Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding

Medeiros, Henry; Maciel, M.; Souza, Richard Demo; Pellenz, Marcelo Eduardo

doi:10.1155/2014/672921

Cited by 30 publications

(19 citation statements)

References 23 publications

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“…Another related research work was done by Medeiros et al in 2014 [20]. They compressed lightweight data for wireless sensor networks (WSNs) by monitoring environmental parameters by using low-resolution sensors.…”

Section: Related Workmentioning

confidence: 99%

Analysis of DICOM Image Compression Alternative Using Huffman Coding

Rahmat

Andreas

Fahmi

et al. 2019

Journal of Healthcare Engineering

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Compression, in general, aims to reduce file size, with or without decreasing data quality of the original file. Digital Imaging and Communication in Medicine (DICOM) is a medical imaging file standard used to store multiple information such as patient data, imaging procedures, and the image itself. With the rising usage of medical imaging in clinical diagnosis, there is a need for a fast and secure method to share large number of medical images between healthcare practitioners, and compression has always been an option. This work analyses the Huffman coding compression method, one of the lossless compression techniques, as an alternative method to compress a DICOM file in open PACS settings. The idea of the Huffman coding compression method is to provide codeword with less number of bits for the symbol that has a higher value of byte frequency distribution. Experiments using different type of DICOM images are conducted, and the analysis on the performances in terms of compression ratio and compression/decompression time, as well as security, is provided. The experimental results showed that the Huffman coding technique has the capability to compress the DICOM file up to 1 : 3.7010 ratio and up to 72.98% space savings.

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Section: Related Workmentioning

confidence: 99%

Analysis of DICOM Image Compression Alternative Using Huffman Coding

Rahmat

Andreas

Fahmi

et al. 2019

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Some of other work shows adaptive modulation scaling which improves energy efficiency of WSN discussed in (Yang et al, 2005). Lightweight Data Compression in Wireless Sensor Networks using Huffman Coding proposed in (Medeiros and Maciel, 2014) uses Huffman algorithm for data reduction. A two-stage DPCM scheme for wireless sensor networks in (Luo et al, 2005) consists of temporal and spatial stages that compress data by making predictions based on samples from the past and helping sensors.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Chakraborty¹,

Tharini²

2017

American Journal of Applied Sciences

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Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each node contains sensors, processors and transceivers for communicating data. Regardless of the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the sink. However the limited network bandwidth, frequent node/link failure along with the unreliable communication medium poses great challenges for node to node communication in WSN. Hence, energy efficient data compression algorithms are necessary for sensor nodes as they enhance the transmission efficiency in WSN. Compressive sensing is a new compression algorithm in which the input signal is converted into sparse signal and the sparse signal is further converted into a signal of reduded dimension than original signal. The dimensionality reduction improves the transmission efficiency. This new concept is recently applied in WSN, however suitable threshold selection to sparsify the one dimensional sensor reading and suitable sparifying basis for image input data are not considered in literature. Hence, in this paper analysis of compressive sensing algorithm with a suitable threshold selection is performed in order to increase the level of sparsity for one dimensional data and a suitable sparsifying basis selection is performed for image data. Results indicate that compressive sensing with suitable threshold selection improves transmission and bandwidth efficiency in case of low correlated one dimensional sensor data and a suitable basis improves the quality of transmission for image sensor data and hence the overall lifetime of sensor network can be increased.

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“…Thus, it is widely discussed in literature. Medeiros et al (2014) suggest a Huffman encoding applied to differences of consecutive measurements and thus achieve high compression ratios. This method works very efficient with time series of single sensors for one dimension with small changes between consecutive observations.…”

Section: Related Workmentioning

confidence: 99%

“…It does not presume high correlation of consecutive observations (time series) like e.g. Huffman encoding does (Kolo et al, 2012, Medeiros et al, 2014. Thus, our algorithm does not need to keep track of individual sensors within a set of observations, but encodes each observation individually within the given value domains.…”

Section: Principle and General Designmentioning

confidence: 99%

Compression and Progressive Retrieval of Multi-Dimensional Sensor Data

Lorkowski¹,

Brinkhoff²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats. Corresponding author

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Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding

Cited by 30 publications

References 23 publications

Analysis of DICOM Image Compression Alternative Using Huffman Coding

Analysis of DICOM Image Compression Alternative Using Huffman Coding

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Compression and Progressive Retrieval of Multi-Dimensional Sensor Data

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