Joint Task Scheduling and Resource Allocation for UAV-Assisted Air-Ground Collaborative Integrated Sensing, Computation, and Communication

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.