Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

Magno, Michele; Polonelli, Tommaso; Casamassima, Filippo; Gómez, Andrés; Farella, Elisabetta; Benini, Luca

doi:10.1007/s11036-016-0755-z

Cited by 21 publications

(12 citation statements)

References 17 publications

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“…The battery life time of the proposed monitoring system using Equation (6) could be extended to 374.855 h instead of 8 h when the traditional operation was used instead. We also compared the power consumption of the proposed monitoring system based on the sleep/wake scheme to the sixteen existing studies that employed different non-contact technologies such as GSM modem, ZigBee, and Bluetooth to communicate the vital signs of the patients [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79]. The power consumption of different medical applications dissimilar to the breathing syndromes was considered for comparison because there are no previous studies similar to the current work focused on energy consumption.…”

Section: Results For Power Consumptionmentioning

confidence: 99%

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

Al-Naji

Al-Askery

Gharghan

et al. 2019

JSAN

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Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.

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Section: Results For Power Consumptionmentioning

confidence: 99%

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

Al-Naji

Al-Askery

Gharghan

et al. 2019

JSAN

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“…Therefore, most researches revolved around WBSN merely concentrated on the reduction of the energy consumed in addition to extending the lifespan of batteries of biosensors devices. 10 The authors in previous studies [11][12][13][14] have proposed different data collection approaches for various applications. These approaches have reduced the collected data, saved the energy, and improved the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Jaber

Idrees

2020

Int J Communication

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SummaryOne of the best and cheapest solutions for continuous and remote monitoring and evaluation of patient health is to use the WBSNs (wireless body sensor networks) due to its great role in decreasing the expenses of the health care system. In this type of network, the sensed vital signs are gathered by biosensor devices then transmitted to the coordinator for further processing and fusion. Since the limited resources for biosensor devices (energy, memory, and processing) in addition to the periodic transmission for the large volume of data, it is essential to optimize the data transmission to save energy while keeping the data accuracy at the coordinator. This work suggests an AREDaCoT (adaptive rate energy‐saving data collecting technique) which aims to energy‐efficient patient health monitoring in periodic WBSNs. The AREDaCoT works in terms of so‐called periods. Each period has two stages: removing redundancy and adapting the sampling rate. The first stage uses improved LED to remove the redundancy in measurement of vital signs, whereas the second stage applies two techniques, assesses the risk score according to the risk level of the patient and the CSr (calibrate sampling rate) fittingly with different ranges of risk level for the sensor sampling ratio to be adapted. The performance of AREDaCoT has been evaluated in light of multiple series of simulations on real health datasets being compared to an existing approach. The acquired results illustrate how AREDaCoT decreases the volume of gathered data; thus, a significant energy saving has been made whilst preserving data accuracy and integrity. Moreover, the percentage results of data reduction over the original data set and the score differences between them are both acceptable.

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“…2) Energy Consumption: Figure 6 shows the HR sensor node's remaining energy at the end of 70 periods for both patient 1 and 2. We suppose that 1 unit of energy is equal to 152 Joules : the sensing task consumes 6 Joules, the processing task consumes 24 Joules, the transmission task (TX) consumes 60 Joules and the receiving task (RX) consumes 62 Joules [23]. Thus, we suppose that each sampled measurement needs 0.6 units of energy to be sent to the coordinator.…”

Section: B Sampling Rate Adaptation and Energy Consumptionmentioning

confidence: 99%

“…In [6], the authors have classified energy-efficient mechanisms in Wireless Sensor Networks (WSNs) into five categories: data reduction approaches, sleep/wakeup schemes, radio optimization techniques, energyefficient routing methods and battery repletion. These approaches can be split up into software and hardware strategies [8]. In addition, some of these mechanisms are suitable for large scale networks such as environmental monitoring, industry, public safety or military systems applications.…”

Section: Introductionmentioning

confidence: 99%

“…Many existing approaches in WBSNs have proposed energy-efficient data collection approaches. Some have used context-awareness based on activity recognition to perform adaptive sampling or adaptive sensing [8]. Some of them applied these approaches only on WBSNs composed of inertial detection sensor nodes such as accelerometers and gyroscopes [9].…”

Section: Introductionmentioning

confidence: 99%

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Real-time sampling rate adaptation based on continuous risk level evaluation in wireless body sensor networks

Habib

Makhoul²,

Darazi

et al. 2017

2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Wireless Body Sensor Networks (WBSNs) are a lowcost solution allowing remote patient monitoring and continuous health assessment, thus reducing healthcare expenditure. In such networks, sensor nodes periodically collect vital signs and send them to the coordinator for fusion. However, sensor nodes have limited energy and processing resources and transmission is the most power-hungry task. In this paper, we target data reduction and energy consumption. We propose to locally adapt, in real-time, the sampling rate of a sensor node according to the variations in the vital sign being monitored and its risk. We propose to dynamically evaluate, in real-time, the risk of any vital sign given the information about the severity level of the patient's health condition and the severity level of the vital sign itself. We have tested our proposed approach on real health datasets in order to evaluate it. The results show that the percentage of detected critical events and the mean-square error (MSE) are both acceptable. In addition, the percentage of data reduction is around 50% implying a reduction of the energy consumption. Adjusting the risk of a vital sign, over time, ensures the adaptation of the sampling rate according to the overall health condition of the patient as well as the severity level of the collected measurements.

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Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

Cited by 21 publications

References 17 publications

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Real-time sampling rate adaptation based on continuous risk level evaluation in wireless body sensor networks

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