Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks

Abstract

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.