Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoT), enabling applications like smart cities and intelligent transportation.
• Effective cluster head (CH) selection in WSNs is vital for optimizing energy consumption due to limited node power.
• Existing CH selection mechanisms often overlook critical node parameters, impacting network efficiency.

Purpose of the Study:

• To develop an advanced evolutionary game-based approach for efficient CH selection in WSNs.
• To enhance energy consumption optimization by integrating multiple sensor node parameters.
• To address the selfish nature of smart nodes in the CH selection process.

Main Methods:

• Designed a novel evolutionary game-based approach for CH selection.
• Incorporated vital parameters including remaining energy, selfishness, hop-level, density, and connectivity degree.
• Modeled network nodes as intelligent and independent strategists.

Main Results:

• The proposed method demonstrated significantly improved performance compared to traditional evolutionary game-based approaches.
• The integration of multiple parameters led to more efficient energy utilization in WSNs.
• The approach effectively managed selfish node behaviors in the network.

Conclusions:

• The developed evolutionary game-based approach offers a superior solution for CH selection in WSNs.
• Considering a comprehensive set of node and network parameters enhances energy efficiency and network performance.
• This work provides a robust framework for intelligent CH selection in IoT applications.