Bundle Charging: Wireless Charging Energy Minimization in Dense Wireless Sensor Networks

Using a mobile charger to wirelessly charge sensors is a promising yet not well-solved technique. Existing trajectory planning schemes for wireless charger either (1) fail to optimize the one-to-many characteristic of wireless charging or (2) fail to jointly optimize the charger movement cost and the charging cost. The objective of this paper is to find the optimal trajectory planning for a mobile charger in terms of energy minimization in the quadratic attenuation charging model. There exists a trade-off between charging efficiency and trajectory distance. If the mobile charger comes close to sensors, the charging efficiency is high, but the entire charging trajectory of the charger will be long and vice versa. To address this trade-off, we propose the idea of charging bundle and optimize the charger's trajectory based on the charging bundle rather than each sensor. The optimal charging bundle generation problem and the bundle trajectory optimization problem are discussed gradually. Both of them are proven to be NP-hard. Then, we first propose a greedy bundle generation algorithm with an approximation ratio of lnn, where n is the number of sensors. After that, we propose a TSP-based solution and further optimize the TSP-trajectory by jointly considering the adjacent charging locations. Theorems are proposed to effectively find the optimal location. Extensive experiments show that our scheme achieves a much better performance than traditional schemes.

[1]  Jiming Chen,et al.  Near-Optimal Velocity Control for Mobile Charging in Wireless Rechargeable Sensor Networks , 2016, IEEE Transactions on Mobile Computing.

[2]  Shan Lin,et al.  Charge me if you can: charging path optimization and scheduling in mobile networks , 2016, MobiHoc.

[3]  Jie Wu,et al.  Minimizing deep sea data collection delay with autonomous underwater vehicles , 2017, J. Parallel Distributed Comput..

[4]  Jie Wu,et al.  Trajectory Scheduling for Timely Data Report in Underwater Wireless Sensor Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[5]  Guihai Chen,et al.  Radiation constrained wireless charger placement , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[6]  Emo Welzl,et al.  Smallest enclosing disks (balls and ellipsoids) , 1991, New Results and New Trends in Computer Science.

[7]  Cong Wang,et al.  Recharging schedules for wireless sensor networks with vehicle movement costs and capacity constraints , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[8]  Shaojie Tang,et al.  CHASE: Charging and Scheduling Scheme for Stochastic Event Capture in Wireless Rechargeable Sensor Networks , 2020, IEEE Transactions on Mobile Computing.

[9]  Ning Wang,et al.  Cost-Efficient Heterogeneous Worker Recruitment under Coverage Requirement in Spatial Crowdsourcing , 2021, IEEE Transactions on Big Data.

[10]  Sajal K. Das,et al.  Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey , 2011, TOSN.

[11]  M. Soljačić,et al.  Simultaneous mid-range power transfer to multiple devices , 2010 .

[12]  Shaojie Tang,et al.  Near Optimal Charging and Scheduling Scheme for Stochastic Event Capture with Rechargeable Sensors , 2013, 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems.

[13]  Jie Wu,et al.  Cooperative Wireless Charging Vehicle Scheduling , 2017, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).

[14]  A. Vacavant,et al.  Reconstructions of Noisy Digital Contours with Maximal Primitives Based on Multi-Scale/Irregular Geometric Representation and Generalized Linear Programming , 2017 .

[15]  Milind Dawande,et al.  Energy efficient schemes for wireless sensor networks with multiple mobile base stations , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[16]  Jie Wu,et al.  Coverage and workload cost balancing in spatial crowdsourcing , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[17]  Guihai Chen,et al.  Using Minimum Mobile Chargers to Keep Large-Scale Wireless Rechargeable Sensor Networks Running Forever , 2013, 2013 22nd International Conference on Computer Communication and Networks (ICCCN).

[18]  Sanglu Lu,et al.  Fast Interference-Aware Scheduling of Multiple Wireless Chargers , 2018, 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).

[19]  A SomasundaraA.,et al.  Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks with Dynamic Deadlines , 2004 .

[20]  M. Soljačić,et al.  Wireless Power Transfer via Strongly Coupled Magnetic Resonances , 2007, Science.

[21]  D.J. Yeager,et al.  Wirelessly-Charged UHF Tags for Sensor Data Collection , 2008, 2008 IEEE International Conference on RFID.

[22]  Khaled M. Elbassioni,et al.  Approximation Algorithms for the Euclidean Traveling Salesman Problem with Discrete and Continuous Neighborhoods , 2009, Int. J. Comput. Geom. Appl..

[23]  Guihai Chen,et al.  Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks , 2014, Comput. Commun..

[24]  Jiming Chen,et al.  Minimizing charging delay in wireless rechargeable sensor networks , 2013, 2013 Proceedings IEEE INFOCOM.

[25]  Joseph S. B. Mitchell,et al.  Approximation algorithms for TSP with neighborhoods in the plane , 2001, SODA '01.

[26]  Hanif D. Sherali,et al.  On renewable sensor networks with wireless energy transfer , 2011, 2011 Proceedings IEEE INFOCOM.

[27]  Donghyun Kim,et al.  Multiple heterogeneous data ferry trajectory planning in wireless sensor networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[28]  Deborah Estrin,et al.  Next Century Challenges: Mobile Networking for Smart Dust , 1999, MobiCom 1999.

[29]  Emanuel Melachrinoudis,et al.  Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[30]  Sanjeev Arora,et al.  Nearly Linear Time Approximation Schemes for Euclidean TSP and Other Geometric Problems , 1997, RANDOM.

[31]  Jianping Pan,et al.  A Progressive Approach to Reducing Data Collection Latency in Wireless Sensor Networks with Mobile Elements , 2013, IEEE Transactions on Mobile Computing.

[32]  Ashutosh Sabharwal,et al.  Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks , 2003, IPSN.

[33]  Ke Li,et al.  Qi-ferry: Energy-constrained wireless charging in wireless sensor networks , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[34]  Alanson P. Sample,et al.  Design of an RFID-Based Battery-Free Programmable Sensing Platform , 2008, IEEE Transactions on Instrumentation and Measurement.

[35]  Chau Yuen,et al.  Energy Synchronized Task Assignment in Rechargeable Sensor Networks , 2016, 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[36]  Shaojie Tang,et al.  Concurrently Wireless Charging Sensor Networks with Efficient Scheduling , 2017, IEEE Transactions on Mobile Computing.

[37]  Jie Wu,et al.  Collaborative mobile charging for sensor networks , 2012, 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012).