Completion Time Minimization in Wireless-Powered UAV-Assisted Data Collection System

In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this letter, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms

Joint Optimization of UAV Trajectory and Sensor Uploading Powers for UAV-assisted Data Collection in Wireless Sensor Networks

In this paper, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: the serving orders for sensors and the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes

Number and Operation Time Minimization for Multi-UAV Enabled Data Collection System with Time Windows

In this paper, we investigate multiple unmanned aerial vehicles (UAVs) enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer non-convex problem, we decompose the problem into two subproblems: UAV trajectory and hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed MACO-based algorithm, is given by leveraging MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm

Device association and trajectory planning for UAV-assisted MEC in IoT: a matching theory-based approach

Availability of data and materials: Not applicable.Copyright © The Author(s) 2023. Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.National Natural Science Foundation of China under Grant 61971421 and Grant 62132004; Quzhou Government under Grant 2021D003; Sichuan Major Research and Development Project under Grant 22QYCX0168

Sensor clustering using a K-means algorithm in combination with optimized unmanned aerial vehicle trajectory in wireless sensor networks

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.Web of Science234art. no. 234

Time allocation and optimization in UAV-enabled wireless powered communication networks

Unmanned aerial vehicles (UAVs) have attracted great research attention due to their flexibility. In this paper, the use of UAVs in wireless sensor networks as an energy transmitter and a data collector is investigated. The UAV is first charged from a charging station, such as a base station (BS), before it flies to the sensors for data collection. Upon arrival, the UAV first charges the sensors via wireless power transfer (WPT) in the downlink, followed by data transmission from the sensors in the uplink. After that, the UAV flies back to the BS to offload data to the BS. We aim to maximize the amount of data offloaded to the BS by allocating optimal time slots to different tasks in this process, given a fixed total time. The maximization is solved in two steps as two convex optimization problems. In the first step, the time allocation between WPT to sensors and data collection from sensors is optimized. In the second step, the time allocation of BS charging, the total time in the first step, and BS data offloading is optimized. Unlike the previous works, our study takes into account the charging process from the BS to the UAV, the propulsion consumption at the UAV and the data offloading process to the BS. Both distance-dependent path loss and small-scale fading are considered. Numerical results show that the optimal time allocation can maximize the amount of data at the BS without wasting any time and energy

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects