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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks.

Sihao Li1,2, Kyeong Soo Kim1, Linlin Zhang1

  • 1School of Advanced Technology, Xi'an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China.

Sensors (Basel, Switzerland)
|July 27, 2022
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Summary

Optimizing message bundling in wireless sensor networks (WSNs) reduces energy use. This study presents a novel scheme balancing bundle size and retransmissions for significant energy savings.

Keywords:
end-to-end delayenergy efficiencymessage bundlingtime synchronization accuracywireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) rely on battery-powered nodes, making energy efficiency critical for operational longevity.
  • Message bundling reduces transmissions but can increase delays, packet error rate (PER), and impact time synchronization.
  • Delay-sensitive applications require careful consideration of end-to-end latency and retransmission impacts.

Purpose of the Study:

  • To propose an optimal message bundling scheme for WSNs that minimizes total energy consumption.
  • To balance the trade-offs between message bundling, packet retransmissions, and link quality.
  • To address the limitations of existing methods regarding delays and PER in WSNs.

Main Methods:

  • Formulated an optimal message bundling problem as an integer nonlinear programming problem.
  • Developed and applied a self-adaptive global-best harmony search (SGHS) algorithm to solve the optimization problem.
  • Evaluated the scheme using the Cooja emulator with the Contiki-NG operating system.

Main Results:

  • The proposed optimal bundling scheme achieved significant energy savings compared to baseline and state-of-the-art methods.
  • Demonstrated up to 51.8% total energy consumption reduction compared to no bundling.
  • Showed an 8.8% improvement in energy savings over an existing integer linear programming model.

Conclusions:

  • The novel optimal message bundling scheme effectively reduces WSN energy consumption.
  • The SGHS algorithm provides an efficient solution for the complex optimization problem.
  • This approach offers a practical method for enhancing WSN performance in delay-sensitive applications.