Abstract
Wireless charging technology has drawn great attention of both academia and industry in recent years, due to its potential of significantly improving the system performance of sensor networks. The emergence of an open-source experimental platform for wireless rechargeable sensor networks, Powercast, has made the theoretical research closer to reality. This pioneering platform is able to recharge sensor nodes much more efficiently and allows different communication protocols to be implemented upon users’ demands. Different from the RFID-based model widely used in the existing works, Powercast designs the charger and sink station separately. This leads to a new design challenge of cooperatively deploying minimum number of chargers and sink stations in wireless rechargeable sensor networks. Such a co-deployment issue is extremely challenging, since the deployments of chargers and sink stations are coupled, and each subproblem is known to be NP-hard. The key to the design is to understand the intrinsic relationship between data flow and energy flow, which is interdependent. In this article, we tackle this challenge by dividing it into two subproblems and optimizing charger and sink station deployment iteratively. Specifically, we first transform each subproblem to a max-flow problem. With this, we are able to select chargers or sink stations according to their contributions to the total flow rate. We design greedy-based algorithms with a guaranteed worst-case bound ln R/ξ for the subproblems of charger deployment and sink station deployment, respectively. Further, we address the original problem by designing an iterative algorithm that solves two subproblems alternatively to achieve a near optimal performance. We corroborate our analysis by extensive simulations under practical coefficient settings and demonstrate the advantage of the proposed algorithm.
- A. Bogdanov, E. Maneva, and S. Riesenfeld. 2004. Power-aware base station positioning for sensor networks. In Proceedings of the 23rd Conference of the IEEE Communications Society. IEEE. DOI:http://dx.doi.org/10.1109/INFCOM.2004.1354529 Google Scholar
Cross Ref
- H. Dai, X. Wu, G. Chen, L. Xu, and S. Lin. 2014. Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks. Comput. Commun. 46 (2014), 54--65. Google Scholar
Cross Ref
- R. Deng, Y. Zhang, S. He, J. Chen, and X. Shen. 2016. Maximizing network utility of rechargeable sensor networks with spatiotemporally coupled constraints. IEEE Journal on Selected Areas in Communications 34, 5 (2016), 1307--1319. Google Scholar
Cross Ref
- M. Dong, K. Ota, and A. Liu. 2016a. RMER: Reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet of Things Journal 3, 4 (2016), 511--519. Google Scholar
Cross Ref
- M. Dong, K. Ota, A. Liu, and M. Guo. 2016b. Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 27, 1 (2016), 225--236. Google Scholar
Digital Library
- L. Fu, P. Cheng, Y. Gu, J. Chen, and T. He. 2013. Minimizing charging delay in wireless rechargeable sensor networks. In Proceedings of the 32nd IEEE International Conference on Computer Communications. IEEE, Turin. DOI:http://dx.doi.org/10.1109/INFCOM.2013.6567103 Google Scholar
Cross Ref
- P. Gonzalez-Brevis, J. Gondzio, Y. Fan, H. Poor, J. Thompson, I. Krikidis, and P. J. Chung. 2011. Base station location optimization for minimal energy consumption in wireless networks. In Proceedings of the 73rd IEEE Vehicular Technology Conference. IEEE. DOI:http://dx.doi.org/10.1109/VETECS.2011.5956204 Google Scholar
Cross Ref
- S. Guo, C. Wang, and Y. Yang. 2014. Joint mobile data gathering and energy provisioning in wireless rechargeable sensor networks. IEEE Trans. Mobile Comput. 13, 12 (2014), 2836--2852. Google Scholar
Cross Ref
- S. He, J. Chen, F. Jiang, D. Yau, G. Xing, and Y. Sun. 2012. Energy provisioning in wireless rechargeable sensor networks. IEEE Trans. Mobile Comput. 12, 10 (2012), 1931--1942. Google Scholar
Digital Library
- S. He, D.-H. Shin, J. Zhang, J. Chen, and Y. Sun. 2016. Full-view area coverage in camera sensor networks: Dimension reduction and near-optimal solutions. IEEE Trans.n Vehic. Technol. 65, 9 (2016), 7448--7461.Google Scholar
Cross Ref
