Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Internet of Things (IoT) devices are crucial for environmental sensing and disaster impact reduction but face energy constraints due to battery-operated sensors.
• Efficient energy consumption, particularly during data transmission in disaster-prone areas, is critical for extending the operational life of IoT networks.
• Clustering-based communication is a key strategy for reducing node energy depletion and enhancing network longevity.

Purpose of the Study:

• To develop and evaluate a novel hybrid bio-inspired algorithm for optimizing energy efficiency and network lifetime in IoT networks for disaster management.
• To address the limitations of existing clustering and routing protocols by incorporating disaster-relevant parameters like residual energy, distance to sink, and network coverage.

Main Methods:

• Proposed a hybrid Butterfly Optimisation Algorithm (BOA) for clustering and Particle Swarm Optimisation (PSO) for routing in IoT networks.
• Integrated key disaster-scenario parameters: node residual energy, distance to the sink, and network coverage into the clustering process.
• Compared the performance of the proposed BOA-PSO algorithm against benchmark protocols (LEACH, DEEC, PSO, PSO-GA, PSO-HAS) using residual energy, throughput, and network lifetime as metrics.

Main Results:

• The BOA-PSO algorithm demonstrated significant residual energy conservation, showing over 17% improvement in short-range and 10% in long-range scenarios.
• Achieved substantial throughput enhancements: 60% over LEACH, 53% over DEEC, and 37% over PSO.
• Reduced packet drops by 60% compared to LEACH and DEEC, and 30% compared to PSO, while increasing overall network lifetime by 10-20%.

Conclusions:

• The hybrid BOA-PSO algorithm offers superior energy efficiency and extended network lifetime for IoT applications in disaster management.
• The proposed approach effectively optimizes clustering and routing by considering critical parameters relevant to disaster environments.
• This research provides a robust solution for enhancing the reliability and performance of IoT networks during critical disaster response operations.