BMRC: A Bitmap-Based Maximum Range Counting Approach for Temporal Data in Sensor Monitoring Networks

Area of Science:

• Computer Science
• Data Science
• Network Engineering

Background:

• The Internet of Things (IoT) generates vast amounts of temporal data from sensor networks, crucial for monitoring real-world events like diseases and disasters.
• Identifying time intervals with the highest incidence of severe events is vital for societal understanding and response, but current methods are inefficient.

Purpose of the Study:

• To propose an efficient approach for identifying maximum range counts in temporal data from sensor networks.
• To address the challenge of efficiently analyzing large-scale temporal datasets generated by high-frequency sensor updates.

Main Methods:

• Development of the Bitmap-based Maximum Range Counting (BMRC) approach.
• Implementation of a scalable strategy to support real-time data insertion and deletion operations for sensor nodes.

Main Results:

• The BMRC approach demonstrates superior efficiency compared to baseline algorithms.
• Experimental results validate the performance of BMRC in handling high-frequency temporal data.

Conclusions:

• The proposed BMRC approach offers an efficient solution for analyzing temporal data in sensor networks.
• BMRC effectively identifies significant time intervals, aiding in the understanding of event patterns and their societal impact.