Distributed carbon emission prediction in edge environment considering spatial-temporal context factors

The increasing demand for real-time carbon emission monitoring in distributed edge environments has highlighted the need for accurate prediction models that consider spatial and temporal context factors. Traditional centralized prediction models face significant challenges in processing large-scale emission data in real-time, especially when dealing with fluctuating data from diverse locations and time intervals. These challenges are further compounded by the need for privacy protection when handling sensitive environmental data. To address these issues, this paper presents a distributed approach to carbon emission prediction in edge environments, incorporating spatial-temporal context factors such as location and time of day. By leveraging locality-sensitive hashing (LSH) techniques, we propose a privacy-preserving model that enables efficient prediction across edge nodes while maintaining user privacy. Experimental results demonstrate the feasibility and effectiveness of the proposed method in accurately predicting carbon emissions, with a significant reduction in computational overhead compared to centralized models. This work provides a practical solution for real-time carbon emission prediction and privacy-preserving environmental monitoring in distributed systems.