Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks

Area of Science:

• Biomedical Engineering
• Wireless Sensor Networks
• Nanotechnology

Background:

• Intrabody nanonetworks (IBNNs) utilize nanoscale devices for physiological data collection, facing challenges in energy, storage, and computation.
• Efficient routing protocols are crucial for the functionality and longevity of IBNNs due to device constraints.

Purpose of the Study:

• To propose a novel, energy-efficient routing scheme for IBNNs that addresses limitations of nanoscale devices.
• To enhance network lifetime, reduce end-to-end delay, and improve packet delivery ratio in IBNNs.

Main Methods:

• Developed a routing scheme combining fuzzy logic for correlation region selection and a bio-inspired firefly algorithm for nano biosensor (NBS) nomination.
• Fuzzy logic selects energy-rich regions with valuable detected information for data aggregation.
• Firefly algorithm optimizes NBS selection based on transmission history and residual energy to prevent premature exhaustion.

Main Results:

• The proposed scheme significantly conserves energy by minimizing the transmission of correlated spatial data.
• Simulations demonstrated substantial improvements in energy savings, reduced end-to-end delay, and increased packet delivery ratio compared to the flooding scheme.
• Optimized selection of correlated regions and NBSs proved critical for enhancing network lifetime and data transmission efficiency.

Conclusions:

• The novel routing scheme effectively addresses energy constraints in IBNNs.
• The integration of fuzzy logic and firefly algorithm offers a promising approach for efficient data routing in nanoscale networks.
• This strategy enhances the overall performance and viability of IBNNs for diagnostic and therapeutic applications.