Design of a high efficiency addressing model for IPv6 Networks via bioinspired computing
Keywords:
Addressing, Genetic, Algorithm, Energy, Efficiency, SpeedAbstract
IPv6 Networks require efficient addressing schemes that must ensure location-awareness for higher energy efficiency under different network conditions. To perform this task, a large number of QoS (Quality of Service) aware addressing models are discussed by researchers, but most of these models adopt a static processing method for addressing, which limits their scalability for large sized networks. Models that support dynamic addressing are very complex to implement, and thus reduce QoS performance for large-scale networks. To overcome these limitations, this text proposes design of a novel high efficiency addressing model for IPv6 Networks via Genetic Algorithm based optimization techniques. The proposed model uses node locations, and energy levels to identify base addressing & sub netting schemes. These schemes are evaluated in terms of transmission energy levels, reception energy levels, and distance between frequently communicating nodes. Based on this evaluation, the model is able to identify similar subnet addresses for nearby nodes that have similar energy signatures. This assists in formation of node clusters, and improving its addressing efficiency under different scenarios. The model was tested under small, medium & large-scale networks, and its addressing efficiency levels were observed. Based on this observation, it was concluded that the proposed model showcased 10.3% lower energy consumption, 8.5% better speed, and 15.4% higher addressing efficiency when compared with other state-of-the-art addressing models. Due to this increase in performance, the proposed model is capable of deployment for large-scale IPv6 Networks.
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