Power Efficient Location Aware Routing Protocol to Improve Routing in MANET

The Mobile Adhoc network (MANET) uses the concept of dynamic topology in the wireless network. The major noteworthy issues in construction of MANET are the energy consumption by the nodes. According to the requirement of present situation a variety of energy efficient routing protocol has been suggested that helps in increasing the lifetime of the network. Emerging Trends in energy efficient routing protocols as the name suggest recognize that many approaches like clustered, genetic algorithm (GA) and so many has came in existence that helps in growing the network lifetime of energy efficient routing protocols. In this paper we proposed a novel Power Efficient Location Aware Routing (PELAR) protocol. In this protocol energy dependent nodes are growing the routing ability of AODV protocol on the source of LAR (Location Aided Routing) protocol. In network nodes are not aware about their energy status and also return flooding of routing packets is utilizes extra energy in network by that the bulk of the energy is exhausted in handshaking process. The main attempt of proposed PELAR protocol is to obtain improved the energy utilization in network. The performance of usual AODV, LAR and PELAR is show via simulation implemented on NS2 and observe that the proposed PELAR protocol decreases the energy utilization and improve the network lifetime that completely depend on the energy of mobile nodes. Keywords: AODV, Energy Efficiency, LAR, MANET, Routing protocol

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicles reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the dataset to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models