Dynamic Data Collection for AAV-Assisted Green Industrial IoT

Abstract

Autonomous aerial vehicles (AAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of AAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, AAV speed variation brings challenges to achieving green communication and reliable data collection. In this article, we propose a dynamic AAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of AAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize AAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and AAV speed are jointly optimized for minimizing the long-term energy consumption of AAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed AAV speed scheme.