Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach

Nowadays, wireless sensor networks (WSNs) are the central means for data gathering in the edge-based internet of things era. Mobile edge nodes, such as mobile sinks (MSs), are the most reasonable way to gather the sensed data in these WSNs by travelling through rendezvous points (RPs) across the region of interest. Nevertheless, effective path planning for the MS is a significantly challenging issue, considering the questions: (1) How many RPs are optimal, and what should be their locations, (2) what is the sequence of RPs followed by the MS to form a path? According to the existing literature, this problem is NP-hard in nature, and no existing solution provides the best solution so far. In this paper, we address the above-mentioned questions and design an efficient scheme for path planning of the MS. The proposed scheme determines an optimal number of RPs using a variable dimension shark smell optimization-based approach. It uses the concept of adaptive search space dimension. The optimization procedure begins with a randomly chosen problem dimension, adapts as the swarm progresses, and then chooses an optimal dimensional space. The scheme has been extensively simulated to show superior performance over other competing schemes in terms of various parameters. Particularly, the proposed scheme achieved 16.79% and 6.29% improvement over the competing schemes in terms of energy consumption and network lifetime, respectively.