Bus network assisted drone scheduling for sustainable charging of wireless rechargeable sensor network ☆
Introduction
Wireless Rechargeable Sensor Network (WRSN) plays an important role in urban life of smart city due to its advantage of sustainable power supply by the wireless charger network [1] and/or harvesting energy from environment [2], such as solar energy and wind energy. WRSN has been applied in many fields [3], such as long-term environmental monitoring [4] and vehicular traffic control application [5].
However, sensor networks deployed in inaccessible outdoor environment, such as precipitation analysis in mountains [6] and water quality monitoring [7], may incur higher cost of deployment and maintenance of wireless chargers. In addition, it is difficult to harvest energy from environment by using solar cells and/or wind energy collector in many sensing applications such as structural monitoring under bridges [8] and monitoring soil conditions [9].
The sensor can obtain energy from the wireless charger embedded drone or Unmanned Aerial Vehicle (UAV) [10], and store the energy in its capacity. [11] explored the feasibility of charging the sensors using drones that can wirelessly transfer energy to the sensors. [12] used the dedicated chargers carried by drones that can fly over the sensor network and transmit energy to the sensors using radio-frequency signals. The drone-enabled wireless charging for WRSN can sustainably replenish energy for the sensors deployed in inaccessible outdoor environment without the deployment and maintain of wireless charger network.
Due to the limited battery capacity, the drone has to return back to the ground charging stations to replenish energy for itself. This increases the energy consumption of the drone flight and decreases the charging efficiency. Due to the limited energy capacity, it is difficult for drone to charge the sensors deployed in a vast area. How to charge the drone efficiently is an interesting and significant problem, and has attracted a lot of attention. The latest research [13] proposed a solution of drone charging by riding buses to continuously collect and communicate video streams from a large number of Points of Interests (PoIs) in urban areas. [14] designed a new EV charging system, which levers the bus network in urban areas through the integration of OnLine Electric Vehicle (OLEV) system [15] and Microwave Power Transfer (MPT) system. By leveraging the bus network, the drone can not only replenish energy by riding on buses, but also extend the range of charging service. Meanwhile, the bus has a large capacity battery that can sustainably collect the energy from OLEV system or its fuel engine, and therefore, has sufficient energy to charge the drone. Moreover, the buses provide pervasive charging opportunities for the drone because of the high popularity and wide coverage of bus network in urban areas.
However, the existing works studied drone-enabled wireless charging for WRSN [12] and drone scheduling using buses to charge the drone [13], separately. Actually, the energy between WRSN, drone and buses should be transferred efficiently to form a closed wireless charging system. Thus, we proposed a bus network assisted drone-enabled wireless charging system for WRSN in urban area. The introduction of bus network in the designed system not only accelerates the energy replenishment of drone, but also reduces the flying energy consumption of drone for charging WRSN. A toy example of our charging system is illustrated in Fig. 1. There are one drone, two buses and three sensors in the charging system. The drone can launch from any sensor and a set of fixed locations (termed landing points) on the bus routes. The buses have the regular schedules of themselves and can charge the drone when the drone rides the buses. The sensors can be charged by the drone. Then, the WRSN and the bus network together form a comprehensive network, which is consisted of sensors, landing points connecting road segments of bus routes, and flight segments between sensors and landing points. The drone rides the bus via the nearest landing point to the charged sensor for replenishing energy of itself from the bus and leaves the bus for charging the next sensor at some landing point when it has sufficient energy. Then the drone charges the sensor, and flies back to the nearest landing point to the charged sensor. Therefore, in this comprehensive network, the drone obtains the energy between any two landing points and consumes energy between any sensor and landing point.
In the designed system, the charging efficiency of drone and the sustainability of WRSN largely dependent on the drone scheduling. Unfortunately, to the best of our knowledge, there is no off-the-shelf bus network assisted drone scheduling for charging WRSN. We consider two drone scheduling scenarios according to the different requirements of sensing tasks. For the first scenario, we consider that the sensing tasks can tolerate some data loss, and allow the sensors to go to sleep for saving their energy. Thus, the drone scheduling is only constrained by the energy of drone in this case. In the second scenario, the sensing tasks require the continuous sensing data (such as vehicular traffic control application [5] and real-time environmental monitoring [16]), and therefore, the dead or dormancy of sensors will largely degrade the sensing quality. Thus, the drone scheduling is constrained by both energy of drone and deadlines of sensing tasks of sensors.
