Lincong Zhang1, Yuqing Liu1, Kefeng Wei2, Weinan Zhao1, Bo Qian1,*
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069108
- 10 November 2025
Abstract In the field of edge computing, achieving low-latency computational task offloading with limited resources is a critical research challenge, particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications. In scenarios where edge servers are sparsely deployed, the lack of coordination and information sharing often leads to load imbalance, thereby increasing system latency. Furthermore, in regions without edge server coverage, tasks must be processed locally, which further exacerbates latency issues. To address these challenges, we propose a novel and efficient Deep Reinforcement Learning (DRL)-based approach aimed at minimizing average… More >