Compression in wireless sensor networks

Razzaque, Mohammad A.; Bleakley, Chris J.; Dobson, Simon

doi:10.1145/2528948

Cited by 145 publications

(34 citation statements)

References 127 publications

(192 reference statements)

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“…6). Finally, we can obtain the coefficients [cA 4 , cD 4 , cD 3 , cD 2 , cD 1 ] and the length L of each decomposition coefficient.…”

Section: Wavelet Decompositionmentioning

confidence: 99%

“…The Internet of Thing (IoT) has developed rapidly in recent years, in which the wireless sensor network is becoming popular with low energy consumption, multifunction and large-scale deployment by sensing, collecting, processing, and transmitting the sensory data through cooperation between nodes [1,2]. However, the number of data transmission between common nodes and sink nodes will increase significantly together with network size explosion, which possibly leads to data congestion, and accordingly high loss rate of sensory data and low signalnoise ratio [3][4][5]. Using data prediction methods to reduce unnecessary data transmission is an effective way to improve the quality of data collection and increase the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

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Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.

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“…6). Finally, we can obtain the coefficients [cA 4 , cD 4 , cD 3 , cD 2 , cD 1 ] and the length L of each decomposition coefficient.…”

Section: Wavelet Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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“…Besides CS, many research groups have developed low power, low-cost compression techniques exploring state-of-the-art compression algorithms [68][69][70]. Subsequently, various efforts have been made to establish different frameworks to compare CS with state-of-the-art compression algorithms for different applications to determine the optimum compression technique.…”

Section: Sensing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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“…It is based on the assumption that some (sparse) signals can be reconstructed from such series of samples that are considered to be incomplete [2], i.e. have insufficient information value for proper signal reconstruction through the sampling theorem.…”

Section: Introductionmentioning

confidence: 99%

“…The cornerstone of signal reconstruction using compressed sensing -the ℓ 1 -minimization [2], which looks for the optimal representation of the signal in such base, where the signal is sparse. In compressed sensing, the measurement matrix A replaces the sampling process.…”

Section: Introductionmentioning

confidence: 99%

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

Kochláň

Hodou

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Compressed sensing represents an interesting approach in signal processing and reconstruction. The theory involves a surprising number of branches of mathematics: linear algebra, functional analysis, convex and non-convex optimization, nonlinear approximation theory and probability. In general, the applications of compressed sensing can be found (or searched) wherever it is possible to express the signal in sparse representation in a "standard" base or in a base that was adjusted for particular signal. Core applications of compressed sensing today include image processing, signal denoising, image deblurring and inpainting. This paper addresses analysis the influence of external phenomena on the signal reconstruction using compressed sensing in wireless sensor networks. Such external phenomena include, for instance, additive white Gaussian noise (AWGN), attenuation or time shift. Three acoustic input signals sparse in frequency domain are used in experiments. The first one with significant frequency band from 500Hz up to 700Hz. The second signal with one significant frequency band from 2400Hz up to 3100Hz with considerable frequency bands between 0Hz to 1000Hz and 5000Hz to 6000Hz. The third signal used is a synthesized artificial sound invented for the experiment purposes only. It is strictly sparse in the frequency domain and has exactly three frequency bands between 400Hz and 500Hz, 2000Hz and 2100Hz, 9000Hz and 9100Hz. The results show that additive noise as well as attenuation have significant effect on the reconstruction accuracy using the selected distribution scenario and reconstruction method. On the other side, the time shift has no significant effect on the reconstruction.

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Compression in wireless sensor networks

Cited by 145 publications

References 127 publications

Data prediction model in wireless sensor networks based on bidirectional LSTM

Data prediction model in wireless sensor networks based on bidirectional LSTM

Compressive Sensing-Based IoT Applications: A Review

Impact of External Phenomena In Compressed Sensing Methods For Wireless Sensor Networks

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