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| Title | Energy-Efficient Iot Data Aggregation Framework Using Low-Power Wide-Area Networks |
|---|---|
| ID_Doc | 23492 |
| Authors | Reddy K.N.; Shukla A.; Sivasubramanian S.; Rajappa Sakthidharan G.; Hussein L.; Vaishnavi R. |
| Year | 2024 |
| Published | 2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024 |
| DOI | http://dx.doi.org/10.1109/ICICAT62666.2024.10923118 |
| Abstract | The present work proposes an IoT data aggregation framework for LPWANs that essentially considers the problem areas of energy consumption, data distortion, scalability, and security. The presented framework combines complex aggregation methods with intelligent transmission time division and data compression to maximize the energy consumption and to ensure that data is transmitted accurately and on time. Through online simulations and real-life testing, the potential benefits have been observed to be up to 40% efficiency increment than the conventional aggregation techniques, 99% data reliability, and a high level of efficiency when used in large-scale networks. The aggregated data collected remain secure since the encryption process adopted does not require a huge computational power, hence ensuring maximum level of security with minimal incorporation of additional power. This has been developed to cater a broad network of application in the IoT domain of varying intricacy including environment monitoring, smart city implementations, within a single and efficient platform that is sans in terms of energy usage in restricted networks. © 2024 IEEE. |
| Author Keywords | Adaptive Transmission Scheduling; Data Compression; Energy Efficiency; Energy Harvesting; IoT Data Aggregation; IoT Scalability; LoRaWAN; Low-Power Wide-Area Networks (LPWAN); Secure Data Transmission; Sigfox |
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