Collaborative cloud–edge task scheduling scheme in the networked UAV Internet of Battlefield Things (IoBT) territories based on deep reinforcement learning model

Abstract

Multiaccess cloud–edge computing (MCEC) represents a burgeoning technology facilitating the delegation of mobile application tasks, especially those demanding swift processing and substantial computational capabilities, to cloud data centers. The intricate maneuvering of unmanned aerial vehicles (UAVs) in interconnected combat cloud systems poses a noteworthy challenge in determining the optimal distribution of task offloading. Uneven task allocation to specific UAVs could result in heightened latency and diminished reliability. We consider combat cloud networks over various regions, each with numerous edge servers that will be connected to different independent UAVs over high-speed links, to handle latency-sensitive and compute-intensive tasks in three possible offloading alternatives: using the nearest edge server, using neighboring edges, and using far-cloud resources. The contribution of this work is a two-step procedure involving reinforcement learning (RL) technique to handle the challenge of cloud–edge servers’ task allocation and to determine the most effective offloading approach that minimizes latency, maximizing reliability. First, it deals with the issues related to task distribution in combat cloud systems, centered on optimizing the balance between latency and reliability in case of task delegation to UAVs. It involves making strategic decisions on when and where tasks can be migrated by considering the mobility of the unmanned aerial systems. The second contribution is based on performing an RL algorithm in a collaborative UAV cluster. Compared with the other two methods, our algorithm improves the latency by about 20%–40% and enhances reliability by about 13%–28% in terms of non-violation of QoS constraints.