چكيده به لاتين
Today, with the development of next-generation communication networks and the advancement of Internet of Things (IoT) devices, we are witnessing a significant transformation in the emergence of new applications and use cases. Constraints in size, computational power, and energy of IoT devices are among the fundamental challenges of this platform, leading devices to lack the capability to execute programs that require extensive computational resources and minimal latency. Edge computing emerges as a promising architecture and technology to reduce latency, improve energy efficiency, and enhance the performance of IoT devices. This architecture improves the capacity of IoT devices for executing practical applications by providing computational and storage resources at the edge of the network and closer to the user. Despite recent advances in telecommunication networks, rural and mountainous areas still lack sufficient quality. Moreover, in urban areas, during peak traffic times, telecommu nication networks fail to deliver the necessary user experience while avoiding communication delays. Therefore, one proposed solution is the use of non-fixed stations such as drones, which have increasingly gained attention from industry and researchers over the past decade. In addition to previous military applications, drones are now being utilized in various industrial and commercial applications. Furthermore, due to the mobility characteristics of drones, they can be used as aerial base stations to increase coverage, capacity, and reliability in these networks and improve system flexibility. Considering the advantages of edge computing, equipping drones with computational and storage resources can be utilized to offload computational tasks from IoT devices to drones to address the challenges of IoT devices. However, the use of edge computing-equipped drones presents challenges such as optimal drone selection for task offloading, simultaneous path planning for drones, and task offloading decision-making, along with energy efficiency considering the battery constraints of drones. Despite the consideration of simultaneous task offloading and path planning in existing works, most of these works do not prioritize energy efficiency. Additionally, existing works in energy modeling only consider the energy consumed by drones and do not consider the energy consumed by IoT devices for task offloading or local execution of remaining tasks. In this research, we first utilize a real-time intelligent localization algorithm to obtain accurate location infor mation of IoT devices for precise computational support. Then, we model the simultaneous path planning of drones and task offloading to maximize the overall system energy efficiency. Furthermore, to solve the proposed problem, an efficient path planning and task offloading algorithm based on the efficient coverage set of drones is proposed. To obtain this set and maximize system efficiency, communications and consensus among IoT devices are utilized. evaluation results of the proposed method show that it improves energy efficiency by up to 137% and reduces energy consumption by up to 28% compared to previous works.
