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

Bin Cao¹, Wangyuan Chen², Ying Shen³

¹College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China. bincao@zjut.edu.cn.

Sensors (Basel, Switzerland)

|September 8, 2017

Summary

This study introduces a new method for analyzing temporal data from sensor networks. The Bitmap-based Maximum Range Counting (BMRC) approach efficiently identifies critical time intervals with high event incidence.

Keywords:

Internet of Things (IoT)bitmap maximum range counting sensor monitoring networks

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Last Updated: Feb 23, 2026

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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.