Energy Aware Oppositional Whale Optimization Based Linear Discriminant Load Balanced Routing And Data Delivery In Manet

Abstract

MANETs are independent wireless networks that emerge often in self-organized application development, where the Mobile Nodes (MN) communicate with others through the wireless interfaces. Designing an energy efficient load balanced routing and data delivery are the major concern of MANETs due to dynamic nature with inadequate battery capability. An Energy Efficient Whale Optimized Linear Discriminant Load Balanced Routing (EEWOLDLBR) technique is presented in MANET for efficient data delivery. The EEWOLDLBR technique comprises three processes namely route path discovery, data transmission and route maintenance.  In EEWOLDLBR technique, multiobjective oppositional learned whale optimization process is carried out to find the optimal mobile nodes for route establishment. With optimized energy efficient MN, Linear Discriminant Load balancing method is used to find the minimal load to deliver the Data Packets (DP). During the data packet delivery, in case of any link break, alternate energy efficient and less loaded mobile node is chosen for route maintenance in network with efficient data delivery. Simulation outcome of EEWOLDLBR technique achieves higher delivery ratio and throughput with lesser delay as well as packet loss as compared with previous routing techniques.