Design and Verification of a Rail-Borne Energy Harvester for Powering Wireless Sensor Networks in the Railway Industry

Gao, Mingyuan; Wang, Ping; Cao, Yong; Chen, Rong; Cai, Dunjin

doi:10.1109/tits.2016.2611647

Cited by 63 publications

(60 citation statements)

References 55 publications

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“…A = (a, b, c), where a ≤ b ≤ c, is a triangular fuzzy number and can be defined as in Equation (3) and depicted in Figure 3: Definition 4. Suppose two triangular fuzzy numbers A1 = (a1, b1, c1) and A2 = (a2, b2, c2), the standard operations on triangular fuzzy numbers are as shown in Equations (4)- (7): Definition 4. Suppose two triangular fuzzy numbers A 1 = (a 1 , b 1 , c 1 ) and A 2 = (a 2 , b 2 , c 2 ), the standard operations on triangular fuzzy numbers are as shown in Equations (4)- (7):…”

Section: Definition 1 a Fuzzy Set A Is Defined In Equationmentioning

confidence: 99%

“…Fuzzy division: A1⊙A2 = (a1/c2, b1/b2, c1/a2) (7) The concept of Chen's maximizing set and minimizing set (1985) is introduced in Definitions 5-6 below:…”

Section: Of 23mentioning

confidence: 99%

“…where the ith row represents the related triangular fuzzy number of the ith criterion, i.e., (2,3,7) is the triangular fuzzy number of the cost of sensor 1. Similarly, the fuzzy criteria matrices of sensors 2-6 can be displayed as:…”

Section: The Proposed Fuzzy Criteria Matrix and Its Additionmentioning

confidence: 99%

“…Wireless (smart) sensor networks (WSN) consist of smart sensors deployed and operated in wireless sensor networks, and have rapidly become an important design widely applied in many modern applications, such as the Internet of Things (IoT) [1,2], smart grids [3], healthcare and medical systems [4], wind energy systems [5], industrial automation [6], the smart transportation industry [7,8], the semiconductor industry [9] and smart cities [10]. The signal, information or flow cannot be successfully transmitted through the nodes, which are the sensors in the WSN, if the operation of WSN system fails because nodes deplete their limited battery power.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Definition 1 a Fuzzy Set A Is Defined In Equationmentioning

confidence: 99%

“…Fuzzy division: A1⊙A2 = (a1/c2, b1/b2, c1/a2) (7) The concept of Chen's maximizing set and minimizing set (1985) is introduced in Definitions 5-6 below:…”

Section: Of 23mentioning

confidence: 99%

Section: The Proposed Fuzzy Criteria Matrix and Its Additionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Mainly two mechanisms are used to convert this mechanical energy to electrical one which are piezoelectric [1][2][3] and electromagnetic principles [4,5].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Vibration Energy Harvesting for Railway Applications

et al. 2018

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Abstract. Safe localization of trains via GPS and wireless sensors is essential for railway traffic supervision. Especially for freight trains and because normally no power source is available on the wagons, special solutions for energy supply have to be developed based on energy harvesting techniques. Since vibration is available in this case, it provides an interesting source of energy. Nevertheless, in order to have an efficient design of the harvesting system, the existing vibration needs to be investigated. In this paper, we focus on the characterization of vibration parameters in railway application. We propose an electromagnetic vibration converter especially developed to this application. Vibration profiles from a train traveling between two German cities were measured using a data acquisition system installed on the train's wagon. Results show that the measured profiles present multiple frequency signals in the range of 10 to 50 Hz and an acceleration of up to 2 g. A prototype for a vibration converter is designed taking into account the real vibration parameters, robustness and integrability requirements. It is based on a moving coil attached to a mechanical spring. For the experimental emulation of the train vibrations, a shaker is used as an external artificial vibration source controlled by a laser sensor in feedback. A maximum voltage of 1.7 V peak to peak which corresponds to a maximum of 10 mW output power where the applied excitation frequency is close to the resonant frequency of the converter which corresponds to 27 Hz.

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An off‐policy approach for model‐free stabilization of linear systems subject to input energy constraint and its application to spacecraft rendezvous

Zhou

2020

Optim Control Appl Methods

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This note is concerned with the problem of stabilizing a class of linear continuous-time systems with completely unknown system dynamics subject to input energy constraint. To deal with this problem, a model-based low gain feedback law is designed firstly by establishing a special algebraic Riccati equation. Such a low gain feedback law can semiglobally stabilize the linear systems subject to input energy constraint with the exact system model. In order to relax the assumption that the system model is exactly known, an off-policy reinforcement learning approach is designed to solve the same problem without requiring the completely knowledge of the system dynamics. Finally, in order to verify the effectiveness of the proposed model-free approach, simulation result on the spacecraft rendezvous problem is introduced. K E Y W O R D Sinput energy constraint, low gain feedback, reinforcement learning, spacecraft rendezvous 1 948

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Design and Verification of a Rail-Borne Energy Harvester for Powering Wireless Sensor Networks in the Railway Industry

Cited by 63 publications

References 55 publications

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

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

Electromagnetic Vibration Energy Harvesting for Railway Applications

An off‐policy approach for model‐free stabilization of linear systems subject to input energy constraint and its application to spacecraft rendezvous

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