A two-stage data fusion model for wireless sensor networks

Yin, Yong; Zhang, Chaoyong

doi:10.1504/ijsnet.2014.063897

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Fig 7 shows the remaining energy on the node which is responsible for sensing the blood pressure. Be that as it may, in A * , all the detected information is sent, in LED*, the sent information is dictated by LED [25], and in Modified LED * [26], the sent information is controlled by Modified LED and finally Advanced LED-AS, the sent data are determined by Advanced LED (Algorithm 1). As appeared in Fig.…”

Section: B Energy Consumptionmentioning

confidence: 99%

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Mehdi

Zarrabi

Zadeh

et al. 2020

mjee

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Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase network lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity.

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Section: B Energy Consumptionmentioning

confidence: 99%

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Mehdi

Zarrabi

Zadeh

et al. 2020

mjee

View full text Add to dashboard Cite

show abstract

“…Other works, such in [5], [16], [17], try to eliminate data redundancy intra and inter nodes. The authors in [16] propose a two-level sensor fusion-based event detection technique for the WSN.…”

Section: Related Workmentioning

confidence: 99%

“…Although the techniques proposed in the literature have successfully adapted the sampling rate, the most of them are performed in a centralized way [5], [7] and are based on organizing sensors into clusters [16], [17] that require huge computations and communications. Indeed, few efforts in distributed sampling algorithms [9], [13] are provided.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring

Harb

Makhoul

2018

IEEE Trans. Ind. Inf.

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The use of wireless sensor network (WSN) for industrial applications has attracted much attention from both academic and industrial sectors. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. In this paper, we propose and compare three data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, while the second one is based on the set similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes have been conducted where the obtained results proved that our methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique.

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A two-stage data fusion model for wireless sensor networks

Cited by 2 publications

References 16 publications

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring

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