[1] M. Alazab, K. Lakshmanna, Q.V. Pham and P.K. Reddy Maddikunta, Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities, Sustain. Energy Technol. Assess. 43 (2021), 100973.
[2] V. Albino and U. Berardi, Dangelico RM (2015) Smart cities: definitions, dimensions, performance, and initiatives, J. Urban Technol. 22 (2015), 1–19.
[3] G. Amelie, M. Serrano and Gh.A. Atemezing, Semantic web methodologies, best practices and ontology engineering applied to Internet of Things, IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015, pp. 412–417.
[4] F. Aymen and C. Mahmoudi, A novel energy optimization approach for electrical vehicles in a smart city, Energies. 12(5) (2019).
[5] A. Azizivahed, A. Arefi, S. Ghavidel, M. Shafie-khah, L. Li, J. Zhang, and J.P.S. Catalão, Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage, IEEE Trans. Sustain. Energy 11 (2020), no. 2, 662–673.
[6] T. Banirostam and M.N. Fesharaki, A new approach for biological complex adaptive system modeling and simulation, Life Sci. J. 9 (2012), no. 3, 2257–2263.
[7] T. Banirostam and M.N. Fesharaki, Effective parameters in convergence of autonomous distributed systems using with immune system approach, 10th Int. Symp. Autonomous Decentr. Syst. (ISADS-IEEE), 2011, pp. 204–208.
[8] C.F. Calvillo, A. Sanchez-Miralles and J. Villar, Energy management and planning in smart cities, Renew. Sustain. Energy Rev. 55 (2016), 273–280.
[9] H. Carlos, E. Biele, Ch. Martini, M. Salvio and C. Toro, Impact of energy monitoring and management systems on the implementation and planning of energy performance improved actions: An empirical analysis based on energy audits in Italy, Energies 14 (2021), no. 16, 4723.
[10] P. Chithaluru, F. Al-Turjman, M. Kumar and T. Stephan, I-AREOR: An energy-balanced clustering protocol for implementing green IoT in smart cities, Sustain. Cities Soc. 61 (2020), 102254.
[11] G. Gianfranco, M. Lupia, G. Cario, F. Tedesco, F. Cicchello Gaccio, F. Lo Scudo and A. Casavola, Advanced adaptive street lighting systems for smart cities, Smart Cities 3 (2020), no. 4, 1495–1512.
[12] M. Glavic, Agents and multi-agent systems: A short introduction for power engineers, University of Liege -Electrical engineering and computer science department, 2006.
[13] Y. Guo, Q. Wu, H. Gao and F. Shen, Distributed voltage regulation of smart distribution networks: Consensus based information synchronization and distributed model predictive control scheme, Int. J. Electric. Power Energy Syst. 111 (2019), 58–65.
[14] S.M.R. Hashemi, H. Hassanpour, E. Kozegar and T. Tan, Cystoscopy image classification using deep convolutional neural networks, Int. J. Nonlinear Anal. Appl. 10 (2019), no. 1, 193–215.
[15] Y. Hayashi, Y. Fujimoto, H. Ishii, Y. Takenobu, H. Kikusato and Sh. Yoshiza, Versatile modeling platform for cooperative energy management systems in smart cities, Proc. IEEE 106 (2018), no. 4, 594–612.
[16] A. Kari and S. Sierla, An overview of machine learning applications for smart buildings, Sustain. Cities Soc. 76 (2022), 103445.
[17] M.I. Khalil, N.Z. Jhanjhi, M. Humayun, S. Sivanesan, M. Masud and M.S. Hossain, Hybrid smart grid with sustainable energy efficient resources for smart cities, Sustain. Energy Technol. Assess. 46 (2021), 101211.
[18] Y. Liu, C. Yang, L. Jiang, S. Xie and Y. Zhang, Intelligent edge computing for IoT-based energy management in smart cities, IEEE Networks 33 (2019), no. 2, 111–117.
[19] Z. Magubane, P. Tarwireyi and M.O. Adigun, Evaluating the energy efficiency of IoT routing protocols, Proc. 2019 Int. Multidiscip. Inf. Technol. Engin. Conf.(IMITEC), Vanderbijlpark, South Africa, 21–22 November 2019; IEEE: Piscataway, NJ, USA, 2019, pp. 1–7.
