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Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA
Mohammed Kaddi1, Mohammed Omari2, Khouloud Salameh2
1LDDI Laboratory, Mathematics and Computer Science Department, University of Adrar, Adrar 01000, Algeria.
Sensors (Basel, Switzerland)
|August 29, 2024
Summary
This study introduces an Energy Optimization Approach (EOAMRCL) for Wireless Sensor Networks (WSNs) using Grey Wolf Optimization (GWO). It significantly improves network lifetime and reduces energy consumption.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Wireless Sensor Networks (WSNs) face critical energy constraints impacting survivability.
- Prolonging network life while meeting application goals is essential for WSNs.
Purpose of the Study:
- To present an Energy Optimization Approach using Multi-objective Grey Wolf Optimization (EOAMRCL) for WSNs.
- To enhance energy efficiency through optimal duty-cycle scheduling, transmission power, and routing paths.
Main Methods:
- A centralized, hierarchical network architecture was employed.
- Grey Wolf Optimization (GWO) was integrated to determine optimal cluster heads (CHs).
- An enhanced CSMA/CA mechanism with sleep/activate modes and node pairing was utilized.
Main Results:
- EOAMRCL demonstrated superior performance in network lifetime and energy consumption compared to existing protocols.
- The approach effectively reduced collisions and improved channel assessment accuracy.
- Optimized duty-cycle scheduling and routing paths led to significant energy savings.
Conclusions:
- The integration of GWO and the enhanced CSMA/CA mechanism is effective for optimizing WSN energy efficiency.
- EOAMRCL significantly prolongs network lifetime and reduces energy consumption in WSNs.
- The proposed approach offers a robust solution for energy-constrained WSN applications.
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