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Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks.

Junaid Akram1, Hafiz Suliman Munawar2, Abbas Z Kouzani3

  • 1Department of Computer Science, Superior University, Lahore 54000, Pakistan.

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Summary
This summary is machine-generated.

This study introduces an adaptive learning automata algorithm (ALAA) for wireless sensor networks, optimizing sensor node scheduling for efficient target monitoring and reduced power consumption. The ALAA effectively balances coverage and energy usage, outperforming existing methods.

Keywords:
adaptive learningcoverage areaenergy efficiencylearning automatasensorswireless sensor network

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Area of Science:

  • Wireless Sensor Networks
  • Machine Learning
  • Microtechnology

Background:

  • Wireless sensor networks (WSNs) face challenges in power consumption and target monitoring due to limited battery life.
  • Existing architectures and algorithms have attempted to address these issues with varying success.

Purpose of the Study:

  • To develop and evaluate a novel scheduling algorithm for WSNs to improve energy efficiency and target coverage.
  • To address the power and target monitoring limitations inherent in WSN technology.

Main Methods:

  • Implementation of the adaptive learning automata algorithm (ALAA), a machine learning scheduling method.
  • Equipping each sensor node with learning automata to autonomously select between active and sleep states.
  • Conducting experiments with varying parameters to validate the algorithm's consistency and effectiveness.

Main Results:

  • The proposed ALAA demonstrates an effective approach to scheduling sensor nodes for comprehensive target monitoring with reduced power usage.
  • Experimental findings confirm the method's consistency across different parameters.
  • Benchmarking against the LADSC scheduling algorithm shows the superiority of the proposed method.

Conclusions:

  • The adaptive learning automata algorithm (ALAA) successfully addresses the challenges of power consumption and target monitoring in WSNs.
  • The proposed algorithm is a valuable technique for scheduling sensor nodes in practical WSN deployments.
  • The study validates the ALAA's effectiveness in achieving project aims and justifying the problem description.