calculates needed components. The speed of the vehicles and node density were varied in this process, and the simulated results showed that CBF outperforms significantly than A-STAR and GPCR in terms of packet delivery ratio as the speed varied and with a better end-to-end delays and routing overhead at a lower speed. In addition, CBF performs better than A-STAR and GPCR in terms of packet delivery ratio as the node varied with a better end-to-end delay, and a better routing overhead at a lower node density.

VEHICULAR ad-HOC NETWORKs (VANETs), as a result of today's vehicles equipped with different wireless technology, have been attracting interest for their potential roles in many fields such as emergency, safety, and intelligent transport system. However, the development of a reliable routing protocol to route data packets between vehicles is still a challenging task due to the high mobility, lack of fixed infrastructure, and obstacles. One technique to tackle this challenge is using machine learning. In this paper, we have proposed a protocol applying multi-agent reinforcement learning (MARL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized NETWORKs. Our protocol is based on a model-based reinforcement learning method which has a higher convergence speed compared to the model-free one. To form the needed model for MARL, we have developed a Fuzzy Logic (FL) system that evaluates the quality of links between neighbor nodes based on parameters such as velocity and connection quality. The performance of the proposed protocol is studied by extensive simulation with respect to various metrics such as delivery ratio, delay, and overhead. The results obtained show significant improvement of VANETs performance in terms of these metrics.

VANET (VEHICULAR Ad-HOC NETWORK) is a developing technology, which is a combination of cellular technology, ad-HOC NETWORK & wireless LAN to improve the safety of vehicle as well as driver. VANET communication can be of two types, first one is broadcast and second one is unicast. Either communication may be broadcast or unicast both are sensitive to different types ofassaults, for example message forgery, (DOS) denial of service, Sybil assault, Greyhole, Blackhole & Wormhole assault. In this paper machine learning method is used to detect the wormhole assault in VANET’ s multi-hop communication. We have created a scenario of VANET by using AODV routing protocol on NS-3. 24. 1 simulator, which utilizes the overall mobility traces generated by the simulator SUMO-0. 32. 0 to model the wormhole assault. The simulation is performed by using NS-3. 24. 1 simulator, and the statistics created by flow monitor are collected. The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. The novelty of this research work is that with the help of proposed detection & prevention technique, VEHICULAR ad-HOC NETWORK can be made free from wormhole assault by using ML approach. The performance of proposed machine learning models is compared with existing work. In this way it is clear that our proposed approach by using ML is powerful tool by which the wormhole assaults can be detected in VANETs. A scheme based on packet lease and cryptographic techniques is used to prevent the wormhole attack in VANET.

VEHICULAR Ad HOC NETWORKs (VANETs) are a particular type of Mobile Ad HOC NETWORKs (MANETs) in which the vehicles are considered as nodes. Due to the rapid topology change and frequent disconnection in these NETWORKs, it is difficult to design an efficient routing protocol for routing data among vehicles. In this work, a new routing protocol is provided based on the glowworm swarm optimization algorithm. By using the glowworm algorithm, the proposed protocol detects the optimal route between three-way and intersections. Then the packets are delivered based on the selected routes. The proposed algorithm assigns a value to each route from a source to the destination using the glowworm swarm optimization algorithm, which is a distributed heuristic algorithm. Then a route with a higher value is selected to send messages from the source to the destination. The simulation results show that the proposed algorithm has a better performance than the similar algorithms.