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Charge-and-Activate Policies for Targets Monitoring in RF-Harvesting Sensor Networks

Journal Article


Abstract


  • In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

Publication Date


  • 2020

Citation


  • Fei, J., Chin, K. W., Yang, C., & Ros, M. (2020). Charge-and-Activate Policies for Targets Monitoring in RF-Harvesting Sensor Networks. IEEE Transactions on Vehicular Technology, 69(7), 7835-7846. doi:10.1109/TVT.2020.2992479

Scopus Eid


  • 2-s2.0-85088584118

Start Page


  • 7835

End Page


  • 7846

Volume


  • 69

Issue


  • 7

Place Of Publication


Abstract


  • In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

Publication Date


  • 2020

Citation


  • Fei, J., Chin, K. W., Yang, C., & Ros, M. (2020). Charge-and-Activate Policies for Targets Monitoring in RF-Harvesting Sensor Networks. IEEE Transactions on Vehicular Technology, 69(7), 7835-7846. doi:10.1109/TVT.2020.2992479

Scopus Eid


  • 2-s2.0-85088584118

Start Page


  • 7835

End Page


  • 7846

Volume


  • 69

Issue


  • 7

Place Of Publication