A Multidimensional Trust Evaluation Model for MANETs

Effective trust management can enhance nodes’ cooperation in selecting trustworthy and optimal paths between the source and destination nodes in mobile ad hoc networks (MANETs). It allows the wireless nodes (WNs) in a MANET environment to deal with uncertainty about the future actions of other participants. The main challenges in MANETs are time-varying network architecture due to the mobility of WNs, the presence of attack-prone nodes, and extreme resource limitations. In this paper, an energy-aware and social trust inspired multidimensional trust management model is proposed to achieve enhanced quality of service (QoS) parameters by overcoming these challenges. The trust management model calculates the trust value of the WNs through peer to peer and link evaluations. Energy and social trust are utilized for peer to peer evaluation, while an optimal routing path with a small number of intermediate nodes with minimum acceptable trust value is used for evaluation of the link. Empirical analysis reveals that the proposed trust model is robust and accurate in comparison to the state-of-the-art model for MANETs

A STABLE CLUSTERING SCHEME WITH NODE PREDICTION IN MANET

The main concern in MANET is increasing network lifetime and security. Clustering is one of the approaches that help in maintaining network stability. Electing an efficient and reliable Cluster Head (CH) is a challenging task. Many approaches are proposed for efficient clustering, weight-based clustering is one among them. This paper proposes a stable clustering scheme which provides network stability and energy efficiency. Proposed Stable Clustering Algorithm with Node Prediction (SCA-NP) computes the weight of the node using a combination of node metrics. Among these metrics, Direct Trust (DT) of the node provides a secure choice of CH and Node Prediction metric based on the minimum estimated time that node stay in the cluster provides the stable clustering. Mobility prediction is considered as the probability that a node stays in the network. This metric helps in electing CH which is available in the network for a longer time. Simulation is done in NS3 to evaluate the performance of SCA-NP in terms of clusters formed, network lifetime, efficiency in packet delivery, detecting malicious nodes and avoiding them in communication