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In search for a robust design of environmental sensor networks.

Setia Budi1,2,3, Ferry Susanto2,4, Paulo de Souza2

  • 1a School of Engineering and ICT , University of Tasmania , Hobart , Australia.

Environmental Technology
|March 23, 2017
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Summary

This study introduces an evolutionary algorithm to design optimal environmental sensor networks (ESN). The approach ensures a robust, fit-for-purpose network with minimal redundancy for effective environmental monitoring.

Keywords:
Sensor networks designevolutionary algorithminverse distance weightingmulti-objective optimisationspatial interpolation

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Area of Science:

  • Environmental Science
  • Computer Science
  • Network Engineering

Background:

  • Environmental sensor networks (ESN) are crucial for monitoring environmental parameters.
  • Designing ESN requires balancing network coverage, robustness, and cost-efficiency.
  • Existing design approaches may lead to suboptimal networks with unnecessary redundancy.

Purpose of the Study:

  • To develop an optimized design approach for environmental sensor networks (ESN).
  • To achieve a robust and fit-for-purpose ESN with minimized redundancy.
  • To provide a decision-making tool for ESN deployment and node count.

Main Methods:

  • Utilized an evolutionary algorithm to search for near-optimal ESN designs.
  • Incorporated network redundancy and robustness as key fitness functions within the algorithm.
  • Evaluated the algorithm's efficacy in generating efficient network configurations.

Main Results:

  • Identified a set of near-optimal environmental sensor network designs.
  • Demonstrated the capability of the evolutionary algorithm to minimize redundancy while maximizing robustness.
  • Provided insights into optimal sensor node placement and quantity.

Conclusions:

  • The proposed evolutionary algorithm effectively designs robust and efficient environmental sensor networks.
  • This approach aids in informed decision-making for deploying sensor nodes and optimizing network performance.
  • The method offers a valuable tool for creating cost-effective and reliable ESNs.