Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A DQN-Based Edge Offloading Method for Smart City Pollution Control
AU - Xu, Jiajie
AU - Xiang, Haolong
AU - Zang, Shaobo
AU - Bilal, Muhammad
AU - Khan, Maqbool
AU - Cui, Guangming
PY - 2025/4/29
Y1 - 2025/4/29
N2 - 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.
AB - 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.
U2 - 10.26599/tst.2024.9010105
DO - 10.26599/tst.2024.9010105
M3 - Journal article
VL - 30
SP - 2227
EP - 2242
JO - Tsinghua Science and Technology
JF - Tsinghua Science and Technology
SN - 1007-0214
IS - 5
ER -