TTGN: Two-Tier Geographical Networking for Industrial Internet of Things With Edge-Based Cognitive Computing

Abstract

Industrial Internet of Things (IIoT) is based on data acquisition and data analytics technologies. A variety of and a large amount of data is collected at management nodes with computing and storage capacities. Recently, the computing ability, denoted by the edge, has been located closer to the service fields to achieve faster and more reliable data-driven service provisioning. Edge computing is a useful resource to facilitate smart manufacturing based on IIoT. Since the current Industrial Wireless Sensor Networks (IWSNs) technologies for IIoT do not perfectly cover all the demands of industries with smart manufacturing such as agile flexibility with asset movement. A major future demand for IWSNs should be to support the mobility of assets in a wireless environment. This paper investigates the shortages of current technologies such as WirelessHART and proposes a novel wireless networking scheme based on edge-based cognitive computing in order to support reliable and low latency communication of mobile assets, which is involved in the smart manufacturing processes. We devise a two-tier geographical networking (TTGN) system that supports position-based mobility detection and networking. Also, resource allocation for the reliable and real-time has been obtained based on the game theory. Evaluation results demonstrate that TTGN can guarantee a high data transmission success ratio, as well as a fast delivery ratio for link path establishment.