Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks (WSNs) . Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay — referred to as the wait-for delay  — which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

Energy consumption and completion time are two hot issues in UAV (Unmanned Aerial Vehicles) assisted edge computing.The current research is mainly focused on the interaction between UAVs and edge servers, while the interplay between UAVs is rarely considered.In this paper, in the considered scenario with multiple UAVs and servers, the UAVs may possess idle resources, and thus function as temporary servers to provide task offloading services for other UAVs.We begin by formulating a multi-objective joint optimization problem, which aims to balance the energy consumption and time delay in order to maximize the benefits of the system. Then we use a multi-objective genetic algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to iteratively find the optimal offloading strategy.Through numerical simulation, the results obtained from the numerical simulation demonstrate the superiority of the proposed method in achieving the Pareto optimal frontier that balances energy consumption and completion time.

Recently, adopting UAVs equipped with the edge computing platform to provide computing service has been considered as a promising approach for resource-limited devices in mobile edge computing (MEC). Unfortunately, the limited resources (e.g., energy, computing and communication) of the UAV may significantly restrict its service capability, which means it has to selectively provide task offloading service to achieve the maximal benefit. In this article, aiming at optimizing the overall benefit of the UAV in a single dispatch, we propose an approximate Benefit Maximizing Task Offloading (BMTO) algorithm, which jointly considers the trajectory scheduling of the UAV and the offloading strategy of tasks. Specially, the flight path of the UAV is decomposed into several hover sites, which are selected by a benefit-cost approach. And the offloading sequence of tasks is arranged to maximize the benefit of the UAV through a surrogate function, which is proved to be a nonnegative monotone submodular function. Thus we transform the original problem into a submodular maximization problem and theoretically prove that BMTO owns an approximation ratio of 1 results show that our proposed algorithm outperforms the benchmark algorithms in terms of total benefit as well as energy efficiency ratio.

Recently, adopting UAVs equipped with the edge computing platform to provide computing service has been considered as a promising approach for resource-limited devices in mobile edge computing (MEC). Unfortunately, the limited resources (e.g., energy, computing and communication) of the UAV may significantly restrict its service capability, which means it has to selectively provide task offloading service to achieve the maximal benefit. In this article, aiming at optimizing the overall benefit of the UAV in a single dispatch, we propose an approximate Benefit Maximizing Task Offloading (BMTO) algorithm, which jointly considers the trajectory scheduling of the UAV and the offloading strategy of tasks. Specially, the flight path of the UAV is decomposed into several hover sites, which are selected by a benefit-cost approach. And the offloading sequence of tasks is arranged to maximize the benefit of the UAV through a surrogate function, which is proved to be a nonnegative monotone submodular function. Thus we transform the original problem into a submodular maximization problem and theoretically prove that BMTO owns an approximation ratio of 1/2(1-1/e). Simulation results show that our proposed algorithm outperforms the benchmark algorithms in terms of total benefit as well as energy efficiency ratio.