[1] Afsar, M.M. and Tayarani-Najaran, M.H. (2014). Clustering in sensor networks: literature survey. Journal of Network and Computer Applications, 46, 198–226. https://doi.org/10.1016/ j.jnca.2014.09.005
[2] Ajay, P., Nagaraj, B., Arunkumar, R. and Huang, R. (2023). Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol. Results in Physics, 51, 106747. https://doi.org/10.1016/j.rinp.2023.106747
[3] Alazab, M., Lakshmanna, K., Reddy, T., Pham, Q.V. and Maddikunta, P.K.R. (2021). Multi- objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities. Sustainable Energy Technologies and Assessments, 43. https://doi.org/10. 1016/j.seta.2020.100973
[4] Alrabea, A., Alzubi, O.A. and Alzubi, J.A. (2022). A task-based model for minimizing energy consumption in WSNs. Energy Systems, 13, 671–688. https://doi.org/10.1007/ s12667-019-00372-w
[5] Arikumar, K.S., Natarajan, V., Satapathy, S.C. and Prathiba, S.B. (2022). DCMI: Dynamic clustering approach using multi-verse optimizer for fog-assisted IoT devices. Soft Computing. https://doi.org/10.21203/rs.3.rs-698256/v1
[6] Aruchamy, P., Gnanaselvi, S., Sowndarya, D. and Naveenkumar, P. (2023). An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks. Concurrency and Computation: Practice and Experience, 35(23). https://doi.org/10.1002/cpe.7818
[7] Baniata, M., Reda, H.T., Chilamkurti, N. and Abuadbba, A. (2021). Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond. Intelligent Sensors, 21. https://doi.org/10.3390/s21020537
[8] Baranidharan, B. and Santhi, B. (2016). DUFC: Distributed Load balancing unequal clustering in wireless sensor network using fuzzy approach. Applied Soft Computing, 40, 495–506. http:// dx.doi.org/10.1016/j.asoc.2015.11.044
[9] Chahid, Y., Benabdellah, M. and Azizi, A. (2017). Internet of things security. In Proceedings of the International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS).
[10] Daanoune, I., Baghdad, A. and Ballouk, A. (2020). An enhanced energy-efficient routing protocol for wireless sensor network. International Journal of Electrical and Computer Engineering, 10(5), 5462–5469. https://doi.org/10.11591/ijece.v10i5.pp5462-5469
[11] Dhandapani, A., Venkateswarib, P., Sivakumarc, T., Rameshd, C. and Vanitha, P. (2022). Cooperative self-scheduling routing protocol based IOT communication for improving life time duty cycled energy efficient protocol in SDN controlled embedded network. Measurement: Sensors, 24. https://doi.org/10.1016/j.measen.2022.100475
[12] Dowlatshahi, M.B., Kuchaki Rafsanjani, M. and Gupta, B.B. (2021). An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities. Applied Soft Computing, 108. https://doi.org/10.1016/j.asoc.2021.107473
[13] Elhoseny, M. and Hassanien, A.E. (2019). Extending homogeneous WSN lifetime in dynamic environments using the clustering model. Dynamic Wireless Sensor Networks. https://doi.org/ 10.1007/978-3-319-92807-4_4
[14] Fanian, F. and Kuchaki Rafsanjani, M. (2018). Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm. Applied Soft Computing, 71, 568–590. https://doi.org/10.1016/j.asoc.2018.07.012
[15] Fanian, F. and Kuchaki Rafsanjani, M. (2020). A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks. Applied Soft Computing, 89. https://doi. org/10.1016/j.asoc.2020.106115
[16] Fereira, R.J., Ranaweera, C., Lee, K. and Schneider, J.G. (2025). Energy efficient resource management for real-time IoT applications. Internet of Things, 30, 101515. https://doi.org/10. 1016/j.iot.2025.101515
[17] Fouladlou, M. and Khademzadeh, A. (2017). An energy efficient clustering algorithm for wireless sensor devices in Internet of Things. Artificial Intelligence and Robotics, 39–44. https://doi. org/10.1109/RIOS.2017.7956441
[18] Gajjar, S., Sarkar, M. and Dasgupta, K. (2016). FAMACROW: Fuzzy and colony optimization based combined MAC, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Applied Soft Computing, 43, 235–247. http://dx.doi.org/10.1016/j.asoc.2016.02.019
[19] Gulati, K., Boddu, R.S.K., Kapila, D., Bangare, S.L., Chandnani, N. and Saravanan, G. (2022). A review on wireless sensor network techniques in Internet of Things (IoT). Materials Today: Pro- ceedings, 51(1), 161–165. https://doi.org/10.1016/j.matpr.2021.05.067
[20] Hassan, A.A.H., Shah, W.M., Hussien Hassan Habeb, A., Othman, M. and Al Mhiqani, M.N. (2020). An improved energy-efficient clustering protocol to prolong the lifetime of the WSN- based IoT. IEEE Access, 8, 200500–200517. https://doi.org/10.1109/ACCESS.2020. 3035624
[21] Heinzelman, W.B., Chandrakasan, A.P. and Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communica- tions, 1(4), 660–670. https://doi.org/10.1109/TWC.2002.804190
[22] Klir, G.J. and Bo, Y. (1996). Fuzzy Logic and Fuzzy System: Selected Papers by Lotfi A. Zadeh. World Scientific.
