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A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in

Fengjun Shang¹, Yi Jiang², Anping Xiong³

¹College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. shangfj@cqupt.edu.cn.

Sensors (Basel, Switzerland)

|November 22, 2016

Summary

This summary is machine-generated.

This study introduces an improved wireless sensor network localization algorithm using multi-granularity region partitioning and distance estimation via Received Signal Strength indicator (RSSI). The method refines node positioning accuracy in real-world environments.

Keywords:

Lagrange RSSI Voronoi diagram WSN vector similar degrees

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Area of Science:

Computer Science
Electrical Engineering
Networking

Background:

The Internet of Things (IoT) relies on Wireless Sensor Networks (WSNs) for data collection and analysis.
Accurate localization of sensor nodes is crucial for WSN functionality within IoT applications.
Existing localization methods face challenges in complex, real-world environments.

Purpose of the Study:

To develop an advanced localization algorithm for sensor nodes in WSNs.
To enhance positioning accuracy by addressing environmental factors affecting node data.
To validate the proposed algorithm in a realistic setting.

Main Methods:

Proposed a multi-granularity region partition for location division.
Utilized Received Signal Strength indicator (RSSI) for distance estimation, optimized with Gaussian fitting.
Employed Voronoi diagrams for regional division and triangle formation for node localization.
Implemented vector similarity and centroid algorithms for nodes within positioning units.
Applied Lagrange multipliers and Taylor expansion for nodes outside positioning units.

Main Results:

The proposed multi-granularity regional division effectively segments the localization area.
Gaussian fitting optimizes RSSI-based distance estimation.
Combined localization techniques (vector similarity, centroid, Lagrange multipliers, Taylor expansion) improve accuracy.
The algorithm demonstrated effectiveness in real-world environmental validation.

Conclusions:

The developed localization algorithm offers improved accuracy for sensor nodes in WSNs.
The integration of multi-granularity partitioning and advanced mathematical methods enhances robustness.
This research contributes to more reliable WSN deployments in IoT ecosystems.