Adaptive and scalable energy aware clustering for mobile wireless sensor networks using a density driven approach

Area of Science:

• Computer Science
• Network Engineering
• Wireless Communication

Background:

• Wireless Sensor Networks (WSNs) face energy efficiency and lifetime challenges, especially with mobile nodes and varied energy levels.
• Existing clustering protocols like DL-HEED, MEDF, and EEMLCR struggle with dynamic WSN environments, impacting data transmission and load balancing.

Purpose of the Study:

• Introduce Mobility & Energy Adaptive Density-based Clustering (MEADC) to improve WSN scalability and adaptability.
• Address limitations of traditional protocols in energy efficiency, network lifetime, and data transmission for mobile WSNs.

Main Methods:

• Segmenting the network into concentric regions for enhanced adaptability.
• Utilizing a hybrid metric (residual energy, geometric centrality, local node density) for cluster formation.
• Implementing rumor-based load dissemination and an energy-centrality score for cluster head selection.

Main Results:

• MEADC reduced end-to-end data transmission delays by up to 150 seconds.
• Network lifetime was extended by 28%-51% compared to baseline protocols.
• Packet delivery efficiency saw a significant improvement of 28%.

Conclusions:

• MEADC offers a robust and energy-efficient clustering framework for WSNs.
• The proposed strategy is well-suited for mobile, real-time, and energy-critical WSN applications.
• MEADC demonstrates superior performance in dynamic environments compared to traditional protocols.