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CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams.

Chengpei Tang1,2, Nian Yang3

  • 1School of Engineering, Sun Yat-Sen University, Guangzhou 510006, China. tchengp@mail.sysu.edu.cn.

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|August 17, 2016
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Summary

This study introduces an energy-efficient data aggregation protocol for wireless sensor networks using a bio-level Voronoi diagram and particle swarm optimization. It minimizes network energy consumption and balances node energy for better performance.

Keywords:
Voronoi diagramcollaborative data aggregationemerging sensor networksmulti-hop wireless networktime synchronization

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) face challenges in energy efficiency and data aggregation.
  • Existing protocols often struggle with balanced energy consumption and optimal routing.

Purpose of the Study:

  • To propose an energy-efficient collaborative data aggregation protocol for WSNs.
  • To integrate topology control, Multiple Access Control (MAC), and routing for optimized performance.
  • To minimize overall network energy consumption and balance node energy.

Main Methods:

  • Utilizing a bio-level Voronoi diagram for network organization and data gathering.
  • Implementing optimal clustering for sensor nodes.
  • Employing particle swarm optimization (PSO) for inter-cluster communication path selection.
  • Minimizing maximum next hop distance and hop count for efficient routing.

Main Results:

  • The proposed protocol demonstrates superior energy efficiency compared to traditional routing protocols.
  • Synchronization accuracy is significantly improved.
  • Achieved balanced energy consumption across all nodes in the network.
  • Reduced overall network energy consumption through optimized multi-hop communication.

Conclusions:

  • The bio-level Voronoi diagram-based collaborative data aggregation protocol offers substantial energy savings in WSNs.
  • PSO-based path optimization effectively minimizes energy consumption and hop count.
  • The protocol enhances network lifetime and reliability through balanced energy distribution.