Suganya B, Gopi R, Kumar A Ranjith, Singh Gavendra
Faculty of Artificial Intelligence & Data Science, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, 621112, India.
Faculty of Computer Science & Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamil Nadu, 621212, India.
Sci Rep. 2024 Jul 16;14(1):16383. doi: 10.1038/s41598-024-67285-2.
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.
资源优化、及时的数据捕获以及高效的无人机操作对于任务成功至关重要。延迟、带宽限制和可扩展性问题是传统集中式处理架构所面临的问题。此外，在保护数据隐私和安全的同时，优化地面站与无人机之间的稳健通信本身就是一项艰巨的任务。本研究采用边缘计算基础设施、人工智能驱动的决策制定和动态任务卸载机制，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）算法相结合，以减少延迟、提高任务效率并节省机载资源。该系统根据距离、可用资源和任务的紧迫性，将计算任务动态分配给边缘节点和无人机。动态任务卸载边缘感知实现框架（DTOE-AIF）将AI算法与边缘计算相结合，从而减少了延迟、提高了任务效率并节省了机载资源。DTOE-AOF在许多领域都很有用，例如精准农业、应急管理、基础设施检查和监测。配备了DTOE-AOF的人工智能驱动的无人机可以迅速勘查损失情况、寻找幸存者并展开救援任务。通过将DTOE-AOF与传统的集中式方法进行比较，全面的仿真研究证实它提高了任务效率、响应时间和资源利用率。