It is very challenging to schedule bus network assisted drone for sustainable charging of WRSN. First, it is impossible to obtain the traveling path of drone by solving the Traveling Salesman Path Probelm (TSPP) directly on the comprehensive network integrated by WRSN and bus network because our objective is to schedule drone for visiting the sensors only. Second, it is difficult to find the energy constrained shortest path of drone riding the buses from a sensor to the next sensor in the comprehensive network. This is because the drone may have the hybrid process of discharging and charging between any two sensors. Thus, the restricted shortest path algorithm [17] cannot be used to find our energy constrained shortest path straightforwardly because the restricted shortest path algorithm requires that the constrained metric should be non-negative. Moreover, the schedule of drone must ensure that each road/flight segment satisfies the energy constraint of drone, i.e., the residual energy of drone at the starting point of the road/flight segment is not less than the consumed energy passing through the segment. However, the residual energy of drone depends on the previously selected road/flight segments.
Our key contributions can be summarized as follows:
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We design a wireless charging system for WRSN through the bus network assisted drone in urban areas. To the best of our knowledge, we are the first to study the drone scheduling problem for such comprehensive wireless charging system.
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We formulate the problem of Drone Scheduling with Bus network (DSB) to minimize the time cost of drone for charging all sensors under the energy constraint of drone, and propose an approximation algorithm, Drone Scheduling Algorithm (DSA), to solve the energy tightened DSB problem.
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Considering the continuous sensing tasks of WRSN, we further formulate the Deadline Drone Scheduling with Bus network (DDSB) problem to maximize the number of charged sensors under the constraints of both energy of drone and deadlines of sensors, and we present an approximation algorithm, Deadline Drone Scheduling Algorithm (DDSA), to solve the energy tightened DDSB problem.
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We conduct extensive simulations and field experiments for the designed algorithms. The simulation results show that DSA can reduce the total time cost by 84.83% compared with Greedy Replenished Energy algorithm, and uses at most 5.98 times of the total time cost of optimal solution on average. Then, DDSA can increase the survival rate of sensors by 51.95% compared with Deadline Greedy Replenished Energy algorithm, and obtain 77.54% survival rate of optimal solution on average.
The rest of the paper is organized as follows. We review the state-of-art research in Section 2. We present the system model, formulate the DSB problem and present an approximation algorithm for the energy tightened DSB problem in Section 3. We formulate the DDSB problem and propose the approximation algorithm for the energy tightened DDSB problem in Section 4. We conduct the simulations and field experiments in Sections 5 Numerical experiments, 6 Field experiment, respectively. We conclude this work in Section 7.
Section snippets
Related work
The recent research on wireless charging for WRSN mainly aimed to solve the problems of wireless charger network deployment and management [18], [19], energy harvesting [20], [21], and route scheduling of drone and wireless charging vehicle [22], [23], [24].
Some works studied the deployment of wireless sensors and charging scheduling of wireless chargers. [18] solved the robust scheduling problem for wireless charger network by considering power jittering and rechargeable device drifting. [19]
System model and problem definition
We consider a sparse WRSN consisting of a set of rechargeable sensors with fixed known positions. There are a bus network and a drone that is responsible for charging the sensors in an urban area. The drone cannot fly directly between any two sensors because of the limited battery capacity, and must ride the buses to charge itself before charging any sensor. We consider that the sensing tasks can tolerate some data loss, therefore, the sensors can go to sleep to save their energy and prolong
Problem formulation
The continuous sensing application does not tolerate data loss. Therefore, the data quality will be deteriorated because of the data loss caused by the dead and dormancy of sensors. Due to the different energy consumption level of sensors, we consider that each sensor has a specified deadline. Generally, it is difficult to charge all sensors before their deadlines because of the limited flying speed and battery capacity of drone. A practical objective is to maximize the number of sensors
Numerical experiments
In this section, we conduct extensive simulations to verify the performance of our proposed algorithms with different number of landing points, energy requirement, number of sensors and deadlines of sensors.
Field experiment
In this subsection, we further evaluate the performance of OPT, DSA, DOPT and DDSA in the transportation network in Xianlin campus of NJUPT as shown in Fig. 9. Fig. 10 gives the test-bed, which consists of one drone carried one TX91501 power transmitter [39], three cars carried the TX91501 chargers as buses, 12 sensors deployed in Xianlin campus of NJUPT and 12 landing points.
