Adaptive sampling algorithms with local emergency detection for energy saving in Wireless Body Sensor Networks

Salim, Christian; Makhoul, Abdallah; Darazi, Rony; Couturier, Raphaël

doi:10.1109/noms.2016.7502890

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…Nguyen et al [ 34 ] propose an information-driven adaptive sampling strategy in mobile robotic wireless sensor networks, which aims to minimize the uncertainty at all of the unobserved locations of interest. Salim et al [ 35 ] propose an adaptive sampling approach for wireless body sensor networks to estimate and adapt the sensing frequency based on previous readings and patient criticality. The adaptive sampling approach aims to handle emergency detection with energy saving.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Zeng

Xiang

2017

Sensors

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Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency.

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Section: Related Workmentioning

confidence: 99%

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Zeng

Xiang

2017

Sensors

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“…Therefore, several solutions have been proposed to manage data collection and transmission. In [18]- [19]- [7]- [20]- [21]- [22], authors suggested sending data with adaptive sampling rate. This proposed method reduces power consumption by sending data with appropriate selection method.…”

Section: Introductionmentioning

confidence: 99%

“…Others in [30] suggested Local Emergency Detection Approach for Saving Energy in Wireless Body Sensor Networks. In [7] authors combined Emergency detection method with adaptive sampling rate. Therefore, the nodes are no longer active at all time.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling

Khani¹,

Shirmohammadi²

2021

Preprint

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Due to the aging population and the growing need for hospitals and care for the elderly and limited hospital resources, the need for a mechanism that can handle the limited hospital resources is felt more than ever. Wireless Body Area networks are one of the technologies that monitor people's health remotely, thus avoiding unnecessary hospitalization and thus managing resource constraints. Wireless Body Area Networks (WBANs) face with energy limitations. The battery in WBANs has limited energy. However, it is not possible to replace or charge batteries in many of these networks. Therefore, it is needed to save energy and extend network life. One of the important methods that have been proposed for this purpose is sampling rate management-based methods. In this paper due to the efficiency of the sampling rate management, an efficient sampling rate method has been proposed. For this purpose to minimize unnecessary data transmission, Flat- Top Pulse Amplitude Modulated (PAM) method has been used. Then in order to make the signal from PAM suitable for data transmission, Pulse Code Modulation (PCM) method to digitize the pulses has been used. Finally, the results and outputs show that significant energy savings have been achieved. The results showed that data transmission is reduced by up to 92.7% and energy saving is achieved significantly

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“…Many challenges exist in such a network such as: the heterogeneity of the collected data (heart rate, respiration rate, blood pressure etc.) and its huge amount, the energy consumption due to periodic transmission as well as privacy and security issues [1], [5], [7]. In this paper we address the energy consumption and data reduction issues which are directly related to one another.…”

Section: Introductionmentioning

confidence: 99%

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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Adaptive sampling algorithms with local emergency detection for energy saving in Wireless Body Sensor Networks

Cited by 14 publications

References 19 publications

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling

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

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Adaptive sampling algorithms with local emergency detection for energy saving in Wireless Body Sensor Networks

Cited by 14 publications

References 19 publications

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling ​

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

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Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling