A Throughput and Priority Optimization Strategy for High Density Healthcare IoT

Abstract

In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4.