Efficient energy management in a smart city based on multi-agent systems over the Internet of Things platform

Document Type : Research Paper


1 Department of Computer, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Technical and Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran



The smart city model on multi-agent systems and the Internet of Things using a wireless sensor network is designed to improve the quality of life for citizens, increase resource efficiency, and reduce costs. This model enables the collection, analysis, and sharing of information by connecting and coordinating devices and systems within the smart city. In this model, intelligent agents act as sensors, and the smart gateway plays the role of a base station. The main goal of this model is to reduce energy consumption. To achieve this goal, intelligent agents are divided into clusters, with each cluster having a cluster head. The cluster head’s task is to collect and aggregate information from the intelligent agents within its cluster and send it to the smart gateway. In the proposed method, each intelligent agent selects a cluster in a distributed manner. An intelligent agent may choose another intelligent agent as its cluster head or select itself as a cluster head and directly send the data to the smart gateway. Each intelligent agent chooses the cluster head after calculating the importance level of neighboring intelligent agents. By using this model, cities can experience increased resource efficiency and cost reduction by leveraging innovative technologies. The proposed method has been implemented in different scenarios of smart cities, such as sparse and crowded smart cities with varying message sizes. In all simulations, the proposed method demonstrated good capabilities in optimizing energy consumption management.


[1] F. Alqahtani, Z. Al-Makhadmeh, A. Tolba, and O. Said, TBM: A trust-based monitoring security scheme to improve the service authentication in the Internet of Things communications, Comput. Commun. 150 (2020) 216–225.
[2] I. Butun, P. Osterberg, and H. Song, Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures, IEEE Commun. Surv. Tutor. 22 (2019), no. 1, 616–644.
[3] D. Christin, Wireless sensor networks and the Internet of Things: selected challenges, Proc. 8th GI/ITG KuVS Fachgesprach Drahtlose Sensornetze, 2009, 31–34.
[4] E. Ever, Performability analysis methods for clustered WSNs as enabling technology for IoT, Performability in Internet of Things, 2019, pp. 1–19.
[5] M. Ershadul Haque, M. Asikuzzaman, I. Ullah Khan, M. Sanwar Hossain, and S.B. Hussain Shah, Comparative study of IoT-based topology maintenance protocol in a wireless sensor network for structural health monitoring, Remote sensing. 12(15) (2020).
[6] M. Handy, M. Haase, and D. Timmermann, Low energy adaptive clustering hierarchy with deterministic cluster-head selection, 4th Int. Workshop Mobile Wireless Commun. Network, 2002.
[7] U. Karabiyik and K. Akkaya, Digital forensics for IoT and WSNS, Mission-Oriented Sensor Networks Syst. 2 (2019), 171–207.
[8] B.L. Kundaliya and S.K. Hadia, Routing algorithms for wireless sensor networks: Analysed and compared, Wireless Person. Commun. 110 (2020), 85–107.
[9] B. Kundaliya and S.K. Hadia, M-RPSS: A modified RPSS for path scheduling of mobile sink in wireless sensor network, Int. J. Commun. Syst. 33 (2020), no. 7, e4335.
[10] V. Lohan and R.P. Singh, Research challenges for internet of things: A review, Int. Conf. Comput. Commun. Technol. Smart Nation, 2017.
[11] A. Moradi, M. Ordouei, and S.M.R. Hashemi, Multi-period generation-transmission expansion planning with an allocation of phase shifter transformers, Int. J. Nonlinear Anal. Appl. (2023) 1–12. doi: 10.22075/ijnaa.2023.23005.4470
[12] M. Ordouei and I. Namdar, Web robot detection based on fuzzy system and PSO algorithm, Int. J. Comput. Sci. Network 7 (2019).
[13] 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) 45–50.
[14] M. Ordouei and M. Moeini, Identification of female infertility in people with thalassemia using neural network, Int. J. Mechatron. Electeric. Comput. Technol. 13 (2023) 5371–5374.
[15] M. Ordouei, A. Broumandnia, T. Banirostam and A. Gilani, Optimization of energy consumption in smart city using reinforcement learning algorithm, Int. J. Nonlinear Anal. Appl. (2023) 1–15, 10.22075/ijnaa.2022.29258.4102
[16] 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. 3 (2018), 208–214.
[17] M. Ordouei, A. Broumandnia, T. Banirostam and A. Gilani, Providing a novel distributed method for energy management in wireless sensor networks based on the node importance criteria, J. Namibian Stud.: History Politics Cult. 34 (2023), 5252–5265.
[18] M. Ordouei, A. Shams and M. Moeini, Artificial intelligence routing algorithms in inter-vehicle mobile networks, Perform. Internet Things 10 (2023), 8751–8757.
[19] P. Pico-Valencia, Towards the Internet of agents: An analysis of the Internet of Things from the intelligence and autonomy perspective, Ingen. Invest. 38 (2018), no. 1, 121–129.
[20] A.M. Rahmani, T. Nguyen Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, and P. Liljeberg, Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach, Future Gen. Comput. Syst. 78 (2018), 641–658.
[21] Sh. Rani, R. Maheswar, G.R. Kanagachidambaresan, and P. Jayarajan, Integration of WSN and IoT for Smart Cities, Springer, 2020.
[22] R. Roman and J. Lopez, Integrating wireless sensor networks and the internet: a security analysis, Internet Res. 19 (2009), no. 2, 246–259.
[23] A. Shahraki, A. Taherkordi, O. Haugen, and F. Eliassen A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms, IEEE Trans. Network Service Manag. 18 (2020), no. 2, 2242–2274.
[24] R. Sharma, S. Prakash, and P. Roy, Methodology, applications, and challenges of WSN-IoT, Int. Conf. Electric. Electron. Engin., 2020.
[25] C. Sobin, A survey on architecture, protocols and challenges in IoT, Wireless Person. Commun. 112 (2020), no. 3, 1383–1429.
[26] Y. Xu, Z. Yue, and L. Lv, Clustering routing algorithm and simulation of Internet of Things perception layer based on energy balance, IEEE Access 7 (2019), 145667–145676.

Articles in Press, Corrected Proof
Available Online from 23 December 2023
  • Receive Date: 19 July 2023
  • Accept Date: 07 December 2023