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

Document Type : Research Paper

Authors

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

10.22075/ijnaa.2023.31301.4610

Abstract

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.

Keywords

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