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Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks.

Yaoming Zhuang1, Chengdong Wu1, Hao Wu2

  • 1Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.

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|May 17, 2020
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Summary
This summary is machine-generated.

A new multi-constrained event-driven deployment model using a collaborative neural network (CONN) algorithm optimizes sensor and robot networks. This approach enhances monitoring in complex environments, reducing adaptation time and costs.

Keywords:
collaborative neural networkevent-driven deploymentmaximum entropy functionmultiple constraintswireless sensor and robot networks

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Area of Science:

  • Robotics and Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless sensor and robot networks (WSRNs) face challenges in complex, hazardous environments with diverse constraints.
  • Traditional event-driven deployment algorithms are limited to single monitoring scenarios, hindering adaptability.
  • Effective deployment is crucial for optimizing monitoring performance in varied conditions.

Purpose of the Study:

  • To propose a novel multi-constrained event-driven deployment model for WSRNs.
  • To develop an adaptive algorithm capable of handling diverse monitoring scenarios simultaneously.
  • To improve the efficiency and reduce the cost of sensor deployment in complex environments.

Main Methods:

  • A multi-constrained event-driven deployment model based on the maximum entropy function.
  • Transformation of the deployment problem into two continuously differentiable single-objective sub-problems.
  • Development of a collaborative neural network (CONN) algorithm utilizing neural network methods.

Main Results:

  • The CONN algorithm effectively addresses data acquisition challenges in complex WSRN environments.
  • It provides adaptive optimal deployment solutions for various complex monitoring scenarios.
  • Experimental validation demonstrates the algorithm's superior performance and adaptability.

Conclusions:

  • The proposed CONN algorithm offers a significant advancement in WSRN deployment strategies.
  • It enhances adaptability and efficiency, reducing deployment time and costs.
  • The method is suitable for a wide range of complex application scenarios.