Nature-inspired donkey and smuggler algorithm for optimal data gathering in partitioned wireless sensor networks for restoring network connectivity

Abstract

Wireless Sensor Networks (WSNs) often operate in hostile environments and are subject to frequent failures. Failure of multiple sensor nodes causes the network to split into disjoint segments, which leads to network partitioning. Federating these disjoint segments is necessary to prevent detrimental effects on WSN applications. This paper investigates a recovery strategy using mobile relay nodes (MD-carrier) for restoring network connectivity. The proposed MD-carrier Tour Planning (MDTP) approach restores network connectivity of partitioned WSNs with reduced tour length and latency. For this reason, failure nodes are identified, and disjoint segments are formed with the k-means algorithm. Then, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used for the election of an AGgregator Node (AGN) for each segment. Furthermore, an algorithm for identifying sojourn locations is proposed, which coordinates the maximum number of AGNs. Choosing the sojourn locations is a challenging task in WSN since the incorrect selection of the sojourn locations would degrade its data collection process. This paper uses the nature-inspired meta-heuristic Donkey And Smuggler Optimization (DASO) algorithm to compute the optimal touring path. MDTP reduces tour length and latency by an average of 30.28% & 24.56% compared to existing approaches.