- W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. 2000. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. IEEE. DOI:http://dx.doi.org/10.1109/HICSS.2000.926982 Google Scholar
Cross Ref
- A. Kurs, A. Karalis, R. Moffatt, J. Joannopoulos, P. Fisher, and M. Soljai. 2007. Wireless power transfer via strongly coupled magnetic resonances. Science 317, 5834 (2007), 83--86. Google Scholar
Cross Ref
- J. H. Liao, W. T. So, and J. R. Jiang. 2013. Optimized charger deployment for wireless rechargeable sensor networks. In Proceedings of the 9th Workshop on Wireless, Ad hoc, and Sensor Networks. Retrieved from http://wasn2013.cs.nthu.edu.tw/paper_result.php.Google Scholar
- H. Liu. 2011. Retrieved from http://hxhl95.github.io/media/ib_ee.pdf.Google Scholar
- J. Long, A. Liu, M. Dong, and Z. Li. 2016. An energy-efficient and sink location privacy enhanced scheme for WSNs through ring based routing. J. Parallel and Distrib. Comput. 81 (2016), 47--65.Google Scholar
- X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han. 2015. Wireless networks with RF energy harvesting: A contemporary survey. IEEE Commun. Surveys Tutor. 17, 2 (2015), 757--789. Google Scholar
Cross Ref
- J. Luo and J. Hubaux. 2005. Joint mobility and routing for lifetime elongation in wireless sensor networks. In Proceedings of the 24th IEEE Annual Joint Conference of Computer and Communications Societies. IEEE. DOI:http://dx.doi.org/10.1109/INFCOM.2005.1498454 Google Scholar
Cross Ref
- C. Mikeka and H. Arai. 2011. Design Issues in Radio Frequency Energy Harvesting System. INTECH Open Access Publisher. Google Scholar
Cross Ref
- E. Modiano, D. Shah, and G. Zussman. 2006. Maximizing throughput in wireless networks via gossiping. In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems. ACM. DOI:http://dx.doi.org/10.1145/1140103.1140283 Google Scholar
Digital Library
- UMich Moo. 2011. Retrieved from https://spqr.eecs.umich.edu/moo/.Google Scholar
- Powercast. 2016. Retrieved from http://www.powercastco.com/.Google Scholar
- L. Qiu, R. Chandra, K. Jain, and M. Mahdian. 2004. Optimizing the placement of integration points in multi-hop wireless networks. In Proceedings of the 12th International Conference on Network Protocols. IEEE, 271--282. DOI:http://dx.doi.org/10.1109/ICNP.2004.1348117 Google Scholar
Cross Ref
- P. T. A. Quang and D.-S. Kim. 2012. Enhancing real-time delivery of gradient routing for industrial wireless sensor networks. IEEE Trans. Industr. Informat. 8, 1 (2012), 61--68. Google Scholar
Cross Ref
- S. Roy, V. Jandhyala, J. R. Smith, D. J. Wetherall, B. P. Otis, R. Chakraborty, M. Buettner, D. J. Yeager, Y.-C. Ko, and A. P. Sample. 2010. RFID: From supply chains to sensor nets. IEEE RFID Virtual J. 98, 9 (2010), 1583--1592. Google Scholar
Cross Ref
- A. Sample, D. Yeager, A. Mamishev P. Powledge, and J. Smith. 2008. Design of an rfid-based battery-free programmable sensing platform. IEEE Trans. Instrument. Measure. 57, 11 (2008), 2608--2615. Google Scholar
Cross Ref
- L. Shi, Z. Kabelac, D. Katabi, and D. Perreault. 2015. Wireless power hotspot that charges all of your devices. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, Paris. DOI:http://dx.doi.org/10.1145/278918.2790092Google Scholar
- Y. Shu, P. Cheng, Y. Gu, J. Chen, and T. He. 2014. Minimizing communication delay in RFID-based wireless rechargeable sensor networks. In Proceedings of the 2014 17th Annual IEEE International Conference on Sensing, Communication, and Networking. IEEE. DOI:http://dx.doi.org/10.1109/SAHCN.2014.6990382 Google Scholar
Cross Ref
- Y. Shu, P. Cheng, Y. Gu, J. Chen, and T. He. 2015. TOC: Localizing wireless rechargeable sensors with time of charge. ACM Trans. Sensor Networks 11, 3 (2015), 44.Google Scholar
Digital Library
- Y. Shu, Y. Gu, and J. Chen. 2014. Dynamic authentication with sensory information for the access control systems. IEEE Trans. Parallel Distrib. Syst. 25, 2 (2014), 427--436. Google Scholar
Digital Library
- J. R. Smith, K. P. Fishkin, B. Jiang, A. Mamishev, M. Philipose, A. D. Rea, S. Roy, and K. Sundara-Rajan. 2005. RFID-based techniques for human-activity detection. ACM Commun. Mag. Special Issue: RFID 48, 9 (2005), 39--44. Google Scholar
Digital Library
