Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) face significant resource constraints, necessitating efficient management for reliable operation.
• Effective network management systems are crucial for optimizing WSN performance and longevity.
• Existing solutions may not adequately address the unique power demands of WSN applications.

Purpose of the Study:

• To model, design, and propose a novel homogeneous WSN hybrid architecture.
• To introduce a dedicated power utilization optimization strategy, Hybrid Energy-Efficient Power manager Scheduling (HEEPS), tailored for WSNs.
• To enhance energy efficiency in WSNs through advanced power management techniques.

Main Methods:

• Implementing a hybrid architecture combining time-out Dynamic Power Management (DPM) Intertask and Dynamic Voltage and Frequency Scaling (DVFS).
• Utilizing Global Earliest Deadline First (GEDF) scheduling with novel tests to mitigate the Dhall effect.
• Predicting, defining, and modeling sensor node behavior and associated energy management mechanisms for optimized consumption.
• Employing the STORM simulator for performance evaluation and comparative analysis.

Main Results:

• The proposed HEEPS strategy effectively schedules tasks to dynamically utilize available energy.
• Performance evaluation demonstrates significant energy savings, with estimated gains of up to 50%.
• Comparative analysis shows HEEPS outperforming various state-of-the-art approaches in energy efficiency.

Conclusions:

• HEEPS provides an effective solution for managing power consumption in resource-constrained WSNs.
• The proposed architecture and scheduling strategy significantly enhance energy efficiency.
• This work contributes a valuable tool for extending the operational lifespan of wireless sensor networks.