Data Accuracy Pattern-based Transmission Period Control Algorithm for IoT networks

• Published in: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) pp. 668 - 673
• Main Authors: Han, Jaeseob, Lee, Gyeong Ho, Park, Hyunseo, Choi, Jun Kyun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.02.2023
• Subjects: Adaptation models; Clustering algorithms; Energy consumption; Internet of Things; K-means clustering; Mathematical models; particle swarm optimization; Performance evaluation; Sensors; Time series analysis; Transmission period control
• ISSN: 2831-6983
• DOI: 10.1109/ICAIIC57133.2023.10067002

As various Internet of Things technologies emerges, IoT monitoring services are rapidly developed. Most IoT sensors deployed in an IoT monitoring environment should reduce the energy consumption of unnecessary data transmission. In this paper, we propose a data accuracy pattern-based transmission period control algorithm. Restoration accuracy patterns of time series data that are missing due to transmission period control are broadly extracted. These restoration accuracy vectors showing similar patterns are clustered into the same cluster. The clustered patterns are modeled based on a logistic function to form a linear weighted sum-based optimization problem that considers the trade-off relationship between the mathematically modeled energy consumption function and the restoration accuracy function. In order to solve the formulated optimization problem, the particle swarm optimization technique is leveraged. The performance evaluations verify that the proposed model simultaneously achieves the best RMSE performance and the second-best energy consumption performance compared to other transmission period control algorithms.