Open AccessM‐LionWhale: multi‐objective optimisation model for secure routing in mobile ad‐hoc network
Author(s) -
Chintalapalli Ram Mohan,
Ananthula Venugopal Reddy
Publication year - 2018
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2017.1279
Subject(s) - computer science , mobile ad hoc network , computer network , quality of service , wireless ad hoc network , throughput , network packet , optimized link state routing protocol , adaptive quality of service multi hop routing , routing (electronic design automation) , destination sequenced distance vector routing , selection algorithm , distributed computing , routing protocol , path (computing) , selection (genetic algorithm) , link state routing protocol , wireless , telecommunications , artificial intelligence
Mobile ad‐hoc network (MANET) is an emerging technology that comes under the category of wireless network. Even though the network assumes that all its mobile nodes are trusted, it is impossible in the real world as few nodes may be malicious. Therefore, it is essential to put forward a mechanism that can provide security by selecting an optimal route for data forwarding. In this study, a goal programming model is designed using a hybrid optimisation algorithm, called M‐LionWhale, for secure routing. M‐LionWhale is an optimisation algorithm that incorporates lion algorithm (LA) into whale optimisation algorithm (WOA) for the optimal selection of the path in MANET. The multi‐objective optimisation model considers several quality of service (QoS) parameters, namely energy, distance, link lifetime, delay, and trust. With the estimated multi‐objective parameters, a fitness function is developed for the best selection of routes. The performance of the proposed algorithm is evaluated using three metrics, such as packet delivery ratio (PDR), throughput, and energy and is compared with that of existing trust‐based QoS routing model, LA, and WOA. The proposed M‐LionWhale algorithm could attain the maximum performance with 24.1313% residual energy, throughput of 0.2966 kbps, and PDR of 0.3051 at maximum simulation time.