Reducing Sensor Failure and Ensuring Scheduling Fairness for Online Charging in Heterogeneous Rechargeable Sensor Networks

The breakthrough of wireless power transfer technology provides an effective solution to the problem of energy depletion in Wireless Rechargeable Sensor Networks (WRSNs). Most existing work focuses on charging between a mobile charger and a requested sensor, such as NJNP and SAMER, under the assumption that sensors have the same battery capacity and energy consumption rate. In reality, it is more general that a WRSN consists of different types of sensors where they have different battery capacity and energy consumption rate, which is referred as Heterogeneous Wireless Rechargeable Sensor Network (HWRSN). We propose a novel online charging algorithm called VTMT to solve the charging problem in HWRSN. First, we propose the concept of Virtual Time, which is positively correlated with the waiting time of the requested sensor. Then selects the next charging sensor primarily based on the Virtual Time (VT) of the sensor and the Moving Time (MT) of the mobile charger to the node. Simulation results show that VTMT outperforms other charging schemes, which effectively reduce the failure rate of nodes and ensure the scheduling fairness.

[1]  Yiwei Thomas Hou,et al.  Wireless power transfer and applications to sensor networks , 2013, IEEE Wireless Communications.

[2]  Djamel Djenouri,et al.  A Study of Wireless Sensor Networks for Urban Traffic Monitoring: Applications and Architectures , 2013, ANT/SEIT.

[3]  Xiaoqiao Meng,et al.  Real-time forest fire detection with wireless sensor networks , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[4]  Daji Qiao,et al.  Prolonging Sensor Network Lifetime Through Wireless Charging , 2010, 2010 31st IEEE Real-Time Systems Symposium.

[5]  Hanif D. Sherali,et al.  Making Sensor Networks Immortal: An Energy-Renewal Approach With Wireless Power Transfer , 2012, IEEE/ACM Transactions on Networking.

[6]  Jianping Pan,et al.  Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks , 2015, IEEE Transactions on Mobile Computing.

[7]  Zhenzhou Tang,et al.  An Environment Monitoring System for Precise Agriculture Based on Wireless Sensor Networks , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[8]  Jianping Pan,et al.  Evaluating On-Demand Data Collection with Mobile Elements in Wireless Sensor Networks , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[9]  Jiming Chen,et al.  Optimal Charging in Wireless Rechargeable Sensor Networks , 2016, IEEE Transactions on Vehicular Technology.

[10]  Xiuqi Li,et al.  Starvation avoidance mobile energy replenishment for wireless rechargeable sensor networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[11]  Periklis Chatzimisios,et al.  Application of Wireless Sensor Networks for Indoor Temperature Regulation , 2014, Int. J. Distributed Sens. Networks.

[12]  Guihai Chen,et al.  Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks , 2018, Comput. Commun..

[13]  Daji Qiao,et al.  J-RoC: A Joint Routing and Charging scheme to prolong sensor network lifetime , 2011, 2011 19th IEEE International Conference on Network Protocols.

[14]  Adnan Harb,et al.  Energy harvesting: State-of-the-art , 2011 .

[15]  Bin Wang,et al.  A Survey of Energy Conservation, Routing and Coverage in Wireless Sensor Networks , 2011, AMT.

[16]  Abhinav Tomar,et al.  An efficient scheme for on-demand energy replenishment in wireless rechargeable sensor networks , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).