Optimal data sink location for real-time NOMA-based Industrial IoTs

Abstract

Real-time performance is one of the most vital metrics for applications in Industrial Internet of Things (IIoTs), and the relative geographic relationship between data sink and wireless sensors has great influence on the real-time performance. Since the locations of wireless sensors are in generally fixed in IIoTs, setting reasonable location for data sink is an efficient way for improving the real-time performance. In this paper, we investigate Non-Orthogonal Multiple Access (NOMA) based IIoTs, and consider how to minimize average access delay by setting suitable location for data sink. We formulate the problem and present an algorithm by mapping the problem into the classic minimum chain covering problem, and make the problem algorithm-tractable. Simulation results reveal that due to the full exploitation of NOMA parallelism, average access delay decreases more than 60% for some typical settings, and it can even reach 70% for the linear network topology.