PDF(Pubmed)
Abstract：
Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)\'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.
摘要：
资源优化,及时捕获数据，高效的无人机（UAV）操作对于任务的成功至关重要。延迟,带宽限制，和可伸缩性问题是传统集中式处理架构遇到的问题。此外,优化地面站和无人机之间的可靠通信，同时保护数据隐私和安全本身是一项艰巨的任务。采用边缘计算基础设施，人工智能驱动的决策，和动态任务卸载机制，本研究提出了用于无人机操作优化的动态任务卸载边缘感知优化框架(DTOE-AOF)。边缘计算和人工智能（AI）算法集成以减少延迟，提高任务效率，并节约机载资源。该系统根据接近度将计算任务动态分配给边缘节点和无人机，可用资源,以及任务的紧迫性。减少延迟，提高任务效率，动态任务卸载边缘感知实现框架（DTOE-AIF）将AI算法与边缘计算集成，从而实现了板载资源节约。DTOE-AOF在许多领域都很有用，比如精准农业，应急管理,基础设施检查，和监测。由AI驱动并配备DTOE-AOF的无人机可以迅速调查损坏情况，寻找幸存者,并启动救援任务。通过将DTOE-AOF与常规集中式方法进行比较，彻底的仿真研究证实，它提高了任务效率，响应时间,和资源利用。
