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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Performance Analysis of Cluster Formation in Wireless Sensor Networks.

Edgar Romo Montiel¹, Mario E Rivero-Angeles², Gerardo Rubino³

¹Instituto Politécnico Nacional-(CIC-IPN), Mexico City 07738, Mexico. eromom0900@alumno.ipn.mx.

Sensors (Basel, Switzerland)

|December 14, 2017

Summary

Optimizing transmission probability and cluster head selection in wireless sensor networks (WSNs) significantly reduces energy consumption. Intelligent methods improve performance but increase complexity and delay.

Keywords:

K-medoids clustering fuzzy C-means transmission probability

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Clustered wireless sensor networks (WSNs) are widely adopted for energy efficiency.
Key factors like transmission probability and cluster head selection are often overlooked.
Fixed transmission probabilities in WSNs can lead to excessive energy use.

Purpose of the Study:

To investigate the impact of transmission probability strategies on WSN performance.
To evaluate intelligent cluster head selection algorithms against random selection.
To analyze the influence of wireless channel errors on system metrics.

Main Methods:

Studied three transmission probability strategies: optimal, fixed, and adaptive.
Compared fuzzy C-means and k-medoids for intelligent cluster head selection against random methods.
Assessed energy consumption, successful transmission probability, and cluster formation latency.
Examined the effect of wireless channel errors.

Main Results:

Intelligent cluster head selection schemes significantly enhance system performance.
Adaptive transmission probability strategies offer better energy efficiency than fixed ones.
Higher complexity and selection delay are associated with intelligent schemes.
Wireless channel errors negatively impact performance across schemes.

Conclusions:

Optimizing transmission probability and employing intelligent cluster head selection are crucial for WSN efficiency.
The choice of strategy involves a trade-off between performance, complexity, and latency.
Further research should consider adaptive strategies and robust selection methods for real-world WSN deployments.