- STLM20. 2016. Retrieved from http://www.st.com/content/st_com/zh/products/mems-and-sensors/temperature-sensors/stlm20.html.Google Scholar
- B. Tong, Z. Li, G. Wang, and W. Zhang. 2010. How wireless power charging technology affects sensor network deployment and routing. In Proceedings of the IEEE 30th International Conference on Distributed Computing Systems. IEEE. DOI:http://dx.doi.org/10.1109/ICDCS.2010.61 Google Scholar
Digital Library
- Z. Vincze, R. Vida, and A. Vidacs. 2007. Deploying multiple sinks in multi-hop wireless sensor networks. In Proceedings of the IEEE International Conference on Pervasive Services. IEEE. DOI:http://dx.doi.org/10.1109/PERSER.2007.4283889 Google Scholar
Cross Ref
- J. Wang, A. Liu, T. Yan, and Z. Zeng. 2017a. A resource allocation model based on double-sided combinational auctions for transparent computing. Peer-to-Peer Networking and Applications 2017, to appear (2017).Google Scholar
- Y. Wang, K. Wu, and L. Ni. 2017b. WiFall: Device-free fall detection by wireless networks. IEEE Trans. Mobile Comput. 16, 2 (2017), 581--594. Google Scholar
Digital Library
- Y. Xiao, D. Niyato, Z. Han, and L. A. DaSilva. 2015. Dynamic energy trading for energy harvesting communication networks: A stochastic energy trading game. IEEE J. Select. Areas Commun. 33, 12 (2015), 2718--2734. Google Scholar
Cross Ref
- G. Xie, K. Ota, M. Dong, F. Pan, and A. Liu. 2016. Energy-efficient routing for mobile data collectors in wireless sensor networks with obstacles. Peer to-Peer Networking and Applications 1, 12 (2016), 472--483.Google Scholar
- G. Yang, S. He, Z. Shi, and J. Chen. 2017. Promoting cooperation by social incentive mechanism in mobile crowdsensing. IEEE Commun. Mag. 2017, to appear (2017). Google Scholar
Digital Library
- Q. Yang, S. He, J. Li, J. Chen, and Y. Sun. 2015. Energy-efficient probabilistic area coverage in wireless sensor. IEEE Trans. Vehic. Technol. 61, 1 (2015), 367--377. Google Scholar
Cross Ref
- R. Zhang, P. Thiran, and M. Vetterli. 2015. Virtually moving base stations for energy efficiency in wireless sensor networks. In Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. ACM. DOI:http://dx.doi.org/10.1145/2746285.2746291 Google Scholar
Digital Library
- S. Zhang, J. Wu, and S. Lu. 2012. Collaborative mobile charging for sensor networks. In Proceedings of the IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems. IEEE. DOI:http://dx.doi.org/10.1109/MASS.2012.6502505 Google Scholar
Digital Library
- Y. Zhang, S. He, and J. Chen. 2016. Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Transactions on Networking 24, 3 (2016), 1632--1646. Google Scholar
Digital Library
- Y. Zou, J. Xiao, K. Wu, J. Han, Y. Li, and L. Ni. 2017. GRfid: A device-free RFID-based gesture recognition system. IEEE Trans. Mobile Comput. 16, 2 (2017), 381--393. Google Scholar
Digital Library
Index Terms
Near-Optimal Co-Deployment of Chargers and Sink Stations in Rechargeable Sensor Networks
Recommendations
Wireless Charger Deployment Optimization for Wireless Rechargeable Sensor Networks
U-MEDIA '14: Proceedings of the 2014 7th International Conference on Ubi-Media Computing and WorkshopsIn a wireless rechargeable sensor network (WRSN), sensor nodes can harvest energy from wireless chargers to refill their power supplies so that the WRSN can operate sustainably. This paper considers wireless chargers equipped with 3D-beamforming ...
Optimization of Charging and Data Collection in Wireless Rechargeable Sensor Networks
HCC 2016: Revised Selected Papers of the Second International Conference on Human Centered Computing - Volume 9567Wireless Rechargeable Sensor NetworksWRSNs can significantly prolong the lifetime by employing a mobile charger to replenish the depleted energy of the sensor nodes. This paper considers the scenario that the mobile charger cannot maintain full ...
A Novel UAV Charging Scheme for Minimizing Coverage Breach in Rechargeable Sensor Networks
Green, Pervasive, and Cloud ComputingAbstractIn wireless rechargeable sensor networks, one of the most important issues is how and when to recharge the sensor nodes. Existing studies show that not all of the sensors can be properly recharged in time due to the limitation of solar or wind-...






Comments