4095 Sensors and Materials, Vol. 34, No. 11 (2022) 4095–4111 MYU Tokyo S & M 3099 * Corresponding author: e-mail: helhageen@ut.edu.sa https://doi.org/10.18494/SAM4106 ISSN 0914-4935 © MYU K.K. https://myukk.org/ Agile Lossless Compression Algorithm for Big Data of Solar Energy Harvesting Wireless Sensor Network Hazem M. El-Hageen, 1,2,3* Hani Albalawi, 1,2 Aadel M. Alatwi, 1,4 Walaa R. Abd Elrahman, 5 and Sultan T. Mohammed Faqeh 1 1 Electrical Engineering Department, Faculty of Engineering, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia 2 Renewable Energy and Energy Efciency Center (REEEC), University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia 3 Egyptian Atomic Energy Authority, P.O. Box 29, Naser City, Cairo 11787, Egypt 4 Industrial Innovation and Robotic Center (IIRC), University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia 5 Mechanical Engineering Department, Faculty of Engineering, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia (Received September 16, 2022; accepted November 2, 2022) Keywords: lossless compression algorithm, solar energy, compressed time series data, 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. 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. 1. Introduction Time series data are generated by various applications, such as smart electricity grid applications, (1) health-monitoring sensor systems, (2) Internet applications, (3) and Internet of Things (IoT) applications. (4–6) These applications require a huge storage capacity to store the data to be analyzed later, leading to the need to compress the data. Data compression, in addition to saving storage capacity, simplifies the data transfer and enhances the performance of time series databases. (7)