Abstract
Sparse Unstructured Peer-to-Peer (P2P) networks pose unique challenges for efficient and scalable routing. The nodes have limited and partial information about the network and the location of the desired data, which makes it hard to find the optimal or shortest path to the data. This article presents Honey Bee Optimization in P2P Networks (HBO_P2P), a unique routing algorithm inspired by the foraging behavior of honey bees. The proposed algorithm aims 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). Message overhead, packet delay, hop count, and throughput are among the important parameters that form the basis of the comparison. Our findings show that our suggested routing algorithm HBO_P2P is effective at resolving issues unique to unstructured P2P networks. The algorithm showcases notable improvements across multiple performance metrics when compared to established optimization techniques.
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Verma, A., Thakur, S., Kumar, A., Mahato, D.P. (2024). Honey Bee Inspired Routing Algorithm for Sparse Unstructured P2P Networks. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-031-57870-0_16
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