[23] Kooshari, A., Fartash, M., Mihannezhad, P., Chahardoli, M., Akbari Torkestani, J. and Nazari, S. (2024). An optimization method in wireless sensor network routing and IoT with water strider algorithm and ant colony optimization algorithm. Evolutionary Intelligence, 17(3), 1527–1545. https://doi.org/10.1007/s12065-023-00847-x
[24] Kuila, P., Gupta, S.K. and Jana, P.K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56. https://doi.org/10.1016/j.swevo.2013.04.002
[25] Kuila, P. and Jana, P.K. (2017). Clustering and Routing Algorithms for Wireless Sensor Networks: Energy Efficiency Approaches. New York: Chapman and Hall/CRC.
[26] Kwon, J.H., Cha, M., Lee, S.-B. and Kim, E.J. (2017). Variable-categorized clustering algorithm using fuzzy logic for internet of things local networks. Multimedia Tools and Applications, 78, 2963–2982. https://doi.org/10.1007/s11042-017-5176-x
[27] Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors, 12, 11113–11153. https://doi.org/10.3390/s120811113
[28] Logambigai, R. and Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 22(3), 945–957. https://doi.org/10.1007/ s11276-015-1013-1
[29] Lounis, M., Bounceur, A., Euler, R. and Pottier, B. (2017). Estimation of energy consumption through parallel computing in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15, 1339–1351. https://doi.org/10.1007/s12652-017-0582-5
[30] Mahmoudi, Y., Zioui, N. and Belbachir, H. (2022). A new quantum-inspired clustering method for reducing energy consumption in IoT network. Internet of Things, 20. https://doi.org/10. 1016/j.iot.2022.100622
[31] Manikandan, S. and Chinnadurai, M. (2021). Effective energy adaptive and consumption in wireless sensor network using distributed source coding and sampling techniques. Wireless Personal Communications, 118(2), 1393–1404. https://doi.org/10.1007/s11277-021-08081-3
[32] Mazumdar, N. and Om, H. (2018). Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks. International Journal of Communication Systems, 31(12), 1–23. https://doi.org/10.1002/dac.3709
[33] Mittal, N., Singh, U. and Sohi, B.S. (2019). An energy-aware cluster-based stable protocol for wireless sensor networks. Neural Computing and Applications, 31, 7269–7286. https://doi. org/10.1007/s00521-018-3542-x
[34] Mirzaie, M. and Mazinani, S.M. (2017). Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network. Computer Communications, 111(1), 56–67. http:
//dx.doi.org/10.1016/j.comcom.2017.07.005
[35] Mohammadi, R., Akleylek, S. and Ghaffari, A. (2023). SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.1424
[36] Mozaffari, M., Mazinani, S.M. and Khazaei, A.A. (2025). An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks. Wireless Networks, 31, 109–125. https://doi.org/10.1007/s11276-024-03737-x
[37] Naji, H.R., Earl Wells, B. and Etzkorn, L. (2004). Creating an adaptive embedded system by applying multi-agent techniques to reconfigurable hardware. Future Generation Computer Systems, 20, 1055–1081. https://doi.org/10.1016/j.future.2004.02.002
[38] Nayak, P. and Vathasavai, B. (2017). Energy efficient clustering algorithm for multi-hop wireless sensor network using type-2 fuzzy logic. IEEE Sensors Journal, 17, 4492–4499. https://doi. org/10.1109/JSEN.2017.2711432
[39] Oudenhoven, J.F.M., Vullers, R.J.M. and Schaijk, R. (2012). A review of the present situation and future developments of micro‐batteries for wireless autonomous sensor systems. International Journal of Energy Research, 36(12), 1139–1150. https://doi.org/10.1002/er.2949
[40] Ramezanzadeh, F. and Shokrzadeh, H. (2024). Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm. E-Prime - Advances in Electrical Engineering,Electronics and Energy, 7. https://doi.org/10.1016/j.prime.2024.100424
[41] Ran, G., Zhang, H. and Gong, S. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information & Computational Science, 7(3), 767–775.
