QoS-Aware Energy Management in Body Sensor Nodes Powered by Human Energy Harvesting

Ibarra, Ernesto; Antonopoulos, Angelos; Kartsakli, Elli; Rodrigues, Joel J. P. C.; Verikoukis, Christos

doi:10.1109/jsen.2015.2483064

Cited by 93 publications

(52 citation statements)

References 20 publications

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“…In cases where energy harvesting technologies are employed, related works have proposed adaptive protocols that deal with the challenge of providing the required quality of service under spatio-temporal uncertainty in the energy input [23][24][25][26]. For an in-depth survey on WBANs, we refer the reader to [27] and [28]; whereas [29] presents a survey on WBANs with special interest in eHealth.…”

Section: Related Workmentioning

confidence: 99%

A residential maintenance-free long-term activity monitoring system for healthcare applications

Fafoutis

Tsimbalo

Mellios

et al. 2016

J Wireless Com Network

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Demographic changes such as the ageing population and the continuous rise of chronic medical conditions such as obesity, diabetes and depression make our healthcare systems economically unsustainable. Sensing technologies are promising solutions that can provide cost-effective answers to these challenges. In this paper, we focus on long-term in-house activity monitoring that aims at early detection and prevention of such conditions. In this context, we present and experimentally evaluate an ultra low-power (less than 100-μW long-term average power consumption) on-body activity sensing prototype system that is based on Bluetooth low energy (BLE). As part of a larger smart home monitoring architecture, the role of the presented system is to collect and reliably deliver acceleration data to the upper layers of the architecture. The system evaluation incorporates a thorough power consumption study that facilitates meaningful battery lifetime estimations, an insightful coverage study in an actual residential environment, and the investigation of energy-efficient packet loss mitigation techniques.

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Section: Related Workmentioning

confidence: 99%

A residential maintenance-free long-term activity monitoring system for healthcare applications

Fafoutis

Tsimbalo

Mellios

et al. 2016

J Wireless Com Network

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show abstract

“…Applications include fall detection [8], activity detection for energy saving at homes or offices [9], 24-hour sleep-wake monitoring in narcolepsy [10], a detection system for motion disorders in Autism patients [11], and other uses leveraging IoTs [12][13][14][15][16][17][18]. The methods introduced in [13,14] leverage body sensor nodes powered by human energy harvesting and wireless sensor networks for remote patient monitoring. Cretikos et al [19], Pantelopoulos et al [20], and Coronato et al [14] introduced systems for monitoring vital signs in medical problems.…”

Section: Related Workmentioning

confidence: 99%

“…In classification procedure, the most frequent training samples k assign an unlabeled object of a particular class. Various distance parameters are used in KNN algorithm as discussed in Equations (10)- (13). In our case, we have used Euclidean distance as in Equation (12) and set the value of k to 1, which implies that the selected class label was exactly the same as the one nearest to the training dataset On the other hand, the RF classification method primarily integrates a set of independent decision tree classifiers [48].…”

Section: Data Classification Using Knn and Rfmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

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“…The authors in [26] propose a joint power QoS control scheme that has three modules that interact with each other to make optimal use of energy and achieve QoS for WBAN application. In this work the energy is harvested in the human environment.…”

Section: Mac Protocols For Energy Harvestingmentioning

confidence: 99%

Cross Layered MAC Design for RF Energy Harvesting Sensor Network

Sasikala¹,

Kumar²

2016

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The main research objective in wireless sensor networks (WSN) domain is to develop algorithms and protocols to ensure minimal energy consumption with maximum network lifetime. In this paper, we propose a novel design for energy harvesting sensor node and cross-layered MAC protocol using three adjacent layers (Physical, MAC and Network) to economize energy for WSN. The basic idea behind our protocol is to re-energize the neighboring nodes using the radio frequency (RF) energy transmitted by the active nodes. This can be achieved by designing new energy harvesting sensor node and redesigning the MAC protocol. The results show that the proposed cross layer CL_EHSN improves the life time of the WSN by 40%.

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QoS-Aware Energy Management in Body Sensor Nodes Powered by Human Energy Harvesting

Cited by 93 publications

References 20 publications

A residential maintenance-free long-term activity monitoring system for healthcare applications

A residential maintenance-free long-term activity monitoring system for healthcare applications

Internet of Things for Sensing: A Case Study in the Healthcare System

Cross Layered MAC Design for RF Energy Harvesting Sensor Network

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