AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

Hu, Huimin; Xiong, Ke; Qu, Gang; Ni, Qiang; Fan, Pingzhi; Letaief, Khaled B.

doi:10.1109/jiot.2020.3012835

Cited by 247 publications

(127 citation statements)

References 48 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…Data can be sent from sensor nodes to the base stations and finally transmitted to the data center in IoT application scenarios. However, in some challenging scenarios without the infrastructure networks like desert and ocean [26], collecting data with unmanned aerial vehicle (UAV) [1], [2], [3] is obvious the choice of solution. In such data collection scenarios, the process of collecting data from a sensor node is shown in FIGURE 1 (a).…”

Section: A Background and Motivationmentioning

confidence: 99%

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Pan

Yang

2021

IEEE Access

View full text Add to dashboard Cite

To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

show abstract

Section: A Background and Motivationmentioning

confidence: 99%

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Pan

Yang

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Data collection from desired regions, while avoiding forbidden regions and reaching the destination [43] min Time Joint optimization of the data collection intervals, the UAV's speed, and the sensors' transmit powers [44] max QoI UAV's trajectory, the time required for energy harvesting and data collection for each SN, are jointly optimized [45] [55] min Time Joint optimization of the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring successful data upload with a given energy budget [56] min Cost Joint optimization of the UAV trajectory with wake-up time allocation and the transmit power…”

Section: Ref Objective Remarksmentioning

confidence: 99%

Joint Optimization of Sensors Association and UAVs Placement in IoT Applications With Practical Network Constraints

2021

View full text Add to dashboard Cite

“…Hu et al formulated an optimization problem to minimize the average Age of Information of the data collected from all ground sensor nodes. They used Karush-Kuhn-Tucker (KKT) conditions to find the optimal energy transfer and the data collection time allocation; through this, a UAV's trajectory planning was obtained by dynamic programming and an ant colony heuristic algorithm [33]. Considering the UAV's hovering inaccuracy on received power at ground deployed sensor nodes, Suman et al proposed a hovering inaccuracy-aware optimal charging system design algorithm to find the optimal transmit power, hovering altitude, and antenna exponent [34].…”

Section: Related Workmentioning

confidence: 99%

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Qin

Zhao

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

show abstract

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

Cited by 247 publications

References 48 publications

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Joint Optimization of Sensors Association and UAVs Placement in IoT Applications With Practical Network Constraints

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Contact Info

Product

Resources

About