[42] Rajaravivarma, V., Yang, Y. and Yang, T. (2003). A overview of wireless sensor network and applications. In Proceedings of the Southeastern Symposium on System Theory (SSST), 17, 432–436. https://doi.org/10.11591/ijeecs.v17.i3
[43] Saeedi, A., Kuchaki Rafsanjani, M. and Yazdani, S. (2024). Energy efficient clustering in IOT-based wireless sensor networks using binary whale optimization algorithm and fuzzy in- ference system. The Journal of Supercomputing, 81(209). https://doi.org/10.1007/ s11227-024-06556-1
[44] Saleh, B. and Neghabi, A.A. (2023). Optimal routing-clustering aware of energy consumption in wireless sensor networks based on deep tree learning. Transactions on Machine Intelligence, 6(4), 236–247. http://dx.doi.org/10.47176/TMI.2023.236
[45] Sedighimanesh, M., Zandhessami, H., Alborzi, M. and Khayyatian, M. (2022). Reducing energy consumption in sensor-based internet of things networks based on multi-objective optimiza- tion algorithms. Journal of Information Systems and Telecommunication (JIST), 10(3), 180–190. https://doi.org/10.52547/jist.15639.10.39.180
[46] Shokouhifar, M. and Jalali, A. (2017). Optimized Sugeno fuzzy clustering algorithm for wireless sensor networks. Engineering Applications of Artificial Intelligence, 60, 16–25. https://doi. org/10.1016/j.engappai.2017.01.007
[47] Sugeno, M. (1977). Fuzzy measures and fuzzy integrals: a survey. In Gupta, M.M., Saridis, G.N. and Gaines, B.R. (Eds.), Fuzzy Automata and Decision Processes, pp. 89–102. North-Holland: New York.
[48] Sundaran, K., Ganapathy, V. and Sudhakara, P. (2017). Fuzzy logic based unequal clustering in wireless sensor network for minimizing energy consumption. In International Computing and Communications Technologies (ICCCT), pp. 304–309. https://doi.org/10.1109/ICCCT2. 2017.7972283
[49] Suryadevara, N.K. (2021). Energy and latency reductions at the fog gateway using a machine learning classifier. Sustainable Computing: Informatics and Systems, 31. https://doi.org/10. 1016/j.suscom.2021.100582
[50] Sureshand, B. and Prasad, S.C. (2023). An energy efficient secure routing scheme using LEACH protocol in WSNs for IoT networks. Measurement: Sensors, 30. https://doi.org/10.1016/ j.measen.2023.100883
[51] Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S. and Kannan, S.A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223. https://doi.org/10.1016/j.comnet.2019. 01.024
[52] Ud Din, I., Khan, K.H., Almogren, A. and Guizani, M. (2025). Harnessing nature-inspired algorithms for energy-efficient artificial intelligence of things. IEEE Internet of Things Journal, 12(9), 12433–12445. https://doi.org/10.1109/JIOT.2024.3520714
[53] Vazhuthi, P.P.I., Prasanth, A., Manikandan, S.P. and Devi Sowndarya, K.K. (2023). A hybrid ANFIS reptile optimization algorithm for energy-efficient inter-cluster routing in internet of things-enabled wireless sensor networks. Peer-to-Peer Networking and Applications, 16, 1049–1068. https:
//doi.org/10.1007/s12083-023-01458-0
[54] Xu, L., O’Hare, G.M.P. and Collier, R. (2017). A Smart and balanced Energy-Efficient Multi- hop clustering algorithm (Smart-BEEM) for MIMO IoT systems in future networks. Sensors, 17. https://doi.org/10.3390/s17071574
[55] Zadeh, L.A. and Berkele, C. (2002). Fuzzy Logic Toolbox, User Guide. MathWorks.
[56] Zahedi, Z.M., Akbari, R., Shokouhifar, M., Safaei, F. and Jalali, A. (2016). Swarm intelligence based fuzzy routing protocol for clustered wireless sensor network. Expert Systems with Applications, 55, 313–328. https://doi.org/10.1016/j.eswa.2016.02.016
[57] Zhang, H., Zhang, M., Qin, T., Wei, W., Fan, Y. and Yang, J. (2024). An energy consumption optimization strategy for wireless sensor networks via multi-objective algorithm. Journal of King Saud University–Computer and Information Sciences, 36(1). https://doi.org/10.1016/j. jksuci.2024.101919
