Experiment, Modeling, and Analysis of Wireless-Powered Sensor Network for Energy Neutral Power Management

Setiawan, Dedi; Aziz, Arif Abdul; Kim, Dong In; Choi, Kae Won

doi:10.1109/jsyst.2017.2774285

Cited by 19 publications

(24 citation statements)

References 25 publications

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“…In [37], duty cycle operation for a self-powering dual mode SWIPT system in which a sensor node adaptively controls single-tone or multi-tone communication mode was introduced and an adaptive mode switching problem was solved to maximize the achievable rate under ENO to realize a battery-free IoT network. Meanwhile, an energy management scheme for a WPSN was proposed in [38], which maximizes the RF energy transfer efficiency while guaranteeing ENO, and the proposed energy management scheme was implemented in an actual testbed to verify its operation. In [39], an adaptive duty cycle control algorithm was proposed to prevent the energy storage of a sensor node from being depleted and its ENO was validated in a multi-antenna WPSN testbed that dynamically steered a microwave beam towards a sensor node.…”

Section: Eno Based On Wptmentioning

confidence: 99%

Energy-Neutral Operation Based on Simultaneous Wireless Information and Power Transfer for Wireless Powered Sensor Networks

Choi

Lee

2019

Energies

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For energy-neutral operation (ENO) of wireless sensor networks (WSNs), we apply a wireless powered communication network (WPCN) to a WSN with a hierarchical structure. In this hierarchical wireless powered sensor network (WPSN), sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. Thus, we apply the simultaneous wireless information and power transfer (SWIPT) technique to the considered WPSN so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, we propose a novel ENO framework, which provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm. The results of extensive simulations demonstrate that the proposed ENO based on SWIPT significantly improves the achievable rate and reduces the energy dissipated in the network while guaranteeing ENO, in comparison with the conventional schemes without SWIPT.

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Section: Eno Based On Wptmentioning

confidence: 99%

Energy-Neutral Operation Based on Simultaneous Wireless Information and Power Transfer for Wireless Powered Sensor Networks

Choi

Lee

2019

Energies

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“…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%

“…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. 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.…”

Section: Introductionmentioning

confidence: 99%

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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“…[31][32][33][34][35][36][37][38]. For example, genetic algorithms [39], artificial bee colony algorithms [40], particle swarm optimization [34,37,41,42], simplified swarm optimization (SSO) [34][35][36][37][38][42][43][44][45], grey wolf [46], neural network [13,41,45,47], harmony search algorithm [47], Sugeno-Type Fuzzy Inference [48], etc. Among these algorithms, SSO proposed by Yeh is the most simple one.…”

Section: Introductionmentioning

confidence: 99%

“…Quang and Kim [12] proposed a gradient routing in an industrial WSN to optimize energy consumption. Setiawan et al [13] came up with an energy management policy to maximize energy transfer efficiency for a WSN. Liu et al [14] optimally designed a WSN to minimize energy consumption.The minimization of energy consumption in WSNs has also been investigated by many researches [15][16][17][18].…”

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confidence: 99%

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

et al. 2018

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Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.Energies 2018, 11, 2385 2 of 23 the WSN system fails because nodes deplete their limited battery power. In other words, a critical restriction in any WSN system is energy consumption. Therefore, numerous investigations of WSNs have had a primary focus on energy consumption.Optimizing energy efficiency (EE) in a WSN, which is defined as the ratio of output over energy consumption, has been the subject of a great number of studies. Mekonnen et al. [2] proposed a prototype of a WSN applied in a video surveillance system to optimize energy consumption. Trapasiya and Soni [11] addressed the goal of retransmission energy reduction in WSNs. Quang and Kim [12] proposed a gradient routing in an industrial WSN to optimize energy consumption. Setiawan et al. [13] came up with an energy management policy to maximize energy transfer efficiency for a WSN. Liu et al. [14] optimally designed a WSN to minimize energy consumption.The minimization of energy consumption in WSNs has also been investigated by many researches [15][16][17][18]. Chanak et al. [19] discussed the balance of energy consumption among deployed sensors in a WSN. The energy-optimal routing problem in WSNs has recently been studied by several works [20][21][22]. The improvement of energy consumption by clustering method in a WSN has also been discussed by some works [23,24].However, the measured values of energy consumption in real-life WSN systems are usually uncertain and imprecise. Fuzzy set theory can effectively resolve these uncertain and imprecise problems. In studies of energy consumption for WSNs, few researchers have presented fuzzy-based methods to solve uncertainty problems in such systems. Collotta et al. [25] considered the turning on/off of devices as the output of a Fuzz...

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Experiment, Modeling, and Analysis of Wireless-Powered Sensor Network for Energy Neutral Power Management

Cited by 19 publications

References 25 publications

Energy-Neutral Operation Based on Simultaneous Wireless Information and Power Transfer for Wireless Powered Sensor Networks

Energy-Neutral Operation Based on Simultaneous Wireless Information and Power Transfer for Wireless Powered Sensor Networks

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

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

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