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.
This is a preview of subscription content, log in via an institution to check access.
References
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999) https://doi.org/10.1126/science.286.5439.509. https://www.science.org/doi/pdf/10.1126/science.286.5439.509
Buford, J.F., Yu, H.: Peer-to-peer networking and applications: synopsis and research directions. In: Shen, X., Yu, H., Buford, J., Akon, M. (eds.) Handbook of Peer-to-Peer Networking, pp. 3–45. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09751-0_1
Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Comput. Surv. 36(4), 335–371 (2004). https://doi.org/10.1145/1041680.1041681
Farooq, M., Farooq, M.: A comprehensive survey of nature-inspired routing protocols. In: Bee-Inspired Protocol Engineering: From Nature to Networks, pp. 19–52 (2009). https://doi.org/10.1007/978-3-540-85954-3_2
Farooq, M., Di Caro, G.A.: Routing protocols for next-generation networks inspired by collective behaviors of insect societies: an overview. In: Swarm Intelligence: Introduction and Applications, pp. 101–160 (2008). https://doi.org/10.1007/978-3-540-74089-6_4
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999). https://doi.org/10.1093/oso/9780195131581.001.0001
Li, K., Torres, C.E., Thomas, K., Rossi, L.F., Shen, C.-C.: Slime mold inspired routing protocols for wireless sensor networks. Swarm Intell. 5, 183–223 (2011)
Ayob, A., Majid, R.A., Hussain, A., Mustaffa, M.M.: Creativity enhancement through experiential learning. Adv. Nat. Appl. Sci. 6(2), 94–99 (2012)
Saleem, M., Di Caro, G.A., Farooq, M.: Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inf. Sci. 181(20), 4597–4624 (2011)
Wedde, H.F., Farooq, M., Lischka, M.: An evolutionary meta hierarchical scheduler for the Linux operating system. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3103, pp. 1334–1335. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24855-2_153
Ren, J., Meng, X.-H.: Cosmological model with viscosity media (dark fluid) described by an effective equation of state. Phys. Lett. B 633(1), 1–8 (2006)
Rubio-Largo, A., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: A multiobjective approach based on artificial bee colony for the static routing and wavelength assignment problem. Soft. Comput. 17(2), 199–211 (2013)
Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 83–94. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28646-2_8
Sim, K.M., Sun, W.H.: Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 33(5), 560–572 (2003)
Šešum-Čavić, V., Kühn, E.: Chapter 8 self-organized load balancing through swarm intelligence. In: Bessis, N., Xhafa, F. (eds.) Next Generation Data Technologies for Collective Computational Intelligence, pp. 195–224. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20344-2_8
Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in internet hosting centers. Adapt. Behav. 12(3–4), 223–240 (2004)
Wong, L.-P., Low, M.Y.H., Chong, C.S.: A bee colony optimization algorithm for traveling salesman problem. In: 2008 Second Asia International Conference on Modelling & Simulation (AMS), pp. 818–823. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-57870-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57869-4
Online ISBN: 978-3-031-57870-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)