Fault-resilient localization using fuzzy logic and NSGA II-based metaheuristic scheme for UWSNs

Abstract

Localization is the fundamental problem for event detecting and monitoring due to the mobility of nodes UWSNs. In the literature, researchers have examined the localization problem through optimization schemes for evaluating the accurate location of unknown target nodes . However, the existing schemes have not considered the failure of anchor nodes during obstacles, natural disasters and environmental interferences. Due to this, the network leads to high localization error and energy depletion. Therefore, in this paper, we considered anchor node failure and bad distance computational between anchor-target pair for better localization. For this, fault-resilient localization using FL and NSGA II-based metaheuristic scheme is proposed for UWSNs. In this scheme, first, we refined the correction value of an anchor node to improve the distance between anchor-target pair. Afterward, FL and NSGA II are applied to identify the set of optimal anchor nodes for the fault-resilient localization process. Furthermore, we have also incorporated fitness value with the three parameters such as residual energy, modified anchor-target distance and a number of the connected nodes as the input parameters, whereas probability is the output for the selection of optimal anchor nodes. Performance evaluation of the proposed scheme outperforms the existing scheme in terms of average localization error, better coverage positioning, localized and bad node proportion with a fast convergence rate under different network scenarios.

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

“…For instance, Draz et al used a bio-inspired meta-heuristic algorithm to schedule nodes, developing a trust-based hybrid-blockchain-enabled localization method [53]. Kumari et al considered anchor failures and investigated an NSGA-based meta-heuristic scheme for underwater localization [54]. Liu et al developed a three-step localization approach using a meta-heuristic algorithm, the gray wolf optimizer, in the final round [55].…”