[20] A. Mathiesen, B. Vad, H. Lund, D. Connolly, H. Wenzel, P. Alberg Qstergaard, B. Moller and S. Nielsen, I. Ridjan, P. Karnoe, K. Sperling and F.K. Hvelplund, Smart Energy Systems for coherent 100% renewable energy and transport solutions, Appl. Energy 145 (2015), 139–154.
[21] S. McClellan, J.A. Jimenez and G. Koutitas, Smart Cities: Applications, Technologies, Standards, and Driving Factors, Springer International Publishing, 2017.
[22] E. Mlecnik, J. Parker, Z. Ma, C. Corchero, A. Knotzer and R. Pernetti, Policy challenges for the development of energy flexibility services, Energy Policy 137 (2020), 111147.
[23] P. Neamatollahi, M. Naghibzadeh and S. Abrishami, Distributed clustering-task scheduling for wireless sensor networks using dynamic hyper round policy, IEEE Trans. Mob. Comput. 17 (2017), 334–347.
[24] M. Ordouei and T. BaniRostam, Integrating data mining and knowledge management to improve customer relationship management in banking industry (Case study of Caspian credit institution), Int. J. Comput. Sci. Network 7 (2018), no. 3, 208–214.
[25] M. Ordouei and T. Banirostam, Diagnosis of liver fibrosis using RBF neural network and artificial bee colony algorithm, Int. J. Adv. Res. Comput. Commun. Engin. 11 (2022), no. 12, 45–50.
[26] E. Petritoli, F. Leccese, S. Pizzuti and F. Pieroni, Smart lighting as basic building block of smart city: An energy performance comparative case study, Measurement 136 (2019), 466–477.
[27] P. Pirozmand, A. Javadpour, H. Nazarian, P. Pinto, S.S. Mirkamali and F. Jafari, GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure, J. Supercomput. 78 (2022), no. 15, 17423–17449.
[28] K. Pourjavan, Explanation of smart city and smart transportation solutions, Karafan 16 (2019), no. 1, 15–35.
[29] N.P. Rana, S. Luthra, S.K. Mangla, R. Islam, S. Roderick and Y.K. Dwivedi, Barriers to the development of smart cities in Indian context, Inf. Syst. Front. 21 (2019), 503–525.
[30] S. Rasaneh and T. Banirostam, A new structure and routing algorithm for optimizing energy consumption in wireless sensor network for disaster management, 4th Int. Conf. Intell. Syst. Model. Simul., 2013, pp. 481–485.
[31] M. Stonebraker, U. C, etintemel and S. Zdonik, The 8 requirements of real-time stream processing, ACM Sigmod Record. 34 (2005), no. 4, 42–47.
[32] S. Tanwar, A. Popat, P. Bhattacharya, R. Gupta and N. Kumar, A taxonomy of energy optimization techniques for smart cities: Architecture and future directions, Expert Syst. 39 (2022).
[33] K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy and A. Kannan, Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT, Comput. Netw. 151 (2019), 211–223.
[34] T. Yigitcanlar, M. Kamruzzaman, L. Buys, G. Ioppolo, J. Sabatini-Marques, E. Moreira da Costa and J. Joseph Yun, Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework, Cities 81 (2018), 145–160.
[35] M. Zekic-Susac, S. Mitrovic, and A. Has, Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities, Int. J. Inf. Manag. 58 (2021), 102074.
[36] X. Zhang, G. Manogaran and B. Muthu, IoT enabled integrated system for green energy into smart cities, Sustain. Energy Technol. Assess. 46 (2021), 101208.
[37] S. Zhexuan, A. Cardenas and R. Masuoka, Semantic middleware for the Internet of things, Internet Things (IoT), IEEE, 2010, pp. 1–8.
[38] X. Zhu, J. Wang, N. Lu, N. Samaan, R. Huang and X. Ke, A hierarchical VLSM-based demand response strategy for coordinative voltage control between transmission and distribution systems, IEEE Transactions on Smart Grid. 10 (2019), no. 5, 4838–4847.