Affinity Propagation-driven Distributed clustering approach to tackle greedy heuristics in Mobile Ad-hoc Networks. (October 2018)

• Record Type: Journal Article
• Authors: Nabar, Kaustubh; Kadambi, Govind
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 988
• Page End: 1011
• Publication Date: 2018-10
• Subjects: Clustering -- Mobile ad hoc networks -- Affinity propagation -- Greedy clustering -- Gauss Markov -- Mobility -- Analytical model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.10.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Abstract: This paper presents a Gauss Markov Mobility-based Affinity Propagation-driven Distributed (GMM-APD) clustering approach which primarily tackles the greedy clustering heuristics in MANETs. GMM-APD addresses two fundamental short-comings of greedy approach namely: (a) Use of local optima concept and (b) frequent event-driven broadcasts by mapping evolution of the network topology into clusters. Node mobility is considered as the proximity criteria while the node movement pattern is represented as a Gauss Markov (GM) distribution. The temporal dependency of GM distribution is used to compute the proximity criteria which is further optimised by allowing the network to evolve using a modified Affinity Propagation technique; followed by the clustering decisions. An analytical model to explain the dynamics of GMM-APD is also presented describing the occurrences of various events taking place on the timeline of cluster and node. Finally, GMM-APD clustering technique is represented as an Integer Linear Problem formulation focussing on minimising the dominating set. The simulations carried out using NS-2 reveal that GMM-APD generates a quasi-optimal set of cluster heads and exhibits enhanced performance as compared to some of the existing clustering techniques in terms of cluster stability, quality and cost.