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This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) is considered as a promising approach to tackle soaring computing requirements. The broadcast nature of air‐to‐ground (A2G) links makes UAV communications vulnerable to eavesdroppers, so secure UAV communications remain an open question. This paper proposes a secure communication scheme for reconfigurable intelligent surface (RIS)‐assisted UAV‐MEC systems, in which the RIS assists the user in offloading data to the legitimate UAV, and the legitimate UAV provides computing services to the user. To fully expand the security computing capacity of the system, the communication link is improved by introducing RIS, and the jammer interferes with the eavesdropper. The secure computing capability of the system is maximized by optimizing communication resources and trajectories. Since the proposed problem is non‐convex, successive convex approximation (SCA) technique and block coordinate descent (BCD) technique is combined to solve the problem. The simulation results show that the proposed scheme in this paper can effectively improve the system secure computing bits compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

With the massive application of unmanned aerial vehicles (UAVs) in disaster relief, fire protection, security and other fields, jamming countermeasures to UAVs in airports and competition venues have become the core link of security in high‐level matches and important places. The existing jamming sources are mainly single point jamming. Due to the shielding of buildings, it is impossible to fully cover the venue, and the existing suppressed jamming will affect the communication broadcasting. Here, a distributed optimal jamming method for massive UAVs is proposed. Through the identification and positioning of massive UAVs, using the location information of distributed jamming sources, the UAV jamming architecture based on satellite navigation is constructed. Combined with tabu search artificial bee colony theory, the distributed optimal jamming of UAVs is realized, which solves the problems of few jamming targets, slow speed and low success rate of the existing optimal jamming methods. The proposed method is compared with the existing methods in three aspects: communication frequency, jamming trajectory deviation and optimization speed. The simulation results show that the proposed method effectively improves the jamming success rate and jamming speed and can meet the application requirements of massive UAV jamming in the region. [ABSTRACT FROM AUTHOR]