The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these challenges by placing computing servers close to end users. Despite extensive research on MEC, optimizing network topology to improve computational efficiency remains underexplored. Recognizing the critical role of network topology, we introduce a novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation. Inspired by communication and sensor networks, DNTD-TO efficiently constructs three-layered network structures for task offloading and generates optimal task allocations for these structures. Comparisons with existing topology design methods demonstrate the promising performance of our approach.

翻译：延迟敏感但计算密集的物联网（IoT）应用日益增多，而物联网设备有限的处理能力带来了挑战。移动边缘计算（MEC）通过将计算服务器部署在靠近终端用户的位置，为解决这些挑战提供了一种前景广阔的方案。尽管已有大量关于MEC的研究，但通过优化网络拓扑以提高计算效率的探索仍显不足。认识到网络拓扑的关键作用，我们提出了一种新颖的面向任务卸载的去中心化网络拓扑设计策略（DNTD-TO），该策略联合考虑了拓扑设计与任务分配。受通信网络和传感器网络的启发，DNTD-TO能够高效构建用于任务卸载的三层网络结构，并为这些结构生成最优的任务分配方案。与现有拓扑设计方法的比较证明了我们方法的优异性能。