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Published on: September 8, 2023
A transmission power optimization with a minimum node degree for energy-efficient wireless sensor networks with
Yi-Ting Chen1, Mong-Fong Horng, Chih-Cheng Lo
1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan. ytchen@bit.kuas.edu.tw
This study proposes an optimized transmission power approach for wireless sensor networks. It enhances network lifetime and connection quality by balancing energy efficiency and full reachability.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Wireless Sensor Networks (WSNs) require optimized transmission power for longevity and reliable connectivity.
- Uncontrolled transmission power leads to interference or communication failures between nodes.
- Node degree and distribution are critical factors influencing transmission power optimization.
Purpose of the Study:
- To propose an optimization approach for energy-efficient and fully reachable WSNs.
- To introduce a transmission range adjustment model based on minimum node degree.
- To balance energy efficiency and network reachability for ideal transmission range.
Main Methods:
- Developed a topology control model for optimizing transmission range based on node degree and density.
- Implemented an adjustment model considering the trade-off between energy efficiency and full reachability.
- Utilized connectivity and reachability as performance indices for network quality evaluation.
Main Results:
- Demonstrated the practicability of the proposed framework through simulation results.
- Evaluated network connection quality using connectivity and reachability indices.
- Analyzed the relationship between indices and node degrees to generalize node density characteristics.
Conclusions:
- The proposed approach offers a reliable and feasible method for WSN transmission power optimization.
- The findings benefit future real-world deployments of energy-efficient and fully reachable WSNs.
- The study provides insights into network characteristics under varying node densities.
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