A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Heterogeneous wireless sensor networks (HWSNs) present significant challenges for energy-aware clustering due to nodes with varying energy levels and consumption constraints.
• Optimizing cluster heads (CHs) is crucial for minimizing energy consumption, reducing delay, and extending network lifetime in WSNs.
• Existing clustering approaches face difficulties in addressing the unique demands of HWSNs.

Purpose of the Study:

• To propose a novel genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme tailored for HWSNs.
• To optimize the selection of cluster heads (CHs) and their positions within the network to improve energy efficiency.
• To mitigate the 'hot spot' problem in WSNs by strategically deploying nodes.

Main Methods:

• Developed a GA-EMC scheme utilizing a genetic algorithm to determine optimal CHs and their locations.
• Calculated chromosome fitness based on distance, optimal CH selection, and residual energy of nodes.
• Implemented multi-hop communication to enhance energy efficiency in HWSNs.
• Strategically deployed supernodes in areas farther from the sink to balance energy consumption.

Main Results:

• The GA-EMC scheme demonstrated a significantly extended network lifetime in simulations.
• GA-EMC achieved improved network stability compared to existing clustering approaches.
• The proposed scheme effectively minimized communication delay in heterogeneous WSN environments.

Conclusions:

• The GA-EMC scheme offers a superior solution for energy-aware clustering in HWSNs.
• The genetic algorithm effectively optimizes CH selection and network configuration for prolonged operational life.
• GA-EMC provides a robust method for enhancing the performance and longevity of wireless sensor networks.