Data harvesting in wireless sensor networks using mobile sinks under real-world circumstances

This paper investigates the problem of data gathering with mobile sinks (MSs) in real-world wireless sensor networks (WSNs), which may contain obstacles, rocks, hills, etc. The previous approaches studied MS-based data gathering in WSNs with simple obstacles. The considered obstacles in these studies did not precisely model the real-world sensing fields. We propose the MS-based data harvesting in real-world sensing Fields (MSDRF) algorithm to tackle the shortcomings of the previous studies. The proposed scheme performs preprocessing on the field first, followed by configuring the WSN in successive rounds for data gathering. The preprocessing method partitions the field into cells and determines the cost of MS traveling between different cells. The network configuration comprises clustering and tour construction phases, which are solved using Artificial Intelligence algorithms. The performed simulations reveal that MSDRF improves energy exhaustion and the standard deviation of the energy of nodes by 31.8% and 39% compared to the previous algorithms.