Abstract
The expansion of the Internet of Things (IoT) has magnified the challenge of managing data generated by IoT devices, notably in meteorological applications like temperature and humidity monitoring. This research addresses the imperative of efficiently reducing IoT data volume while preserving data integrity and underscores the significant implications for energy consumption. Our approach involved a two-fold strategy, employing the DHT11 sensor and ESP32 microcontroller for data collection, followed by an exploration of various data compression algorithms: delta encoding, run-length encoding (RLE), variable-length integer encoding (VLI), and bit-packing. The strategic combination of RLE and delta encoding yielded an exceptional compression rate of 98%. Beyond data reduction, this methodology offers energy savings by minimizing data transmission times, evidenced by the swift 133-microsecond compression process. Furthermore, the seamless transmission of compressed IoT data to Azure Cloud not only reduced cloud storage costs but also optimized storage space, contributing to energy efficiency. This research illuminates the significance of data compression in mitigating the environmental impact of IoT technologies, fostering a greener, more energy-conscious future.
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