Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network

Time series data are collected through most of the applications that permeate our lives today. Internet of Things (IoT) sensor data are generated through smart applications and stored in databases. Time series databases require huge storage spaces, as over time they consume a large amount of memory....

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Vydané v:Sensors and materials Ročník 34; číslo 11; s. 4095
Hlavní autori: El-Hageen, Hazem M., Albalawi, Hani, Alatwi, Aadel M., Elrahman, Walaa R. Abd, Faqeh, Sultan T. Mohammed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tokyo MYU Scientific Publishing Division 01.01.2022
Predmet:
Algorithms
Big Data
Energy harvesting
Internet of Things
Solar energy
Solar panels
Time series
Wireless sensor networks
ISSN:0914-4935, 2435-0869
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Shrnutí:Time series data are collected through most of the applications that permeate our lives today. Internet of Things (IoT) sensor data are generated through smart applications and stored in databases. Time series databases require huge storage spaces, as over time they consume a large amount of memory. In this paper, we propose an enhanced compression algorithm for time series data generated by IoT systems that monitor the production of electrical energy by solar panels. The best way to ensure that solar energy systems have high efficiency is to continuously monitor all electrical and environmental factors. However, this requires the collection of enormous quantities of data that can be used to detect defects in the generation of electric energy or in solar panels. As the data must be available for analysis, a lossless compression algorithm is needed. In addition, the compressed data must be in a format that can be queried to perform analysis operations dependent on speed; this means that the decompression of data should not be time-consuming. Our results showed the high speed of the compression process along with good compression rate (16.6%) after applying the proposed compression algorithm.
Bibliografia:ObjectType-Article-1
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ISSN:0914-4935
2435-0869
DOI:10.18494/SAM4106