An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) are crucial for Internet of Things (IoT) applications, especially in safety-critical areas like smart city disaster management.
• Ensuring the longevity and availability of WSNs is paramount for effective disaster response.
• Clustering is a vital routing technique for optimizing energy efficiency in WSNs.

Purpose of the Study:

• To develop an evolutionary clustering and routing method for WSNs tailored to disaster management scenarios.
• To enhance energy consumption management in WSN nodes within disaster-affected areas.
• To improve the overall performance and reliability of WSNs in critical applications.

Main Methods:

• A two-phase approach combining hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for optimal cluster head selection.
• Implementation of a PSO-based multi-hop routing system featuring enhanced tree encoding.
• Modification of the data packet format to improve transmission efficiency.

Main Results:

• The proposed method demonstrates superior performance in disaster scenarios compared to existing approaches.
• Significant improvements observed in overall residual energy and the number of live nodes.
• Enhanced network coverage and a higher packet delivery ratio were achieved.

Conclusions:

• The evolutionary clustering and routing method effectively manages energy consumption in WSNs for disaster management.
• The approach offers a robust solution for extending WSN lifetime and improving data reliability in critical situations.
• This method presents a promising advancement for smart city safety applications utilizing WSNs.