Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things

Jan, Syed Roohullah; Ghaleb, Baraq; Tariq, Umair Ullah; Ali, Haider; Sabrina, Fariza; Liu, Lu

Abstract

The Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach’s exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion.

Citation

Jan, S. R., Ghaleb, B., Tariq, U. U., Ali, H., Sabrina, F., & Liu, L. (2024). Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things. Electronics, 13(9), Article 1651. https://doi.org/10.3390/electronics13091651

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2024
• Online Publication Date: Apr 25, 2024
• Publication Date: 2024
• Deposit Date: May 13, 2024
• Publicly Available Date: May 13, 2024
• Journal: Electronics
• Electronic ISSN: 2079-9292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 9
• Article Number: 1651
• DOI: https://doi.org/10.3390/electronics13091651
• Keywords: accuracy, QoS, longest common subsequence, data aggregation, Internet of Things