Energy-Aware Sensor Scheduling and Opportunistic Transmission for State Estimation in Industrial IoT Systems

The wide application of Internet of Things (IoT) technology in industrial automation promotes the emergence of industrial IoT systems, where state estimation plays a critical role in accurately tracking the state of control system and enabling informed decisions and appropriate actions. State estimation is generally performed with the sensory data delivered over IoT. Therefore, the transmission performance of sensory data plays a crucial role in the accuracy of state estimation. However, it is challenging to ensure the required transmission reliability for state estimation due to spectrum scarcity, energy limitation, and the harsh industrial radio environment. To address this issue, we propose an energy-aware sensor scheduling and opportunistic transmission scheme to balance the estimation performance and resource consumption. In particular, we define a novel performance metric, estimation error ratio, to evaluate the impact of sensor scheduling on estimation performance. Then, a spectrum and energy resources constrained minimization problem is formulated to reduce the weighted sum of estimation error and energy consumption. In order to effectively solve the non-convex problem, an estimation error ratio-based algorithm is designed to determine the scheduled sensors. Moreover, we propose an iterative method to obtain the near-optimal solution for the opportunistic transmission problem. Finally, simulation results demonstrate that the proposed scheme can effectively reduce energy consumption with the estimation accuracy guarantee.

I. Introduction

The Internet of Things (IoT) is a network of interconnected objects that can efficiently exchange information [1]. Its ability to facilitate low-latency data collection, analysis, and decision-making promotes its application in many industries, such as healthcare, manufacturing, and agriculture [2]–[5]. The wide application of IoT technology in industrial automation promotes the emergence of industrial IoT systems [6]. In industrial IoT systems, sensors are widely deployed in industrial plant to collect sensory data for performing state estimation. Therefore, the accuracy of state estimation heavily depends on the data transmission performance. However, the harsh industrial environment and unreliable wireless channels make it challenge to support the required transmission performance for state estimation. Therefore, how to enhance the estima tion performance with imperfect and limited communication resource is critical for industrial IoT systems.