Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) comprise numerous resource-constrained sensor nodes.
• Effective resource management is crucial for extending WSN operational lifetime.
• Clustering is a key strategy for optimizing energy conservation and network longevity in WSNs.

Purpose of the Study:

• To propose a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm for WSNs.
• To enhance energy efficiency and data rates in WSN communication.
• To improve overall network performance metrics like lifetime, energy saving, and delay.

Main Methods:

• Developed the ETAAR algorithm based on Cooperative Game Theory (CGT).
• Utilized CGT for energy-efficient cluster head selection.
• Employed a coalition game approach considering energy, throughput, and delay for adaptive time slot transmission.

Main Results:

• ETAAR significantly outperforms existing routing protocols.
• Achieved a 48% extension in network lifetime.
• Demonstrated a 60% energy saving and a 52.5% reduction in average end-to-end delay.

Conclusions:

• The proposed ETAAR algorithm effectively optimizes routing in WSNs.
• ETAAR provides a robust solution for energy conservation and throughput enhancement.
• The algorithm shows superior performance in terms of residual energy, packet delivery ratio, and reduced network delay.