Fuzzy-based Firefly and ACO Algorithm for Densely Deployed WSN
Most of the wireless sensor networks (WSNs) used in healthcare and security sectors are affected by the battery constraints, which cause a low network lifetime problem and prevents these networks from achieving their maximum performance. It is anticipated that by combining fuzzy logic (FL) approximation reasoning approach with WSN, the complex behavior of WSN will be easier to handle. In healthcare, WSNs are used to track activities of daily living (ADL) and collect data for longitudinal studies. It is easy to understand how such WSNs could be used to violate people’s privacy. The main aim of this research is to address the issues associated with battery constraints for WSN and resolve these issues. Such an algorithm could be successfully applied to environmental monitoring for healthcare systems where a dense sensor network is required and the stability period should be high.
Keywordsclustering, firefly, WSN, ant colony optimization, fuzzy, wireless sensor healthcare network, FIS,
References1. F. Fanian, M.K. Rafsanjani, Cluster-based routing protocols in wireless sensor networks: A survey based on methodology, Journal of Network and Computer Applications, 142: 111–142, 2019, doi: 10.1016/j.jnca.2019.04.021.
2. M. Mittal, The aspect of ESB with wireless sensor network, [in:] R.S. Bhadoria, N. Chaudhari, G.S. Tomar, S. Singh [Eds.], Exploring Enterprise Service Bus in the Service-Oriented Architecture Paradigm, IGI Global, USA, pp. 142–155, 2017.
3. W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, [in:] Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1–10, 2000, doi: 10.1109/HICSS.2000.926982.
4. R. Rai, P. Rai, Survey on energy-efficient routing protocols in wireless sensor networks using game theory, [in:] H. Sarma, S. Borah, N. Dutta [Eds.], Advances in Communication, Cloud, and Big Data, Springer, Singapore, pp. 1–9, 2019.
5. L.A. Zadeh, Fuzzy Sets, [in:] Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, G.J. Klir, B. Yuan [Eds.], World Scientific, pp. 394–432, 1996, doi: 10.1142/9789814261302_0021.
6. J.S. Lee, W.L. Cheng, A fuzzy-logic-based clustering approach for wireless sensor networks using energy prediction, IEEE Sensor Journal, 12(9): 2891–2897, 2012, doi: 10.1109/JSEN.2012.2204737.
7. R. Fotohi, S.F. Bari, A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms, The Journal of Supercomputing, 76: 6860–6886, 2020, doi: 10.1007/s11227-019-03131-x.
8. R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, P. Singh, Prediction of heart disease using a combination of machine learning and deep learning, [in:] A.A. Abd El-Latif [Ed.], Computational Intelligence and Neuroscience, 2021: Article ID 8387680, pp. 1–11, 2021, doi: 10.1155/2021/8387680.
9. V.K. Arora, V. Sharma, M. Sachdeva, ACO optimized self-organized tree-based energy balance algorithm for wireless sensor network, Journal of Ambient Intelligence and Humanized Computing, 10(12): 4963–4975, 2019, doi: 10.1007/s12652-019-01186-5.
10. R. GhasemAghaei, A. Mahfujur Rahman, M. Abdur Rahman, W. Gueaieb, A. El Saddik, Ant colony-based many-to-one sensory data routing in Wireless Sensor Networks, [in:] 2008 IEEE/ACS International Conference on Computer Systems and Applications, pp. 1005–1010, 2008, doi: 10.1109/AICCSA.2008.4493668.
11. A. Sharmin, F. Anwar, S.M.A. Motakabber, A novel bio-inspired routing algorithm based on ACO for WSNs, Bulletin of Electrical Engineering and Informatics, 8(2): 718–726, 2019, doi: 10.11591/eei.v8i3.1492.
12. T. Camilo, C. Carreto, J.S. Silva, F. Boavida, An energy-efficient ant-based routing algorithm for wireless sensor networks, [in:] M. Dorigo, L.M. Gambardella, M. Birattari, A. Martinoli, R. Poli, T. Stützle [Eds.], International workshop on ant colony optimization and swarm intelligence, ANTS 2006. Lecture Notes in Computer Science, vol. 4150, Springer, Berlin, Heidelberg, pp. 49–59, 2006, doi: 10.1007/11839088_5.
