Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

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dc.citation.issue19
dc.citation.volume22
dc.contributor.authorMishra M
dc.contributor.authorSen Gupta G
dc.contributor.authorGui X
dc.contributor.editorGarcía Ó
dc.coverage.spatialSwitzerland
dc.date.accessioned2023-10-18T19:59:36Z
dc.date.accessioned2023-10-19T20:38:38Z
dc.date.available2022-10-10
dc.date.available2023-10-18T19:59:36Z
dc.date.available2023-10-19T20:38:38Z
dc.date.issued2022-10-10
dc.date.updated2023-10-18T02:43:37Z
dc.description.abstractThe exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.
dc.description.confidentialfalse
dc.edition.editionOctober 2022
dc.format.extent7685-
dc.identifier7685
dc.identifiers22197685
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/36236783
dc.identifier.citationMishra M, Sen Gupta G, Gui X. (2022). Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications.. Sensors (Basel). 22. 19. (pp. 7685-).
dc.identifier.doi10.3390/s22197685
dc.identifier.eissn1424-8220
dc.identifier.elements-typejournal-article
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10179/20340
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/1424-8220/22/19/7685
dc.relation.isPartOfSensors (Basel)
dc.rights(c) 2022 The Author/sen_US
dc.rightsCC BYen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectH-RLEAHE
dc.subjectIoT
dc.subjectRLE
dc.subjectadaptive huffman encoding
dc.subjectdata compression
dc.titleInvestigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
dc.typeJournal article
pubs.elements-id457318
pubs.organisational-groupOther
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