Abstract
The ant colony optimization (ACO) is an evolutionary algorithm that tries to imitate the usual biological behavior of ants. Since Internet of Things (IoT) works by integrating and connecting devices of heterogeneous architecture, the size of the network increases rapidly. Therefore, in such situations ACO can be used to attain ideal solutions for large-scale optimization problems. As wireless sensors network (WSN) can integrate itself with IoT, the routing challenges faced by both of WSN and IoT are similar. To cope with the dynamics of the environment many intelligent routing algorithms have been designed. In this paper, an ACO-based routing algorithm for IoT networks has been proposed to analyze and enhance the scalability of the network, by minimizing the delay of the time critical applications. This would help in finding the optimal path for data transmission, and improve the efficiency of IoT communications. The proposed algorithm is simulated using network simulators (NS-2) that showed improvement in conserving energy when compared to the traditional ACO-based routing. Our proposed scheme prolonged the network lifetime and was found to have a 20% more packet delivery ratio, 19% reduced end-to-end delay and almost consumed 78% less energy.
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References
Aggarwal R, Mittal A, Kaur R. Various optimization techniques used in Wireless Sensor Networks. Int Res J Eng Technol (IRJET). 2016;3(6):2085–90.
Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 2005;3(3):325–49. https://doi.org/10.1016/j.adhoc.2003.09.010.
Bao R, Pan H, Dong Q, Yu L, Shao L. Ant colony-based routing algorithm for wireless sensor networks. Chinese J Sens Actuat. 2011;24(11):1644–8.
Bijarbooneh FH, et al. Cloud-assisted data fusion and sensor selection for internet of things. IEEE Internet Things J. 2016;3(3):257–68. https://doi.org/10.1109/JIOT.2015.2502182.
Chakraborty I, Chakraborty A, Das P. Sensor selection and data fusion approach for IoT applications. Adv Intell Syst Comput. 2019. https://doi.org/10.1007/978-981-13-1280-9_2.
Chakraborty I and Hussain MA. A simple joint routing and scheduling algorithm for a multi-hop wireless network. In: 2012 International Conference on Computer Systems and Industrial Informatics, ICCSII 2012. 2012. https://doi.org/10.1109/ICCSII.2012.6454620.
Chakraborty I and Sarmah U. A simple routing algorithm for Multi-hop Wireless Network. In: Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015. 2015. https://doi.org/10.1109/ISCO.2015.7282314.
Colorni A, et al. Distributed optimization by ant colonies. In: Proc. on European conference on artificial life. Elsevier Publishing; 1991. p. 134–42.
Colorni A, Dorigo M, Maniezzo V. An investigation of some properties of an “Ant algorithm.” PPSN'92. Belgium: Elsevier Publishing; 1992. p. 509–20.
Devi MD, Geetha K, Saranyadevi K. Content based routing using information centric network for IoT. Procedia Comput Sci. 2017;115:707–14. https://doi.org/10.1016/j.procs.2017.09.145.
Djukanovic G, Popovic G, Kanellopoulos D. Scaling complexity comparison of an ACO-based routing algorithm used as an IoT network core. J Inf Technol Appl (Banja Luka) - APEIRON. 2020;20(2):73–80. https://doi.org/10.7251/jit2002073dj.
Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Comput Intell Mag. 2006;1(4):28–39.
Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybernetics). 1996;26(1):29–41. https://doi.org/10.1109/3477.484436.
Dorigo M, Maria L. Ant colonies for the travelling salesman problem. Biosystems. 1997;43:73–81.
Duan P, Al Y. Research on an improved ant colony optimization algorithm and its application. Int J Hybrid Inf Technol. 2016;9(4):223–34. https://doi.org/10.14257/ijhit.2016.9.4.20.
Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform. 2005;19(1):43–53. https://doi.org/10.1016/j.aei.2005.01.004.
Gambardella LM, Taillard D, Dorigo M. Ant colonies for the quadratic assignment problem. J Oper Res Soc. 1999;50(2):167–76. https://doi.org/10.1057/palgrave.jors.2600676.
Gupta V, Sharma SK, et al. Cluster head selection using modified ACO BT. In: Das KN, et al., editors. Proceedings of Fourth International Conference on soft computing for problem solving. New Delhi: Springer India; 2015. p. 11–20.
Heinzelman WB, Chandrakasan AP, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun. 2002;1(4):660–70. https://doi.org/10.1109/TWC.2002.804190.
Hu H. Trust based secure and energy-efficient protocol for wireless sensor networks. In: IEEE access, vol. 10. 2022. p. 10585–96.
Khoshkangini R and Zaboli S. Efficient routing protocol via Ant Colony Optimization (ACO) and Breadth First Search (BFS). 2015, p. 374–81. https://doi.org/10.1109/iThings.2014.69.
Kooshari A, et al. An optimization method in wireless sensor network routing and IoT with water strider algorithm and ant colony optimization algorithm. Evolut Intell. 2023. https://doi.org/10.1007/s12065-023-00847-x. (0123456789).
Maniezzo V, Colorni A. The ant system applied to the quadratic assignment problem. IEEE Trans Knowl Data Eng. 1999;11(5):769–78. https://doi.org/10.1109/69.806935.
Nayyar A, Singh R. Ant colony optimization (ACO) based routing protocols for wireless sensor networks (WSN): a survey. Int J Adv Comput Sci Appl. 2017;8(2):148–55.
Nayyar A, Singh R. IEEMARP—a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks. Multimed Tools Appl. 2020. https://doi.org/10.1007/s11042-019-7627-z.
Sharmin A, Anwar F and Motakabber SMA. A noble approach of ACO Algorithm for WSN. In: Proceedings of the 2018 7th International Conference on Computer and Communication Engineering, ICCCE 2018, 2018, p. 152–6. https://doi.org/10.1109/ICCCE.2018.8539295.
Srivastava A, Mishra PK. A survey on WSN Issues with its heuristics and meta-heuristics solutions, wireless personal communications. Springer; 2021. https://doi.org/10.1007/s11277-021-08659-x.
Wang Y, et al. Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks. J China Univ Posts Telecommun. 2014;21(1):43–59. https://doi.org/10.1016/S1005-8885(14)60267-3.
Liu X, Li S, Wang M. An ant colony based routing algorithm for wireless sensor network. Int J Future Gener Commun Netw. 2016;9(6):75–86. https://doi.org/10.14257/ijfgcn.2016.9.6.0.
Xue X et al. The basic principle and application of ant colony optimization algorithm. In: 2010 International Conference on Artificial Intelligence and Education (ICAIE), 2010, p. 358–60.
Yadav RK, Mahapatra RP. Energy aware optimized clustering for hierarchical routing in wireless sensor network. Comput Sci Rev. 2021;41: 100417. https://doi.org/10.1016/j.cosrev.2021.100417.
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This article is part of the topical collection “SWOT to AI-embraced Communication Systems (SWOT-AI)” guest edited by Somnath Mukhopadhyay, Debashis De, Sunita Sarkar and Celia Shahnaz.
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Chakraborty, I., Das, P. An Efficient ACO-based Routing and Data Fusion Approach for IoT Networks. SN COMPUT. SCI. 4, 808 (2023). https://doi.org/10.1007/s42979-023-02257-3
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DOI: https://doi.org/10.1007/s42979-023-02257-3