Cooperative UAV Trajectory Design and Resource Allocation in Blockchain-Enabled Secure Aerial Edge Computing Network

Mobile Mdge Computing (MEC) has emerged as a crucial technology for supporting computation-intensive and latency-sensitive Internet of things (IoT) applications. Meanwhile, UAVs can serve as MEC servers, providing cost-effective computation offloading services to IoT terminals with their flexible deployment capabilities, especially in areas lacking ground infrastructure. Nevertheless, the computation offloading process suffers from potential security risks, while the randomness and uncertainty of terminals’ data sensing may exacerbate the queue backlogs. To tackle these challenges, a UAV-enabled secure aerial computing network that integrates MEC and blockchain is proposed, with the aim of jointly designing data sensing, offloading, and computing, together with UAV trajectory planning to maximize the long-term average data sensing rate under queuing delay and block creation delay constraints. To address the coupling between long-term constraints and short-term decisions, we apply Lyapunov optimization to decompose the original problem into three deterministic subproblems for each time slot. We then develop a multi-agent learning-based approach to collaboratively train terminal transmission power and UAV flight trajectories. Moreover, sensing rate and edge resource allocation are adaptively optimized in response to real-time data arrivals and queue backlogs. Simulation results demonstrate the superior performance of our solution, achieving over a 13.16% improvement in data sensing rate and more than a 29.47% reduction in queue delay compared to benchmark methods.