Sustainable Energy-Efficient Multi-Objective Task Processing Based on Edge Computing

As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data leakage, especially in dense urban areas. This paper presents a low-latency, energy-efficient digital twin (DT) architecture tailored for smart cities, integrating edge computing (EC) and multiple s (IRS) to enhance communication. Dynamic voltage and frequency scaling (DVFS) technology is considered for user devices to reduce energy consumption. To mitigate the risk of user privacy leakage during task offloading, we address sensitive user location data that may be exposed by proposing a perturbed sliding task queue (PSTQ) algorithm based on differential privacy (DP), and demonstrate the effectiveness of the algorithm. To optimize task processing time and energy efficiency, we decompose the complex problem using block coordinate descent and propose an intelligent scheduling for energy sustainability (ISES) algorithm based on Karush-Kuhn-Tucker conditions and deep reinforcement learning (DRL). Experimental results demonstrate that our proposed architecture and algorithms achieve over 90% improvement in key optimization objectives, alleviating the computational pressure on existing devices while significantly enhancing task processing efficiency and energy sustainability.