Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Area of Science:

• Marine Science
• Environmental Monitoring
• Computer Science

Background:

• Marine pollution monitoring requires efficient, low-power equipment recovery.
• Wireless sensor networks (WSNs) offer potential for low-energy marine observation beacon recovery.
• Reducing and balancing energy consumption in WSNs are critical challenges.

Purpose of the Study:

• To present an energy-saving clustering algorithm for WSNs to address marine monitoring challenges.
• To improve the efficiency and reduce the energy consumption of marine observation beacon recovery.
• To enhance the network life cycle and recovery strategy of marine monitoring equipment.

Main Methods:

• Developed a novel energy-saving clustering algorithm (KFNS) integrating k-means and fuzzy logic.
• Implemented a three-phase approach: distributed boundary node selection (monitoring), cluster routing for energy balancing, and fuzzy logic with Dijkstra/DFS for optimal recovery path.
• Utilized fuzzy membership functions and depth-first search (DFS) for node weight determination and optimal recovery order.

Main Results:

• The proposed KFNS algorithm demonstrated a longer network life cycle compared to existing methods.
• Experimental results indicate a more efficient recovery strategy with balanced energy consumption.
• The algorithm effectively addresses the extreme imbalance of energy in WSN nodes.

Conclusions:

• The KFNS algorithm offers a viable solution for energy-efficient marine monitoring and equipment recovery.
• The proposed method significantly improves network longevity and recovery effectiveness.
• This approach contributes to sustainable marine environmental monitoring through optimized WSN performance.