A Low-Power, Battery-Free Tag for Body Sensor Networks

Mandal, Soumyajit; Turicchia, L.; Sarpeshkar, Rahul

doi:10.1109/mprv.2010.1

Cited by 50 publications

(25 citation statements)

References 13 publications

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“…Furthermore, sensors, which harvest energy from nearby health monitoring bands has been implemented [68]. Moreover, radiofrequency (RF) energy harvesting antenna in a wearable sensor was introduced by researchers at the Massachusetts Institute of Technology [69]. Current devices can generate energy from both a body and environment, but further development is required to harvest more power.…”

Section: Wearablesmentioning

confidence: 99%

Healthcare in the Smart Home: A Study of Past, Present and Future

2017

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Ubiquitous or Pervasive Computing is an increasingly used term throughout the technology industry and is beginning to enter the consumer electronics space in its most recent form under the umbrella term: "Internet of Things". One area of focus is in augmenting the home with intelligent, networked sensors and computers to create a Smart Home which opens a host of possibilities for the role of tomorrow's dwelling. As the world's population continues to live longer and consequently experience more medical-related ailments, at the same time institutional healthcare is struggling to cope, the role of the Smart Home becomes paramount to monitoring a dweller's health and providing any necessary intervention. This study looks at the history of Smart Home Healthcare, current research areas, and potential areas of future investigation. Unique categorisations are presented in Activities of Daily Living (ADL) and Personal Sensors, and a thorough look at the application of Smart Home Healthcare is presented. Technology can augment traditional methods of healthcare delivery and in some cases completely replace it. Costs can be reduced and medical adherence can be increased, all of which contribute to a more sustainable and effective model of care.

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Section: Wearablesmentioning

confidence: 99%

Healthcare in the Smart Home: A Study of Past, Present and Future

2017

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“…Tuned circuits are used one at receiver and one at transmitter, due to inductive coupling power is transferred by magnetic fields developed between the circuits [45]. Two types of resonant inductive coupling are: Weak inductive coupling and strong inductive coupling [46,47].…”

Section: Resonant Energymentioning

confidence: 99%

Current Developments of Energy Scavenging, Converting and Storing in WSNs

Bhaskar¹,

Champawat²,

Bhaskar³

2015

IJCA

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Wireless sensor networks (WSNs) design requires multidisciplinary approach in the field of wireless communication, embedded systems, networking, digital signal processing, hardware and software engineering. Major factors to influence the WSNs design are hardware and software constraints, scalability, cost, transmission media, network topology and power consumption etc. Most of WSN nodes are battery powered. With the limited capacity of batteries to power WSN nodes, need of energy harvester or scavenger is required to harvest or scavenge energy from the environment to improve the life-time of the sensor node. The harvested or scavenged energy is converted by power converters to recharge the sensor nodes or for the storage devices. This paper gives current developments of energy harvesting technologies, power converters and storage devices proposed by various researchers in WSNs along with some open research problems.

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“…1 are essentially RFID tags, which may use the RFID protocols and mechanisms to send tag identifiers as well as to share sensed data. Due to the advance in lowpower electronics, it is now feasible to envisage a low-cost ultralow-power integrated RFID tag that provides powerharvesting, sensing, and actuating capabilities [11]- [13], [15]- [19]. Such an RFID-tag-and-sensor integration framework may find important applications in bio-monitoring of patients and the elderly [11], [13], [15], [16], cyber-centric monitoring [17], and prognostics and health management [19].…”

Section: A Related Workmentioning

confidence: 99%

“…There has been a growing interest in the field of body sensor networks for pervasive healthcare applications [13], [33]. Commonly, sensors are placed on the human body to measure specified vital-sign data.…”

Section: ) Body Sensor Networkmentioning

confidence: 99%

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Channel Modeling of Information Transmission Over Cognitive Interrogator-Sensor Networks

Chen

Woo

Wang

2011

IEEE Trans. Veh. Technol.

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Abstract-This paper looks into the modeling of information transmission over cognitive interrogator-sensor networks (CISNs), which represent a novel and important class of sensor networks deployed for surveillance, tracking, and imaging applications. The crux of the problem is to develop a channel model that allows for the evaluation of the sensing channel capacity and error rate performance in CISNs, for which the sensing link is overlaid by the communication link. First, the layered discrete memoryless channel and finite-state Markov channel models are identified as useful tools to capture the essence of information transfer over the communication and sensing links in CISNs. Two different sensor encoding strategies, namely, 1) the amplify-modulate-and-forward (AMF) and 2) decode-modulate-and-forward (DMF) schemes, are considered. Subsequently, the concepts of double-directional channel capacity azimuth spectrum (CCAS) and Chernoff information azimuth spectrum (CIAS) are proposed to facilitate network-level assessment of the communication and sensing link reliability. A study case considering a typical CISN for environmental monitoring, conditioned on different wireless propagation and sensing conditions, is then presented to demonstrate the steps to derive the various model parameters. Finally, the potential applications of the proposed analytical framework in cognitive sensing, network performance assessment, and simulation are briefly discussed.Index Terms-Channel capacity, Chernoff information, cognitive radar (CR), discrete memoryless channel (DMC), double directional, finite-state Markov channel (FSMC), radio-frequency identification (RFID), wireless sensor network (WSN).

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A Low-Power, Battery-Free Tag for Body Sensor Networks

Cited by 50 publications

References 13 publications

Healthcare in the Smart Home: A Study of Past, Present and Future

Healthcare in the Smart Home: A Study of Past, Present and Future

Current Developments of Energy Scavenging, Converting and Storing in WSNs

Channel Modeling of Information Transmission Over Cognitive Interrogator-Sensor Networks

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