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Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network
Dost Muhammad Saqib Bhatti1, Nasir Saeed2, Haewoon Nam3
1Department of Electronics and Communication Engineering, Hanyang University, Ansan 15588, Korea. saqib@hanyang.ac.kr.
Sensors (Basel, Switzerland)
|September 13, 2016
Summary
This study introduces a novel energy-saving cooperative spectrum sensing algorithm using fuzzy c-means clustering. It enhances detection probability while minimizing network energy consumption for improved wireless communication efficiency.
Area of Science:
- Wireless Communication
- Signal Processing
- Network Energy Efficiency
Background:
- Single sensor spectrum sensing is unreliable due to fading and shadowing.
- Cooperative spectrum sensing enhances reliability using spatial diversity.
- Existing cooperative methods increase network energy consumption and reduce efficiency.
Purpose of the Study:
- To propose a novel cluster-based cooperative spectrum sensing algorithm.
- To reduce energy wastage in wireless networks.
- To improve the efficiency and reliability of spectrum sensing.
Main Methods:
- Utilizing fuzzy c-means (FCM) clustering to form sensor clusters.
- Selecting cluster heads (CHs) based on location, SNR, and residual energy.
- Implementing a cooperative spectrum sensing strategy with selected CHs reporting to a fusion center (FC).
Main Results:
- Achieved maximum probability of detection under imperfect channel conditions.
- Significantly minimized network energy consumption compared to conventional schemes.
- Enhanced overall network efficiency through optimized cluster head selection.
Conclusions:
- The proposed cluster-based cooperative spectrum sensing algorithm effectively conserves energy.
- The algorithm improves detection probability while reducing energy expenditure.
- This approach offers a more efficient and reliable solution for spectrum sensing in wireless networks.
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