research-article

W3W: Energy Management of Hybrid Energy Supplied Sensors for Internet of Things

Authors:

Yu WangAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 1

Article No.: 10, Pages 1 - 23

https://doi.org/10.1145/3280964

Published: 10 February 2019 Publication History

Abstract

The usage of hybrid energy supplied sensors in the Internet of Things has enabled longer lifetime of sensors and expanded scope of applications. These sensors can combine advantages of environmental energy harvesting techniques and wireless energy harvesting techniques. However, how to coordinate them is still a challenge and has not been studied extensively. In this article, we present a system based on mobile crowd wireless charging to manage energy of hybrid energy supplied sensors. When environmental energy is insufficient, the system will utilize smart devices carried by mobile users as chargers to provide wireless energy. We construct and study a W3W problem in the system: <underline>w</underline>hen to leverage mobile crowd wireless charging to support rechargeable sensors, <underline>w</underline>here to perform wireless energy transfer, and <underline>w</underline>hom to allocate and incentivize as chargers to maximize useful energy value over all sensors subject to a budget. In order to control the actual quality of wireless energy charging, we propose a design principle named task completion trustfulness. We consider offline and online conditions and design corresponding algorithms with incentive allocations. Extensive simulations are conducted to demonstrate the effectiveness of our algorithms, which also validates our theoretical results.

References

[1]

R. Moffatt A. Kurs and M. Soljacic. 2010. Simultaneous mid-range power transfer to multiple devices. Applied Physics Letters 96, 4 (2010), 34.

[2]

Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010. The internet of things: A survey. Computer Networks 54, 15 (2010), 2787--2805.

Digital Library

[3]

Alessandro Cammarano, Chiara Petrioli, and Dora Spenza. 2012. Pro-Energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks. In IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS’12). 75--83.

Digital Library

[4]

Shuo Chen, Yuanchao Shu, Bihan Yu, Chao Liang, Zhiguo Shi, and Jiming Chen. 2016. Demo: Mobile wireless charging and sensing by drones. In International Conference on Mobile Systems, Applications, and Services (MobiSys’16). 99.

Digital Library

[5]

Shuo Han Chen, Yung Chun Chang, Tseng Yi Chen, Yu Chun Cheng, Hsin Wen Wei, Tsan Sheng Hsu, and Wei Kuan Shih. 2016. Prolong lifetime of dynamic sensor network by an intelligent wireless charging vehicle. In IEEE Vehicular Technology Conference. 1--5.

[6]

Siyao Cheng, Zhipeng Cai, and Jianzhong Li. 2015. Curve query processing in wireless sensor networks. IEEE Transactions on Vehicular Technology 64, 11 (2015), 5198--5209.

[7]

Siyao Cheng, Zhipeng Cai, Jianzhong Li, and Xiaolin Fang. 2015. Drawing dominant dataset from big sensory data in wireless sensor networks. In IEEE International Conference on Computer Communications (INFOCOM’15). 531--539.

[8]

Siyao Cheng, Zhipeng Cai, Jianzhong Li, and Hong Gao. 2017. Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering 29, 4 (2017), 813--827.

Digital Library

[9]

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms (3rd ed.). The MIT Press.

Digital Library

[10]

Lingkun Fu, Liang He, Peng Cheng, Yu Gu, Jianping Pan, and Jiming Chen. 2016. ESync: Energy synchronized mobile charging in rechargeable wireless sensor networks. IEEE Transactions on Vehicular Technology 65, 9 (2016), 7415--7431.

[11]

Yuan Gao, Cong Wang, and Yuanyuan Yang. 2015. Joint wireless charging and sensor activity management in wireless rechargeable sensor networks. In International Conference on Parallel Processing. 789--798.

Digital Library

[12]

Songtao Guo, Chunrong He, and Yuanyuan Yang. 2015. ResAll: Energy efficiency maximization for wireless energy harvesting sensor networks. In IEEE International Conference on Sensing, Communication, and Networking (SECON’15). 64--72.

Digital Library

[13]

Fan Li, Song Yang, Hanshang Li, Ting Li, and Yu Wang. 2018. Multi-expertise aware participant selection in mobile crowd sensing via online learning. In IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS. 1--9.

[14]

Shibo He, Jiming Chen, Fachang Jiang, David K. Y. Yau, Guoliang Xing, and Youxian Sun. 2011. Energy provisioning in wireless rechargeable sensor networks. In IEEE International Conference on Computer Communications (INFOCOM’11). 2006--2014.

[15]

Samir Khuller, Anna Moss, and Joseph Naor. 1999. The budgeted maximum coverage problem. Information Processing Letters 70, 1 (1999), 39--45.

Digital Library

[16]

Fan Li, Siyuan Chen, Shaojie Tang, Xiao He, and Yu Wang. 2013. Efficient topology design in time-evolving and energy-harvesting wireless sensor networks. IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 411, 6 (2013), 1--9.

Digital Library

[17]

Ji Li, Siyao Cheng, Zhipeng Cai, Jiguo Yu, Chaokun Wang, and Yingshu Li. 2017. Approximate holistic aggregation in wireless sensor networks. ACM Transactions on Sensor Networks 13, 2 (2017), 11.

Digital Library

[18]

Chi Lin, Ding Han, Jing Deng, and Guowei Wu. 2017. P2S: A primary and passer-by scheduling algorithm for on-demand charging architecture in wireless rechargeable sensor networks. IEEE Transactions on Vehicular Technology 66, 9 (2017), 8047--8058.

