ROLG: Delay Sensitive And Fault Tolerant Hybrid Mobility Model For Wireless Body Area Network

Abstract

In recent years, WBAN has been one of the most used networks in the field of healthcare. Along with its uses, it comes along with a lot of challenges to manage to get the maximum outcome from the network. The most significant challenges of all time involve the location of the sink node and the identification of lost nodes during movement. The proposed mobility model named ROLG which is delay sensitive and fault tolerant hybrid mobility model for WBAN environment. ROLG inherits the benefits of both group and individual mobility patterns and thus allows a realistic modeling of the body movements and ensure minimal communication delays and higher network resilience. The model also incorporates predictive mapping of mobility and localized recovery that ensures data flow even in the event of node or link failure. The proposed approach utilizes the grey wolf algorithm for achieving the tremendous growth in the network performance of WBAN. The simulation results revealed that with optimized 25 sink nodes, the performance of PDR performs well with no packet loss, enhances the throughput to 34.61 kbps, and reduces the delay to 0.001226 seconds which proves superior efficiency in comparison to random sink movement. At 50 nodes, PDR functions well with no packet loss and reduced delay to 0.002563 seconds, throughput stays at 28.94 kbps with optimized sink movement whereas, with random sink movement throughput lowers to 19.48 and increased the delay. The ROLG mobility model has successfully localized every lost node in the network and has achieved high proficiency in the overall network performance.