A DQN-Based Edge Offloading Method for Smart City Pollution Control

Abstract

Smart city pollution control is fundamental to urban sustainability, which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring. Generally, monitoring data needs to be transmitted to centralized servers for pollution control service determination. In order to achieve highly efficient service quality, edge computing is involved in the smart city pollution control system (SCPCS) as it provides computational capabilities near the monitoring devices and low-latency pollution control services. However, considering the diversity of service requests, determination of offloading destination is a crucial challenge for SCPCS. In this paper, A Deep Q-Network (DQN)-based edge offloading method, called N-DEO, is proposed. Initially, N-DEO employs neural hierarchical interpolation for time series forecasting (N-HITS) to forecast pollution control service requests. Afterwards, an epsilon-greedy policy is designed to select actions. Finally, the optimal service offloading strategy is determined by the DQN algorithm. Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.