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Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

Tengyue Zou1, Zhenjia Li2, Shuyuan Li3

  • 1College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China. zouty@fafu.edu.cn.

Sensors (Basel, Switzerland)
|May 5, 2017
PubMed
Summary

This study introduces adaptive mobile wireless sensor networks for improved target detection. Mobile sensors move to high-risk areas, enhancing detection accuracy and energy efficiency compared to static systems.

Keywords:
alarmdata fusionenergy controlintrusive detectionwireless sensor network

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Area of Science:

  • Robotics and Sensor Networks
  • Artificial Intelligence
  • Surveillance Systems

Background:

  • Traditional static sensor networks have limitations in target detection due to uniform deployment, leading to missed detections in high-risk areas and energy wastage in safe zones.
  • Area surveillance, elder care, and fire alarms commonly utilize target detection, highlighting the need for more efficient and adaptive systems.
  • The probability of intrusion varies across different field areas, making uniform sensor distribution suboptimal.

Purpose of the Study:

  • To develop an adaptive and pertinent target detection system using mobile wireless sensor networks.
  • To enhance detection accuracy and reduce energy consumption in surveillance applications.
  • To overcome the limitations of static sensor networks in dynamic environments.

Main Methods:

  • Implemented mobile wireless sensor nodes capable of moving towards risk areas using an adaptive learning procedure based on Bayesian networks.
  • Utilized a clustering algorithm based on k-means++ and an energy control mechanism to optimize node energy consumption.
  • Employed the extended Kalman filter and a voting data fusion method for improved target localization accuracy.

Main Results:

  • The proposed adaptive mobile sensor network system demonstrated superior performance compared to traditional static systems.
  • The adaptive learning procedure effectively directed mobile nodes to areas with higher intrusion probability.
  • Energy-efficient methods and advanced localization techniques significantly improved overall system effectiveness.

Conclusions:

  • Mobile wireless sensor networks offer significant advantages in target detection by adapting to dynamic risk levels.
  • The integration of Bayesian networks, k-means++, and Kalman filtering results in an energy-efficient and accurate detection system.
  • This adaptive approach represents a substantial improvement over traditional fixed sensor network deployments for surveillance and monitoring.