A futuristic trust coefficient-based semi-Markov prediction model for mitigating selfish nodes in MANETs

Abstract

In mobile ad hoc networks (MANETs), network survivability is considered as a potential factor required for maintaining maximum degree of connectivity among the mobile nodes even during failures and attacks. But, the selfish mobile nodes pose devastating influence towards network survivability. Hence, a prediction model that assesses network survivability through stochastic properties derived from nodes' behaviour becomes essential. This paper proposes a futuristic trust coefficient-based semi-Markov prediction model (FTCSPM) that investigates and quantifies the impact of selfish behaviour towards the survivability of the network. This FTCSPM approach incorporates a non birth-death process for manipulating futuristic trust coefficient since it does not consider the transition of a mobile node from the failed state to a selfish state into account. This semi-Markov prediction model also aids in framing a lower and upper bound for network survivability. Extensive simulations were carried out through ns-2 and the results indicates that FTCSPM show better performance than the existing benchmark mitigation mechanisms like correlated node behaviour model (CNBM), probabilistic behavior model (PBM) and epidemic correlated node behavioural model (ECNBM) proposed for selfish nodes. Further, FTCSPM isolates the selfish nodes rapidly at the rate of 33 % than the considered benchmark systems. Furthermore, the validation of this prediction model performed through Weibull distribution has a high degree of correlation with the simulation results and thus assures the reliability and correctness of the proposed approach. In addition, this approach computes the mean transition time incurred by a mobile node to transit from cooperative to selfish mode as 6.49 s and also identifies the minimum and maximum selfish behaviour detection time as 140 and 180 s, respectively.

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

“…This algorithm detects the selfish nodes by forecasting the number of the forwarding packets, the number of received packets to the destination, and the remaining energy. FTCSMP has low energy consumption and high false positive rate [24]. A probabilistic behavioural model (PBM) is the method, which models the node behaviour.…”

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

“…The comparative analysis of these states of art for hidden markov based trust models are presented in table 1. Further, FUCEM [5], FTCSPM [9], and OADM [13] discussed above are considered for comparison since they are proven as significant models for efficient and effective evaluation of node's trust. In addition, these models predict the node's behavior effectively and improve the performance of the network.…”

Opportunistic content dissemination in mobile social networks via adjustment of user selfishness

“…The performance of energy-constrained gossip routing with selfish users is investigated in [35], which the authors' model the data transmission process of users as a two-dimensional continuous time Markov process. The authors in [36] investigate the semi-Markov prediction model in a MANET with selfish nodes to forecast the mobile node's behaviour based on their current state. The authors in [37,38] proposed a novel multi-cast protocol to disseminate the content of common interest by a discrete-time pure-birth-based Markov-chain (DT-BPMC) with respect to the users' social interaction.…”