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Tour planning for multiple mobile sinks in wireless sensor networks: A shark smell optimization approach
Applied Soft Computing ( IF 5.472 ) Pub Date : 2020-10-17 , DOI: 10.1016/j.asoc.2020.106802
Raj Anwit; Abhinav Tomar; Prasanta K. Jana

Sink mobility has been regarded as a widely accepted method for data collection in wireless sensor networks (WSNs) as it significantly improves the network performance. Particularly, data collection using mobile sink based on rendezvous points (RPs) is a hot research topic which has been paid enormous attention in WSN community. However, efficient tour planning for the mobile sink (MS) is a challenging problem, especially for delay-harsh applications that require shorter paths of the MS. Existing literature finds this problem as NP-hard in nature, and thus nature-inspired algorithms are in demand as they can provide a near-optimal solution within acceptable time and space constraints. There are many schemes on MS tour planning that exist in the form of heuristics or nature-inspired algorithms, nevertheless, they leave out the scope for further research as most of them have not considered disjoint networks. Moreover, they fail to jointly optimize both the number of RPs and the number of MSs. To this end, this paper presents a novel scheme comprising of two algorithms based on shark smell optimization (SSO) technique that solves the MS tour planning problem. The first algorithm is used to determine an optimal number of RPs and their locations. Based on this, the second algorithm optimizes the number of MSs so as to minimize the overall tour length. Each of the algorithms is developed with an efficient and novel particle encoding scheme along with the derivation of fitness function. The tour planning is formulated as an Integer Linear Programming problem for the first algorithm and Non-linear Programming problem for the second algorithm. Simulation results of our scheme confirm the improvement over the state-of-the-art algorithms. The results are also statistically validated through hypothesis testing using ANOVA and post hoc analysis.

更新日期:2020-10-17

 

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