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Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting

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  • 1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing100083, China. liufenustb@163.com.

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Summary
This summary is machine-generated.

This study introduces an adaptive dynamic programming algorithm for scheduling sensors in energy-harvesting wireless sensor networks. The proposed method optimizes collaborative target tracking accuracy while managing energy constraints for extended network life.

Keywords:
adaptive dynamic programmingenergy harvestingextended Kalman filtersensor schedulingtarget trackingwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are crucial for collaborative target tracking but face limitations in sensing, communication, and computation resources.
  • Energy harvesting technologies enable potentially infinite network lifetimes for WSNs, but introduce challenges in sensor scheduling over infinite horizons.
  • Balancing tracking accuracy and energy consumption is a key challenge in WSNs, especially with energy harvesting capabilities.

Purpose of the Study:

  • To develop a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for energy-harvesting WSNs.
  • To address the challenge of scheduling sensors over an infinite horizon while considering limited energy harvesting capabilities.
  • To achieve high collaborative target tracking accuracy in energy-harvesting WSNs.

Main Methods:

  • Proposed an adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS).
  • Utilized the extended Kalman filter (EKF) for target state prediction and estimation within the scheduling framework.
  • Scheduled multiple sensors at each time step over an infinite horizon.

Main Results:

  • Theoretical analysis confirmed the optimality of the ADP-MSS algorithm.
  • Simulation results demonstrated superior tracking accuracy of ADP-MSS compared to single-sensor scheduling and simulated annealing methods.
  • The ADP-MSS algorithm effectively balances tracking accuracy and energy consumption in energy-harvesting WSNs.

Conclusions:

  • The ADP-MSS algorithm provides an optimal solution for multi-sensor scheduling in energy-harvesting WSNs.
  • The proposed method significantly enhances collaborative target tracking performance.
  • ADP-MSS offers a viable approach for extending the operational lifetime of WSNs through intelligent energy management and sensor scheduling.