Distributed sampling rate allocation for data quality maximization in rechargeable sensor networks

Lu, Ting; Li, Guohua; Li, Wei; Chang, Shan; Guo, Wenjing

doi:10.1016/j.jnca.2016.12.021

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…Due to the time-fluctuating qualities of collected vitality, the issue of information examining rate designation to boost the system execution is another testing issue. In [12], they were worried about how to adaptively choose the information inspecting rate to amplify the information nature of all sensor nodes. To solve this issue, they divided the information inspecting rate portion into two stages to decouple the vitality and information examining rate.…”

Section: Research Backgroundmentioning

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: Research Backgroundmentioning

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

“…Conventional methods include random sampling, weight sampling [28], stratified sampling [29], etc. To ensure a better approximation results, different improved sampling schemes have been proposed to be more suitable for different situations in research literature [1], [3], [30]. For instance, Roy et al proposed a distributed stratified-sampling method which partitions the surveyed population into homogeneous subgroups over social networks used in the MapReduce framework [31].…”

Section: Related Workmentioning

confidence: 99%

“…In online stream data processing, one can transform continuous raw data into valuable information, which is widely applied to various fields including online query analysis [1], network traffic monitor [2], and sensor-based measurement networks [3]. Among them, data continuously arrives and users are concerned about real-time results, such as detecting anomalies within a specific time period when monitoring network traffic.…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Approximate Stream Processing With Customized Error Control

et al. 2019

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In approximate processing on stream data, most works focus on how to approximate online arrival data. However, the efficiency of approximation needs to consider multiple aspects. Generally, customers submit their requests with specific quality requirements (e.g., maximum error). This raises a critical problem that online quality control is required to meet the desired quality of service. Since the continuous arriving data may not be entirely stored and needs to be processed immediately, it brings the difficulty of acquiring knowledge online which significantly affects the quality of results. To address these problems, we present an online adaptive approximate processing framework with a delicate combination of data learning, sampling, and quality control. We first design an online data learning strategy for stream data. With the real-time learning results, we propose a dynamic sampling strategy that switches to different sampling methods based on the change of the load. Finally, we present a double-check error control strategy to monitor and correct large errors. Each operation module is correlated through online learning and feedback. The experiments with both synthetic and real-world datasets show that the proposed approximate framework is not only applicable to different data distributions but also provides a customized error control.

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“…One of the first steps to develop a BMS is related to the establishment of a battery model that correctly reflects the battery characteristics over time . However, some subsystems of the BMS, particularly the battery models used to estimate the internal battery state, are not capable of dealing with energy harvesting technologies, making it difficult to tolerate power failures because they can not accurately predict when batteries will stop operating …”

Section: Introductionmentioning

confidence: 99%

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

et al. 2020

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Energy limitation is one of the major bottlenecks during the operation of many emerging applications, such as electric vehicles, water and gas meters and a number of sensors used in the context of the Internet of Things and cyberphysical systems. Energy harvesting techniques have arisen as a promising solution to minimize the energy issues found in these types of application domains. In energy harvesting systems, a critical challenge is the need to use battery models capable of accurately estimating both the input and output power of batteries. This article proposes a temperature-dependent analytical battery model capable of estimating some output quantitiesfor example, state of charge, voltage and lifetimeof batteries that use energy harvesting technologies. This model was validated by comparing its analytical results with a dataset called the Randomized Battery Usage Data Set, which is available at the data repository of the National Aeronautics and Space Administration (NASA) website. It is also presented a proof-of-concept application, demonstrating that the use of these technologies can serve as an effective means to extend the operating time of batteries, resulting in significant benefits for a number of applications. K E Y W O R D Swireless sensor network, battery modelling, energy harvesting, thermal effect

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Distributed sampling rate allocation for data quality maximization in rechargeable sensor networks

Cited by 17 publications

References 18 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

Online Adaptive Approximate Stream Processing With Customized Error Control

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

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