[19]

Tu Liang Lin, Sheng Lin Li, and Hong Yi Chang. 2016. A power balance aware wireless charger deployment method for complete coverage in wireless rechargeable sensor networks. Energies 9, 9 (2016), 695.

[20]

Daniele Miorandi, Sabrina Sicari, Francesco De Pellegrini, and Imrich Chlamtac. 2012. Internet of things: Vision, applications and research challenges. Ad Hoc Networks 10, 7 (2012), 1497--1516.

Digital Library

[21]

Mohammed Moness and Ahmed Mahmoud Moustafa. 2016. A survey of cyber-physical advances and challenges of wind energy conversion systems: Prospects for internet of energy. IEEE Internet of Things Journal 3, 2 (2016), 134--145.

[22]

Yi Qu, Ke Xu, Jiangchuan Liu, and Wenlong Chen. 2015. Toward a practical energy conservation mechanism with assistance of resourceful mules. IEEE Internet of Things Journal 2, 2 (2015), 145--158.

[23]

Xunpeng Rao, Panlong Yang, Yubo Yan, Hao Zhou, and Xuangou Wu. 2017. Optimal recharging with practical considerations in wireless rechargeable sensor network. IEEE Access 5, 99 (2017), 4401--4409.

[24]

Navin Sharma, Jeremy Gummeson, David Irwin, and Prashant Shenoy. 2010. Cloudy computing: Leveraging weather forecasts in energy harvesting sensor systems. In IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’10). 1--9.

[25]

Yi Shi, Liguang Xie, Y. Thomas Hou, and Hanif D. Sherali. 2011. On renewable sensor networks with wireless energy transfer. In IEEE International Conference on Computer Communications (INFOCOM’11). 1350--1358.

[26]

Yaron Singer. 2010. Budget feasible mechanisms. In Foundations of Computer Science. 765--774.

Digital Library

[27]

William Spaniel. 2014. Game Theory 101: The Complete Textbook. CreateSpace.

[28]

Cong Wang, Ji Li, Yuanyuan Yang, and Fan Ye. 2016. A hybrid framework combining solar energy harvesting and wireless charging for wireless sensor networks. In IEEE International Conference on Computer Communications (INFOCOM). 1--9.

Digital Library

[29]

Guowei Wu, Chi Lin, Ying Li, Lin Yao, and Ailun Chen. 2015. A multi-node renewable algorithm based on charging range in large-scale wireless sensor network. In International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. 94--100.

[30]

Liguang Xie, Yi Shi, Y. Thomas Hou, Wenjing Lou, Hanif D. Sherali, and Scott F. Midkiff. 2015. Multi-node wireless energy charging in sensor networks. IEEE/ACM Transactions on Networking 23, 2 (2015), 437--450.

Digital Library

[31]

Liguang Xie, Yi Shi, Y. Thomas Hou, and Hanif D. Sherali. 2012. Making sensor networks immortal: An energy-renewal approach with wireless power transfer. IEEE/ACM Transactions on Networking 20, 6 (2012), 1748--1761.

Digital Library

[32]

Wenzheng Xu, Weifa Liang, Jian Peng, Yiguang Liu, and Yan Wang. 2017. Maximizing charging satisfaction of smartphone users via wireless energy transfer. IEEE Transactions on Mobile Computing 16, 4 (2017), 990--1004.

Digital Library

[33]

Weiqiang Xu, Yushu Zhang, Qingjiang Shi, and Xiaodong Wang. 2015. Energy management and cross layer optimization for wireless sensor network powered by heterogeneous energy sources. IEEE Transactions on Wireless Communications 14, 5 (2015), 2814--2826.

Digital Library

[34]

Zhezhuang Xu, Liquan Chen, Cailian Chen, and Xinping Guan. 2016. Joint clustering and routing design for reliable and efficient data collection in large-scale wireless sensor networks. IEEE Internet of Things Journal 3, 4 (2016), 520--532.

[35]

Yuanyuan Yang, Cong Wang, and Ji Li. 2015. Power sensor networks by wireless energy ął current status and future trends. In International Conference on Computing, Networking and Communications. 648--652.

[36]

Yufeng Zhan, Yuanqing Xia, Yang Liu, Fan Li, and Yu Wang. 2017. Time-sensitive data collection with incentive-aware for mobile opportunistic crowdsensing. IEEE Transactions on Vehicular Technology 66, 9 (2017), 7849--7861.

[37]

Sheng Zhang, Zhuzhong Qian, Fanyu Kong, Jie Wu, and Sanglu Lu. 2015. P3: Joint optimization of charger placement and power allocation for wireless power transfer. In IEEE International Conference on Computer Communications (INFOCOM’15). 2344--2352.

[38]

Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang, and Jian Tang. 2015. Keep your promise: Mechanism design against free-riding and false-reporting in crowdsourcing. IEEE Internet of Things Journal 2, 6 (2015), 562--572.

[39]

Xinglin Zhang, Zheng Yang, Wei Sun, Yunhao Liu, Shaohua Tang, Kai Xing, and Xufei Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys 8 Tutorials 18, 1 (2016), 54--67.

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Index Terms

W3W: Energy Management of Hybrid Energy Supplied Sensors for Internet of Things
1. Networks
  1. Network protocols
    1. Network protocol design
  2. Network types
    1. Mobile networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 1

February 2019

382 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3300201

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 February 2019

Accepted: 01 September 2018

Revised: 01 September 2018

Received: 01 June 2018

Published in TOSN Volume 15, Issue 1

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Beijing Institute of Technology Research Fund Program for Young Scholars
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