Sustainable and Optimized Data Collection via Mobile Edge Computing for Disjoint Wireless Sensor Networks

With the ever-increasing demand for Internet of Things (IoT) applications, wireless sensor networks (WSNs) have become the central means to disseminate data for analysis in the era of mobile edge computing. Mobile sinks (MSs) as edge nodes have emerged as an efficient solution to the performance enhancement of WSNs. One important task of the MSs is to collect data in a sustainable and optimized manner by visiting certain rendezvous points (RPs) inside the WSN. However, most existing works focus only on connected WSNs, while disjoint networks are the reality in many IoT applications. Moreover, none of them have considered a realistic propagation model. They have also ignored optimizing both the number of RPs and MSs. This paper proposes a novel data collection scheme while paying attention to all these issues. The scheme is specially designed for delay-harsh applications. First, we propose a convex hull-based algorithm to determine RPs for constructing an optimal tour of a MS. Then using the resulting set of RPs, we present another algorithm based on the Jaya metaheuristic to determine an optimal number of MSs and their balanced tours. Rigorous simulations show that our scheme outperforms existing algorithms in terms of various performance metrics.