An Autonomous Diagnostic Tool for the WirelessHART Industrial Standard

Over the last years, Wireless Sensor Networks (WSN) went from being a promising technology for countless industrial applications to a de facto technology used in todays' applications. WSNs have been gaining momentum over costly wired technologies, offering low installation costs, self-organization, and added functionality. As a consequence of their enormous potential, WSNs were subject to standardization and some industrial standards and open source solutions like WirelessHART, Zigbee, ISA100, IEEE802.15.4 and OpenWSN were announced. However, despite considerable efforts to provide mechanisms that increase the availability, reliability, security and maintainability of this type of networks, WSNs have kept one of their main characteristics: fault-proneness. As a result, the offer of postdeployment diagnostic tools has been increasing in the last decade in order to diagnose WSN failures as soon as possible. Nevertheless, current WSN diagnostic tools still have many limitations and cannot be considered ready to use in real-world scenarios. In this paper we present an autonomous diagnostic tool that addresses these limitations in a real industrial Internet of Things (IIoT) scenario. Our tool is based on simple metrics, a logging tool, a data-mining algorithm, and available network metrics, and it monitors the condition of the sensor nodes firmware, hardware and the network itself. The proposed demonstration was tested and validated using the WirelessHART IIoT standard.