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Related Experiment Video

Updated: May 22, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Adapting mobile beacon-assisted localization in wireless sensor networks.

Guodong Teng¹, Kougen Zheng, Wei Dong

¹College of Computer Science, Zhejiang University, Hangzhou 310027, P.R. China; E-Mails: zkg@zju.edu.cn ; dongw@zju.edu.cn.

Sensors (Basel, Switzerland)

|May 11, 2012

Summary

This summary is machine-generated.

This study introduces Mobile Beacon-assisted Localization (MBL) and Adapting MBL (A-MBL) for Wireless Sensor Networks (WSNs). These novel methods enhance sensor node localization accuracy without special hardware, outperforming existing approaches.

Keywords:

Adapting Mobile Beacon-assisted Localization (A-MBL)Localization Mobile Beacon-assisted Localization (MBL)Particle filter Wireless Sensor Networks (WSNs)

Related Experiment Videos

Last Updated: May 22, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

Published on: February 8, 2019

Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Accurate sensor node localization is critical for Wireless Sensor Network (WSN) applications.
Existing mobile-assisted localization methods often require specialized hardware or rely on centralized, deterministic algorithms.
Imprecision in location estimates is a key challenge in current WSN localization techniques.

Purpose of the Study:

To propose a novel range-free, distributed, and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs.
To introduce an adaptive version, Adapting MBL (A-MBL), to improve MBL's efficiency and accuracy.
To evaluate the performance of MBL and A-MBL against existing methods using a single mobile beacon.

Main Methods:

Development of a probabilistic, range-free Mobile Beacon-assisted Localization (MBL) algorithm.
Introduction of Adapting MBL (A-MBL) with dynamic adjustment of sample sets and model parameters.
Comparative performance evaluation against Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO).

Main Results:

MBL and A-MBL demonstrate superior accuracy compared to MSL and ADO in static WSNs.
Both proposed methods effectively utilize a single mobile beacon for localization.
A-MBL enhances the efficiency and accuracy of the baseline MBL approach.

Conclusions:

The proposed MBL and A-MBL offer effective, distributed, and hardware-independent solutions for WSN localization.
These methods provide a significant improvement in localization accuracy for static WSNs.
The adaptive nature of A-MBL allows for optimized performance during the estimation process.