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Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme.

Low earth orbit (LEO) satellite network has the advantages of comprehensive coverage and has the unique benefits of short satellite-to-ground transmission distance and low construction cost, which can effectively complement the limited coverage of ground mobile communication network. Hence, LEO satellites gain extensive attention in the field of mobile communication. However, restricted by the existing mode (BP, Bent Pipe) of high transmission latency problems in the LEO space satellite computing (LSSC), it is challenging to meet the low latency requirement of time-sensitive tasks. Therefore, on-orbit collaborative computing technology for LSSC is proposed in this paper. While due to the high dynamic and non-centrality of the LEO satellite system, collaborative computing in satellite networks will face many difficulties. Aiming at the high dynamic and non-centrality of the LEO satellite network, this paper proposes a dispersed computing paradigm for the LEO satellite network, which is suitable for high dynamic no-center scenarios. In this dispersed computing paradigm, a steady-state matrix based on the time-expanded graph (TEG) model is introduced in this paper to steady-state the topology of a high dynamic LEO satellite network. According to the matrix, a transmission capacity and computing capacity based diffusion algorithm (TCGDA) is proposed to perform optimal task allocation in the LEO satellite network. The simulation results show that the proposed dispersed computing method can effectively complete the computing task with the optimized latency.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for Internet of Things (IoT). Constrained by the limited computation and battery capabilities of IoT devices, computational-intensive tasks are difficult to be tackled locally. In this paper, a UAV-aided mobile edge computing (UAMEC) system is constructed to assist IoT devices to tackle computational-intensive tasks. Furthermore, we jointly optimize communications, computing and caching resources allocation strategies for the sake of maximizing the net revenue from the UAMEC under the condition of guaranteeing user’s quality of experience (QoE). A multi-dimensional hybrid adaptive particle swarm (MHAPSO) algorithm is conceived to solve this joint optimization problem. Finally, the effectiveness and superiority of our proposed scheme are demonstrated.

Intelligent emergency rescue equipment increasingly plays an important emergency rescue, which can greatly enhance the capability and efficiency of the rescue activities. In this paper, we construct a multi-UAV enabled latency and reliability guaranteed mobile edge computing framework (LR-UMEN) to provide on-demand computing services, where both the latency and the reliability are considered. Moreover, being different from the existing work about the latency and reliability, we propose a joint optimization of communications, computing and caching to decrease the latency as well as enhance the reliability relying on the novel definition of ‘expected latency’, which effectively benchmarks the characteristic of both the latency and reliability considered. Finally, a hybrid binary particle swarm optimization (HBPSO) algorithm is conceived for solving the non-convex optimization problem, followed by a range of simulations.