First, Fig. 11 shows that the total time cost of DSA is 7.6 times that of OPT on average, and survival rate of
Conclusion
In this article, we have designed the drone-enabled unique wireless charging system for sensors supported by the bus network in urban areas. The bus are sustainably charged by the OLEV system or its fuel engine, and has sufficient energy to charge the drone. The sensors are charged by the drone. We have formulated DSB problem to minimize the total time cost of drone subject to all sensors can be charged exactly once by the drone under the energy constraint of drone, and proposed an
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Yong Jin received the M.S. degree from Nanjing Tech University, Nanjing, China, in 2009. He is currently pursuing the Ph.D. degree with the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, China. He is an associate professor in the School of Computer Science & Engineering, Changshu Institute of Technology, Changshu, China. His current research interests include wireless charging, intelligent transportation
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Yong Jin received the M.S. degree from Nanjing Tech University, Nanjing, China, in 2009. He is currently pursuing the Ph.D. degree with the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, China. He is an associate professor in the School of Computer Science & Engineering, Changshu Institute of Technology, Changshu, China. His current research interests include wireless charging, intelligent transportation system, 5G, etc.
Jia Xu (M’15) received the M.S. degree in School of Information and Engineering from Yangzhou University, Jiangsu, China, in 2006 and the PhD. Degree in School of Computer Science and Engineering from Nanjing University of Science and Technology, Jiangsu, China, in 2010. He is currently a professor in the School of Computer Science at Nanjing University of Posts and Telecommunications. He was a visiting Scholar in the Department of Electrical Engineering & Computer Science at Colorado School of Mines from Nov. 2014 to May. 2015. His main research interests include crowdsourcing, edge computing and wireless sensor networks. Prof. Xu has served as the PC Co-Chair of SciSec 2019, Organizing Chair of ISKE 2017, TPC member of Globecom, ICC, MASS, ICNC, EDGE. He currently serves as the Publicity Co-Chair of SciSec 2021.
Sixu Wu received the bachelor’s degree in School of Computer Science from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2019. He is currently working toward the master’s degree in the same university. His research interests are mainly in the areas of the mobile crowd sensing, and wireless charger network.
Lijie Xu received his Ph.D. degree in the Department of Computer Science and Technology from Nanjing University, Nanjing, in 2014. He was a research assistant in the Department of Computing at the Hong Kong Polytechnic University, Hong Kong, from 2011 to 2012. He is currently an associate professor in the School of Computer Science at Nanjing University of Posts and Telecommunications, Nanjing. His research interests are mainly in the areas of wireless sensor networks, ad-hoc networks, mobile and distributed computing, and graph theory algorithms.
Dejun Yang (SM’19) received the B.S. degree in Computer Science from Peking University, Beijing, China, in 2007 and Ph.D. degree in Computer Science from Arizona State University, Tempe, AZ, USA, in 2013. He is currently the Ben L. Fryrear Assistant Professor of Computer Science in the Department of Electrical Engineering & Computer Science at Colorado School of Mines. His main research interests include economic and optimization approaches to networks, crowdsourcing, smart grid, big data, and cloud computing. Prof. Yang has served as a Technical Program Committee Member for many conferences, including the IEEE International Conference on Computer Communications (INFOCOM), the IEEE International Conference on Communications (ICC), and the IEEE Global Communications Conference (GLOBECOM). He has received Best Paper Awards at the IEEE GLOBECOM (2015), the IEEE International Conference on Mobile Ad hoc and Sensor Sys-tems (2011), and the IEEE ICC (2011 and 2012), as well as a Best Paper Award Runner-up at the IEEE International Conference on Network Protocols (2010).
Kaijian Xia (M’16), received the M.S. degree in School of Information and Engineering from Jiangnan University, Wuxi, Jiangsu, China, in 2009 and the Ph.D. degree at School of Information and Control Engineering from China University of Mining and Technology, Xuzhou, Jiangsu, China, in 2020. Currently, he is a senior engineer of computer science with the affiliated Changshu Hospital of Soochow University, Changshu, Jiangsu, China, and an associate professor of medical information with Xuzhou Medical University. His research interests include intelligent medical information, deep learning, biomedical image analysis, bio-inspired computing, pattern recognition and transfer learning. He has served as the TPC Vice-Chair for CyberLife 2019 and currently serves the Editor in Chief for International Journal of Health Systems and Translational Medicine, an Associate Editor for Journal of Medical Imaging and Health Informatics.
The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.
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This work was supported in part by the NSFC, China under grants No. 61872193 and 62072254, Postgraduate Research & Practice Innovation Program of Jiangsu Province, China under grant No. SJKY190760, Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant No. 19KJB520020, and NSF, USA under grant No. 1717315.