Wang Ning, Li Ya, Li Yuanbang, Nie Huanxin
School of Computer Science and Technology, Zhoukou Normal University, ZhouKou, 466001, Henan, China.
School of Network Engineering, Zhoukou Normal University, ZhouKou, 466001, Henan, China.
Sci Rep. 2025 May 29;15(1):18844. doi: 10.1038/s41598-025-02529-3.
With the development of better communication networks and other related technologies, the IoT has become an integral part of modern IT. However, mobile devices' limited memory, computing power, and battery life pose significant challenges to their widespread use. As an alternate, mobile cloud computing (MCC) makes good use of cloud resources to boost mobile devices' storage and processing capabilities. This involves moving some program logic to the cloud, which improves performance and saves power. Techniques for mobility-aware offloading are necessary because device movement affects connection quality and network access. Depending on less-than-ideal mobility models, insufficient fault tolerance, inaccurate offloading, and poor task scheduling are just a few of the limitations that current mobility-aware offloading methods often face. Using fault-tolerant approaches and user mobility patterns defined by a Markov chain, this research introduces a novel decision-making framework for mobility-aware offloading. The evaluation findings show that compared to current approaches, the suggested method achieves execution speeds up to 77.35% faster and energy use down to 67.14%.
随着更好的通信网络和其他相关技术的发展，物联网已成为现代信息技术不可或缺的一部分。然而，移动设备有限的内存、计算能力和电池寿命对其广泛应用构成了重大挑战。作为一种替代方案，移动云计算（MCC）充分利用云资源来提升移动设备的存储和处理能力。这涉及将一些程序逻辑转移到云端，从而提高性能并节省电量。由于设备移动会影响连接质量和网络访问，因此需要具备移动感知卸载技术。基于不太理想的移动模型、容错能力不足、卸载不准确以及任务调度不佳等问题，这些只是当前移动感知卸载方法经常面临的部分局限性。本研究利用由马尔可夫链定义的容错方法和用户移动模式，引入了一种用于移动感知卸载的新型决策框架。评估结果表明，与当前方法相比，所提出的方法执行速度快了77.35%，能源消耗降低至67.14%。
