In wireless sensor networks (WSNs), utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs’ wake-up schedule and UAV’s trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.

Completion time and energy consumption of the UAV are two important design aspects in UAV-enabled applications. In this paper, we consider a UAV-enabled mobile edge computing (MEC) system for Internet of Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices’ task and energy budget constraints. We first consider the UAV energy minimization problem without pre-determined completion time, a discretized non-convex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is non-convex and challenging to solve. By using the same path discretization approximation model to reformulate problem, similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the trade-off between the UAV energy and completion time. Simulation results are provided to corroborate our study and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.

Energy consumption is one important issue for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode). For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min-max multiple Travelling Salesman Problem (min-max m-TSP) and convex optimization techniques. Next, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode). By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush-Kuhn-Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. Simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost, which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost, which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

In this letter, we study unmanned aerial vehicle (UAV) enabled Internet-of-Things (IoT) system, where a rotary-wing UAV is employed to collect data from a set of IoT devices. By introducing the propulsion energy model for rotary-wing UAV, we aim to minimize the maximum energy consumption among all devices, while satisfying the UAV energy budget. The problem is formulated with a non-convex programming by jointly optimizing the communication scheduling and transmit power allocation for devices as well as the UAV trajectory. An efficient solution is proposed by applying the alternating optimization and successive convex approximation techniques. Simulation results show that the proposed solution can achieve significant gains over benchmark schemes for data collection over IoT system.

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet of Things (IoT) applications, which are usually time sensitive and computation intensive. In this article, a novel design framework is proposed for a multi-UAV-enabled MEC system, where edge servers are equipped on multiple UAVs to provide flexible computation assistance to IoT devices with hard deadlines. The aim is to maximize the number of served IoT devices through jointly optimizing UAV trajectory and service indicator as well as resource allocation and computation offloading, where the chosen IoT devices will complete their computation tasks on time under given energy budgets and co-channel interference is taken into account. We formulate the optimization problem as a mixed integer nonlinear programming (MINLP), which is challenging to solve directly. The problem is first reformulated to a more mathematically tractable form by adding a penalty term to the objective function. We then decouple the problem into two subproblems and develop an iterative algorithm by solving the two subproblems with alternating optimization and successive convex approximation techniques, where the proposed algorithm converges to a Karush–Kuhn–Tucker (KKT) solution. In addition, an efficient initialization scheme is proposed based on multiple traveling salesman problem with time windows (m-TSPTWs) method. Finally, simulation results are provided to demonstrate that the proposed joint design achieves significant performance gains over baseline schemes.