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This study uses neural networks for optimal sensor placement in wireless sensor networks (WSNs), significantly extending network lifetime while balancing coverage and energy constraints for scalable deployments.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges in dense deployments due to conflicting constraints like sensor placement, coverage, connectivity, and energy.
  • Scaling large WSNs is difficult because maintaining a trade-off among these constraints is complex.
  • Existing solutions often rely on heuristics to achieve near-optimal network behavior in polynomial time.

Purpose of the Study:

  • To formulate a topology control and lifetime extension problem for WSNs considering sensor placement, coverage, and energy constraints.
  • To investigate the application of neural network configurations for solving this problem.
  • To dynamically propose and manage sensor placement coordinates to maximize network lifetime.

Main Methods:

  • Formulation of a topology control and lifetime extension problem for WSNs.
  • Application and testing of various neural network configurations.
  • Dynamic proposal and handling of sensor placement coordinates in a 2D plane by the neural network.

Main Results:

  • The proposed neural network algorithm successfully improves network lifetime in simulations.
  • The algorithm maintains essential communication and energy constraints.
  • Performance improvements are demonstrated for medium- and large-scale WSN deployments.

Conclusions:

  • Neural networks offer a viable approach to optimize sensor placement in WSNs.
  • The proposed method effectively extends network lifetime while adhering to critical operational constraints.
  • This approach addresses the scalability challenges in dense WSN deployments.