Wearable battery-less wireless sensor network with electromagnetic energy harvesting system

Chamanian, Salar; Uluşan, Hasan; Zorlu, Özge; Baghaee, Sajjad; Uysal-Biyikoğlu, Elif; Külah, Haluk

doi:10.1016/j.sna.2016.07.020

Cited by 52 publications

(29 citation statements)

References 16 publications

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“…Mechanical energy has been effectively harvested from both human and industrial sources [37] . Motion based energy harvesters are being applied to enable wearable batteryless sensors [38] . The key disadvantage with mechanical energy harvesters is that the device's region operation is often in a narrow range of motion and acceleration.…”

Section: Energy Harvesting Sensor Systemsmentioning

confidence: 99%

Development of a reader device for fully passive wireless sensors

et al. 2017

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I would like to express gratitude for all funding sources that contributed towards this work including; the VTT Thesis completion grant, the EU ERASMUS Mundus TEAM exchange program, TEKES SHOK Intersync project and Enterprise Ireland DIACAPS project. 4I would like to acknowledge the revolutionary work of the great Nicola Tesla, father of modern power systems, radio communications and wireless power transfer. His ideas are at the core of this work.

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Section: Energy Harvesting Sensor Systemsmentioning

confidence: 99%

Development of a reader device for fully passive wireless sensors

et al. 2017

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“…Moreover, batteries must be changed or recharged, and these operations decrease the efficiency of the network and increase the cost in many applications. (1) Several strategies have been studied to extend the lifespan of WSNs, such as switching between active and sleep modes to reduce energy consumption and introducing energy harvesting technology to supply power to the nodes. (2,3) Energy harvesting technology in particular has attracted considerable attention as an alternative method of powering nodes in wireless communication, and ways to collect energy from direct force input such as vibrations and movements have been studied.…”

Section: Introductionmentioning

confidence: 99%

Fabrication and Mechanical Properties of Carbon-fiber-reinforced Polymer Composites with Lead-free Piezoelectric Nanoparticles

Kurita¹,

Wang²,

Nagaoka³

et al. 2020

Sensors and Materials

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Wireless sensor networks (WSNs) are one of the key factors in realizing an Internet of Things (IoT) society. Piezoelectric materials that can convert mechanical energy into electric energy directly are highly promising as energy-harvesting materials for supplying power to wireless sensors. However, it is well known that lead zirconate titanate (PZT), which has the highest piezoelectric performance, is harmful to the environment. Moreover, the high density and brittleness of the piezoceramics hinder the fabrication of a long-life energy-harvesting device. In this study, we fabricated 0-3 structure piezoelectric composite materials consisting of lead-free piezoceramic nanoparticles, epoxy resin, and carbon-fiber-reinforced polymer (CFRP) and evaluated their mechanical properties. The maximum strain energy of the piezoelectric resin/CFRP composites was 20-40% larger than that of piezoelectric/epoxy composites. The maximum stress and flexural modulus of the lead-free piezoelectric potassium sodium niobate (KNN) nanoparticles/CFRP composite were approximately 5-10% larger than those of the barium titanate (BTO) nanoparticles/CFRP composite. Consequently, it is likely that better energy harvesting performance and mechanical properties can be obtained by using KNN nanoparticles than by using BTO nanoparticles.

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“…Sensor nodes with the EH capability harvest energy, store, and use it. Some examples of EH nodes include RF‐powered wireless temperature sensor nodes for monitoring electric equipments, and wearable battery‐less devices …”

Section: Introductionmentioning

confidence: 99%

Learning AP in wireless powered communication networks

Iqbal

Kim

Lee

2019

Int J Communication

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In a wireless powered communication network (WPCN), sensor nodes harvest energy to transmit information. By a harvest-then-transmit (HT) protocol, nodes can be classified into either energy receiving (ER) or data transmitting (DT) nodes depending on the current level of the harvested energy. Since nodes may join or leave a network any time and energy levels vary, the distribution of ER and DT nodes changes over time. As the number of contending DT nodes is highly dynamic, a quick learning mechanism is required for an access point (AP). We propose a learning AP that learns from experience and adapts the frame size according to the changes in the number of DT nodes. The proposed learning AP is also shown to learn well and react to the situation. We compare the performances of the proposed learning mechanism with a WPCN and conventional HT FSA schemes. The proposed RL scheme outperforms the comparative schemes in terms of success rate and delay. KEYWORDSMAC, Q-learning, wireless powered communication networks, wireless sensor networks Int J Commun Syst. 2019;32:e4027. wileyonlinelibrary.com/journal/dac IQBAL ET AL.amount of harvesting and consuming the energy of nodes and dynamically joining/leaving nodes change the number of data transmitting nodes in a network. 10 Machine learning (ML) has drawn much attention recently. Learning algorithms can be considered as a mechanism to find the best solution in a highly fluctuating network situation when mathematical solutions are complex and hard to find. A learning mechanism can be used for a system to adapt the optimal frame size as soon the changes are experienced. Reinforcement learning (RL)-based algorithms are inferred from animal learning-behavior 11,12 and are effective ML algorithms. We have investigated the frame size optimization problem in a highly dynamic energy harvesting network and developed a Q-learning-based mechanism as a solution. The variation in the network status (energy levels of nodes and numbers of data transmitting and energy harvesting nodes) is caused by the random and dynamic energy harvesting and energy consumption of nodes. Since the number of data transmitting nodes is dynamically changing, the optimal communication frame changes as the variations occur. The optimal frame size is directly related with the number of contending nodes since it changes in a dynamic energy harvesting network. It needs to automatically optimize the frame size immediately by the experience and learning from the outcomes of the changes in the number of data transmitting nodes. Predicting or estimating the number of data transmitting nodes in a network helps in adapting the optimal frame size to achieve the maximum channel utilization. We have formulated and developed a semi-random MDP. The states are the frame size and the actions are the increasing/decreasing of the frame size. The learning agent, AP in this case, learns from the contention result experiences and adapts its frame size. The learning of the frame size is performed by observing the number of idle and ...

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Wearable battery-less wireless sensor network with electromagnetic energy harvesting system

Cited by 52 publications

References 16 publications

Development of a reader device for fully passive wireless sensors

Development of a reader device for fully passive wireless sensors

Fabrication and Mechanical Properties of Carbon-fiber-reinforced Polymer Composites with Lead-free Piezoelectric Nanoparticles

Learning AP in wireless powered communication networks

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