Multi‐objective optimisation for selective packet discarding in wireless sensor network. (1st June 2015)

• Record Type: Journal Article
• Authors: Yaakob, Naimah; Khalil, Ibrahim; Atiquzzaman, Mohammed
• Is Part Of: IET wireless sensor systems. Volume 5:Number 3(2015)
• Page Start: 124
• Page End: 136
• Publication Date: 2015-06-01
• Subjects: optimisation -- wireless sensor networks; multiobjective optimisation tool -- selective packet discarding policy -- wireless sensor network -- traffic convergence -- WSN -- simultaneous data transmission -- buffer capacity -- energy depletion -- MOO -- LINGGO -- Network Simulator 2; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2014.0020
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : Traffic convergence in wireless sensor networks (WSN) during simultaneous data transmission may overwhelm its limited buffer capacity, resulting in congestion, waste of resources and severe performance degradation. The obvious consequences include high packet loss rate, huge amounts of wasted energy and obsolete data that may lead to inaccurate information. Since WSN suffers from scarce resources such as energy, data transmission which is the main cause of energy depletion should be kept to the very minimum. Various studies have used packet discarding as a means to reduce high traffic volumes. However, none of these methods have ever been applied in WSN which possesses different characteristics. This study proposes a new technique for mitigating congestion by selectively discarding some of the least important packets to give sufficient room for more important ones to get through. The proposed discarding policy is integrated with multi‐objective optimisation (MOO) which can optimise several objectives at once. The proposed selective packet discarding policy discards the unimportant packets based on some discarding criteria which will be optimised by the MOO. Performance evaluation using the optimisation tool (LINGGO) and simulation in Network Simulator 2 shows remarkable and promising performance with more than 50% improvement.