Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks.

Sajida Imran¹, Young-Bae Ko²

¹Department of Computer Engineering, Ajou University, Suwon 443749, Korea. sajida@ajou.ac.kr.

Sensors (Basel, Switzerland)

|February 18, 2017

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments.

Sensors (Basel, Switzerland)·2024

Same author

Investigating vertical transmission and maternofoetal outcomes in pregnant women with COVID-19: a case-control study from Pakistan.

JPMA. The Journal of the Pakistan Medical Association·2024

Same author

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions.

Frontiers in public health·2022

Same author

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2022

Same author

Trust Based Multipath QoS Routing Protocol for Mission-Critical Data Transmission in Tactical Ad-Hoc Networks.

Sensors (Basel, Switzerland)·2020

Same author

A Stepwise and Hybrid Trust Evaluation Scheme for Tactical Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2020

This study introduces a robust wireless sensor network approach for detecting continuous objects, effectively handling boundary node failures to improve accuracy and reduce data transmission.

Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless sensor networks (WSNs) face challenges in detecting and tracking continuous objects due to their dynamic nature.
Accurate object boundary estimation in WSNs relies heavily on the reliability of boundary nodes (BNs).
Failure of BNs can significantly degrade detection and boundary estimation accuracy.

Purpose of the Study:

To develop an efficient object detection approach for WSNs that is resilient to boundary node failures.
To reduce transmission overhead without compromising boundary estimation accuracy.
To enhance the overall performance of WSNs in detecting and tracking continuous phenomena.

Main Methods:

Utilizing spatial and temporal features of sensor nodes for detecting object boundary nodes.

Keywords:

continuous object detection and tracking node failure wireless sensor network

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: Mar 7, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Implementing a Voronoi diagram-based network clustering for improved node organization.

Developing a failure detection and recovery scheme for boundary nodes.

Main Results:

The proposed approach effectively detects and recovers from boundary node failures.
Significant reduction in the number and size of transmissions was achieved.
Maintained high boundary estimation accuracy despite node failures.
Demonstrated improvements in energy efficiency and communication overhead.

Conclusions:

The developed failure-prone object detection approach enhances WSN reliability for continuous object tracking.
The integration of Voronoi diagrams and failure recovery mechanisms improves boundary accuracy and network efficiency.
The approach offers a promising solution for robust object detection in dynamic WSN environments.