Low-latency mobile data collection for Wireless Rechargeable Sensor Networks

Wireless charging is a game-changing technology to provide reliable energy source for wireless sensor networks. Combining wireless charging with mobile data collection on a single mobile vehicle can mitigate the nonuniform energy distribution problem. However, data latency may be too long for some applications because vehicles have to spend significant time in charging before uploading data to the base station. In this paper, we propose a new framework that employs a dedicated vehicle for data collection and theoretically study the trade-offs between data latency and the number of recharging vehicles needed. We first study how to minimize data latency while ensuring all sensory data are collected and derive a latency bound. Then we establish a mathematical model to calculate the minimum number of recharging vehicles needed. Finally, we conduct simulations to validate the theoretical results and evaluate the efficiency of the framework. The results show that our scheme can reduce the number of nonfunctional nodes by 30-60%, and cut down data collection latency more than an order of magnitude compared to the previous work.

[1]  Cong Wang,et al.  Energy-efficient mobile data collection in energy-harvesting wireless sensor networks , 2014, 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS).

[2]  Yuanyuan Yang,et al.  SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks , 2006, IEEE Transactions on Parallel and Distributed Systems.

[3]  K DasSajal,et al.  Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution , 2008 .

[4]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[5]  Chi Ma,et al.  Battery-aware routing for streaming data transmissions in wireless sensor networks , 2005, BROADNETS.

[6]  Chi Ma,et al.  Data-centric energy efficient scheduling for densely deployed sensor networks , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[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]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[9]  Yuanyuan Yang,et al.  Clustering and load balancing in hybrid sensor networks with mobile cluster heads , 2006, QShine '06.

[10]  Cong Wang,et al.  Joint Mobile Data Gathering and Energy Provisioning in Wireless Rechargeable Sensor Networks , 2014, IEEE Transactions on Mobile Computing.

[11]  Yuanyuan Yang,et al.  A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks , 2014, IEEE Transactions on Mobile Computing.

[12]  Sajal K. Das,et al.  Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution , 2008, IEEE Transactions on Parallel and Distributed Systems.

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

[14]  Patrick Jaillet,et al.  Probabilistic Traveling Salesman Problems , 1985 .

[15]  Yuanyuan Yang,et al.  Optimization-Based Distributed Algorithms for Mobile Data Gathering in Wireless Sensor Networks , 2012, IEEE Trans. Mob. Comput..

[16]  Cong Wang,et al.  Multi-vehicle Coordination for Wireless Energy Replenishment in Sensor Networks , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[17]  R. Kershner The Number of Circles Covering a Set , 1939 .

[18]  Yuanyuan Yang,et al.  Energy-Efficient Multihop Polling in Clusters of Two-Layered Heterogeneous Sensor Networks , 2008, IEEE Transactions on Computers.

[19]  S. Ross A First Course in Probability , 1977 .

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