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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
654
Neural Network-Based Routing Energy-Saving Algorithm for Wireless Sensor Networks
Lili Pang1, Jiaye Xie1, Qiqing Xu1
1Industrial Center, Nanjing Institute of Technology, Nanjing 211167, China.
Computational Intelligence and Neuroscience
|July 11, 2022
Summary
This study introduces the SMPSO-BP algorithm to improve energy efficiency in wireless sensor networks. The new routing algorithm significantly reduces energy consumption compared to existing methods.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Mobile Internet technology advancements drive smart object development, but node size and battery limitations pose energy challenges.
- Wireless sensor network (WSN) energy-saving technology is crucial, with routing improvements being a key focus.
- Existing routing algorithms, like LEACH, face limitations in optimizing energy consumption for smart objects.
Purpose of the Study:
- To analyze data transmission energy consumption in smart object networks.
- To propose and evaluate an improved routing algorithm for WSNs to enhance energy efficiency.
- To address the energy bottleneck in smart object mobile networks.
Main Methods:
- In-depth analysis of the LEACH routing algorithm.
- Development and proposal of a novel improved algorithm, SMPSO-BP.
- Simulation and experimental testing to validate the proposed algorithm's performance and reliability.
Main Results:
- The SMPSO-BP algorithm demonstrates faster convergence (approx. 600 iterations) compared to LEACH and improved LEACH.
- Experimental results show SMPSO-BP consumes less energy during wireless sensor network routing.
- The proposed energy-saving algorithm integrated with neural network data fusion is proven feasible.
Conclusions:
- The SMPSO-BP algorithm offers superior performance in terms of convergence speed and energy efficiency for WSNs.
- This research provides a viable solution for the energy consumption challenges in smart object networks.
- The findings support the feasibility of neural network data fusion mechanisms for energy-saving in WSNs.
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