Wireless-Powered Sensor Networks: How to Realize

Choi, Kae Won; Ginting, Lorenz; Rosyady, Phisca Aditya; Aziz, Arif Abdul; Kim, Dong In

doi:10.1109/twc.2016.2621766

Cited by 98 publications

(80 citation statements)

References 26 publications

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“…This paper extends our previous work [4] for the WPSN with only a single sensor node to a more generalized WPSN model accommodating multiple sensor nodes. In the multinode context, it is a challenging task to simultaneously charge multiple sensor nodes by dynamically forming multiple microwave energy beams.…”

Section: Introductionsupporting

confidence: 57%

“…1. We have conducted experiment-driven researches on the WPSN in our previous works [3] and [4]. In [3], we have provided a comprehensive system model of the WPSN with a single transmit antenna based on the experimental results on a real-life testbed.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], we have provided a comprehensive system model of the WPSN with a single transmit antenna based on the experimental results on a real-life testbed. In another work [4], we have studied the multi-antenna WPSN, in which the power beacon is able to steer the microwave energy beam towards specific directions. In [4], we have proposed a channel estimation algorithm for energy beamforming and an adaptive duty cycling algorithm for energy neutral operation.…”

Section: Introductionmentioning

confidence: 99%

“…In another work [4], we have studied the multi-antenna WPSN, in which the power beacon is able to steer the microwave energy beam towards specific directions. In [4], we have proposed a channel estimation algorithm for energy beamforming and an adaptive duty cycling algorithm for energy neutral operation. In addition, the proposed algorithms have been implemented on a full-fledged WPSN testbed, and were verified by extensive experiments.…”

Section: Introductionmentioning

confidence: 99%

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Theory and Experiment for Wireless-Powered Sensor Networks: How to Keep Sensors Alive

Choi

Rosyady

Ginting

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we investigate a multi-node multiantenna wireless-powered sensor networks (WPSN) comprised of one power beacon and multiple sensor nodes. We have implemented a real-life multi-node multi-antenna WPSN testbed that operates in real time. We propose a beam-splitting beamforming technique that enables a power beacon to split microwave energy beams towards multiple nodes for simultaneous charging. We experimentally demonstrate that the beam-splitting beamforming technique achieves the Pareto optimality. For perpetual operation of the sensor nodes, we adopt an energy neutral control algorithm that keeps a sensor node alive by balancing the harvested and consumed power. The joint beam-splitting and energy neutral control algorithm is designed by means of the Lyapunov optimization technique. By experiments, we have shown that the proposed algorithm can successfully keep all sensor nodes alive by optimally splitting energy beams towards multiple sensor nodes.

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Section: Introductionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Theory and Experiment for Wireless-Powered Sensor Networks: How to Keep Sensors Alive

Choi

Rosyady

Ginting

et al. 2018

IEEE Trans. Wireless Commun.

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“…In wireless power transfer (WPT), the coverage is very limited because of the very low efficiency of WPT. It is expected that the coverage of WPT "is practically" (please see the DEMO posted to the link of youtube: https://youtu.be/qP9fZQX1sDk with [36,37]) up to 10 m for wireless-powered sensor applications. This is why we consider such a small cell network for WPT.…”

Section: Simulation Resultsmentioning

confidence: 99%

Transmission Power and Antenna Allocation for Energy-Efficient RF Energy Harvesting Networks with Massive MIMO

et al. 2017

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Abstract:The optimum transmission strategy for maximizing energy efficiency (EE) of a multi-user massive multiple-input multiple-output (MIMO) system in radio frequency energy harvesting networks is investigated. We focus on dynamic time-switching (TS) antennas, to avoid the practical problems of power-splitting antennas, such as complex architectures, power loss and signal distortion when splitting the power of the received signal into power for information decoding (ID) and energy harvesting (EH). However, since a single TS antenna cannot serve ID and EH simultaneously, the MIMO system is considered in this paper. We thus formulate an EE optimization problem and propose an iterative algorithm as a tractable solution, including an antenna selection strategy to optimally switch each TS antenna between ID mode and EH mode using nonlinear fractional programming and the Lagrange dual method. Further, the problem is solved under practical constraints of maximum transmission power and outage probabilities for a minimum amount of harvested power and rate capacity for each user. Simulation results show that the proposed algorithm is more energy-efficient than that of baseline schemes, and demonstrates the trade-off between the required amount of harvested power and energy efficiency.

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Passive Neighbor Discovery with Scanning Mechanism for Mobile Ad Hoc Devices in WUR Networks

Park

2018

Lecture Notes on Data Engineering and Communications Technologies

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Wireless-Powered Sensor Networks: How to Realize

Cited by 98 publications

References 26 publications

Theory and Experiment for Wireless-Powered Sensor Networks: How to Keep Sensors Alive

Theory and Experiment for Wireless-Powered Sensor Networks: How to Keep Sensors Alive

Transmission Power and Antenna Allocation for Energy-Efficient RF Energy Harvesting Networks with Massive MIMO

Passive Neighbor Discovery with Scanning Mechanism for Mobile Ad Hoc Devices in WUR Networks

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