Unstructured peer-to-peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routingalgorithm inspired by the foraging behavior of honey bees named Honey bee...
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Unstructured peer-to-peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routingalgorithm inspired by the foraging behavior of honey bees named Honey bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps. We worked on the scalable and efficient routing in unstructured network. We used the Honey bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P ***
Due to extreme complexity in nowadays networks, routing becomes a challenging task. This problem is especially delicate in unstructured P2P networks, as there is neither a global view on the network nor a global addre...
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Due to extreme complexity in nowadays networks, routing becomes a challenging task. This problem is especially delicate in unstructured P2P networks, as there is neither a global view on the network nor a global address mapping. Although different conventional solutions are commercially available, swarm-intelligent approaches are promising in case of frequently changing conditions in P2P networks. In this article, an approach inspired by Dictyostelium discoideum slime molds and bees with distributive and autonomous properties is proposed. Both bio-mechanisms are "tailored" for routing in unstructured P2P systems, resulting in swarm-inspired routingalgorithms, SMNet and beeNet. They are compared with three swarm-based routingalgorithms and two conventional approaches. The benchmarks include parameter sensitivity -, comparative -, statistical - and scalability-analysis. SMNet outperforms the other algorithms in the comparative analysis regarding the average data packet delay, especially for bigger network sizes and data packet traffic levels. Both algorithms show good scalability.
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