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Data Offloading for Edge-Enabled Smart City Services via Location-Functionality Correlation Analysis
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AI
DOI：10.1109/TSC.2025.3623180.png)
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Abstract
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Mobile edge computing significantly reduces the delay for smart city services by offloading the related datasets on the edge server. The correlation among smart city datasets regarding their location and functionality plays a crucial role in determining the data offloading strategy. The more closely related these datasets are, the higher the probability they will be requested simultaneously. However, most existing methods often ignore the impact of location-functionality correlation on the datasets’ access frequencies. If datasets with high correlation are offloaded on separate edge servers, it will increase cross-server delay. Therefore, we proposed a method to analyze location-functionality correlation among smart city datasets and optimize data offloading strategy. Then, we formalize a data offloading model to optimize the total expected profits, which is an NP-hard problem. To solve this problem, we propose a pruned Q-learning for data offloading algorithm that learns the location-functionality correlation among the datasets. To validate its effectiveness, we first conduct simulation experiments to validate the offloading strategy under both complex service scenarios and large-scale datasets. Furthermore, we implement a data offloading architecture based on SuperMap iServer. The experimental results demonstrate that our algorithm reduces the delay and energy consumption by 34.65% and 25.54%, respectively, compared to the baseline algorithms.
Keywords:
Edge computing
data offloading
smart city
Q-learning
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