Computational intelligence-based connectivity restoration in wireless sensor and actor networks

Abstract

Abstract Network failure is categorized into the two types of software and hardware (physical layer) failure. This paper focuses on the physical layer failure in the wireless sensor and actor networks (WSANs). Actors play an important role in data processing, decision-making, and performing appropriate reactions. Single or multiple nodes failure of actors due to the explosion, energy depletion, or harsh environments, can cause multiple disjoint partitions. This paper has proposed a new computational intelligence-based connectivity restoration (CICR) method. It uses a combination of advanced computational intelligence methods to solve restoration problem. The proposed algorithm applies the novel enhanced Lagrangian relaxation with a novel metaheuristic sequential improved grey wolf optimizer (SIGWO) search space algorithm in simultaneous selection of k sponsor and p pathway nodes. The reactive proposed method aims to reduce the travel distance or moving cost and communication cost. As a result, the restored network has minimum of topology change and energy consumption. In terms of total traveled distance, CICR has 37.19%, 71.47%, and 44.71% improvement in the single-node failure averagely in comparison with HCR, HCARE, and CMH, respectively. Also, it has an average of 61.54%, 40.1%, and 57.76% improvement in comparison with DCR, PRACAR, and RTN in multiple partitions resulted from multiple nodes failure, respectively. The reliability of CICR method has improved averagely by 35.85%, 38.46%, 22.03% over HCR, CMH, and HCARE in single-node failure. In multiple nodes failure, reliability of CICR has averagely 61.54% and 20% over DCR and PRACAR, respectively.

Keywords

• Wireless sensor and actor networks
• Restoration
• Travel distance
• Distributed optimization process
• Partitioning