Design of a Hybrid Bio-inspired Model for Improving Addressing Capabilities of IPv6 Networks

  • Reema Roychaudhary Assistant Professor, Computer Engineering, St.Vincent Pallotti COE&T, Nagpur, Maharashtra
  • Rekha Shahapurkar Computer Science & Engineering, Oriental University, Indore, Madhya Pradesh, India
Keywords: Addressing, BCO, Bioinspired, Cognitive, Delay, Stochastic GA, Hybrid, IPv6, PSO, Social, Energy.


Addressing in IPv6 networks is a complex task, that involves multiple decision criterion that include base addressing, subnet addressing, location & energy aware addressing, and temporal performance aware addressing constraints. To incorporate these constraints a wide variety of models are proposed by researchers, and most them utilize location-aware addressing and do not consider multimodal parameters. Schemes that consider these parameters are either highly complex, or cannot be scaled for heterogeneous network scenarios. To overcome these limitations, this text proposes design of a novel hybrid bioinspired model that assists in improving addressing capabilities of IPv6 networks. The proposed model bee colony optimization (BCO), genetic algorithm (GA), and particle swarm optimization (PSO) in order to improve addressing quality for different network types. Initially, GA is used to stochastically assign location-specific addresses to a simulated network, which is incrementally tuned by PSO via a cognitive & social learning process. The fine-tuned addresses are further optimized via integration of BCO model, which assists in integrating energy awareness. Final addresses are initially simulated with exhaustive communication test, and then deployed to real-time networks for optimized operations. Due to which, the assigned addresses are observed to be delay & energy efficient, thereby assisting in deploying them for real-time use cases. The proposed addressing model was tested under different scaled networks, and an energy efficiency of 8.3%, with delay reduction of 6.5% was achieved when compared with various state-of-the-art methods, which assists in deploying the model for multiple scaled network scenarios


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