13. X.S. Yang, Firefly algorithms for multimodal optimization, [in:] O. Watanabe, T. Zeugmann [Eds.], Stochastic Algorithms: Foundations and Applications. SAGA 2009. Lecture Notes in Computer Science, vol. 5792, Springer, Berlin, Heidelberg, 2009, doi: 10.1007/978-3-642-04944-6_14.
14. S. Arora, S. Singh, The firefly optimization algorithm: convergence analysis and parameter selection, International Journal of Computer Applications, 69(3): 48–52, 2013.
15. D. Chandirasekaran, T. Jayabarathi, A case study of bio-optimization techniques for wireless sensor network in node location awareness, Indian Journal of Science and Technology, 8(31): 1–9, 2015, doi: 10.17485/ijst/2015/v8i31/67726.
16. W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications, 1(4): 660–670, 2002, doi: 10.1109/TWC.2002.804190.
17. T. Sharma, B. Kumar, F-MCHEL: Fuzzy based master cluster head election protocol in wireless sensor network, International Journal of Computer Science and Telecommunication, 3(10): 8–13, 2012.
18. H. Bagci, A. Yazici, An energy-aware fuzzy unequal clustering algorithm for wireless sensor networks, [in:] International Conference on Fuzzy Systems, IEEE, pp. 1–8, July, 2010, doi: 10.1109/FUZZY.2010.5584580.
19. J.M. Kim, S.H. Park, Y.J. Han, T.M. Chung, CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks, [in:] Proceedings of the 10th International Conference on Advanced Communication Technology, pp. 654–659, 2008, doi: 10.1109/ICACT.2008.4493846.
20. A. Kishor, C. Chakraborty, Artificial intelligence and internet of things based healthcare 4.0 monitoring system, Wireless Personal Communications, 2021, doi: 10.1007/s11277-021-08708-5.
21. J.-Y. Kim, T. Sharma, B. Kumar, G.S. Tomar, K. Berry, W.-H. Lee, Intercluster ant colony optimization algorithm for wireless sensor network in dense environment, International Journal of Distributed Sensor Networks, 10(4): 457402, 2014, doi: 10.1155/2014/457402.
22. P.S. Mehra, M.N. Doja, B. Alam, Fuzzy based enhanced cluster head selection (FBECS) for WSN, Journal of King Saud University-Science, 32(1): 390–401, 2020, doi: 10.1016/j.jksus.2018.04.031.
23. T. Sharma, A. Mohapatra, G. Tomar, Fuzzy-based DBSCAN algorithm to eect master cluster head and enhance the network lifetime and avoid redundancy in wireless sensor network, [in:] D. Gupta, A. Khanna, S. Bhattacharyya, A.E. Hassanien, S. Anand, A. Jaiswal [Eds.], International Conference on Innovative Computing and Communications, Advances in Intelligent Systems and Computing, vol. 1166: Springer, Singapore, 2021, doi: 10.1007/978-981-15-5148-2_88.
24. M. Baskaran, C. Sadagopan, Synchronous firefly algorithm for cluster head selection in WSN, The Scientific World Journal, 2015: Article ID 780879, 7 pages, 2015, doi: 10.1155/2015/780879.
25. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80: 23983–24004, 2021, doi: 10.1007/s11042-021-10840-0.
26. S. Radhika, P. Rangarajan, Fuzzy based sleep scheduling algorithm with machine learning techniques to enhance energy efficiency, Wireless Personal Communications, 118(4): 3025–3044, 2021, doi: 10.1007/s11277-021-08167-y.
27. B. Prabhu, N. Balakumar, Enhanced clustering methodology for lifetime maximization in dense WSN fields, International Journal for Technological Research in Engineering, 4(2), 2016, https://ssrn.com/abstract=2875366.
28. A. Kishor, C. Chakarbarty, Task offloading in fog computing for using smart ant colony optimization, Wireless Personal Communications, 2021, doi: 10.1007/s11277-021-08714-7.
29. D. Ushakov, E. Goryunova, K. Shatila, Assessing the impact of environmental management systems on corporate and environmental performance, IOP Conference Series: Earth and Environmental Science, 937(2), p. 022038, 2021, doi: 10.1088/1755-1315/937/2/022038.