Energy Engineering and Management

Energy Engineering and Management

An Energy Consumption Management Framework in IoT and Smart Cities

Document Type : Original Article

Authors
soheila karbasi
Maryam Malek
Department of Computer Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran,
10.22052/eem.2026.257762.1149
Abstract
 In recent years, research in the field of smart cities and their related parameters has become very popular. One of the fundamental challenges in managing smart cities within the Internet of Things (IoT) context is the amount of energy consumption, which is supplied through electrical energy. This research presents a new framework for energy consumption management in smart cities while introducing the advantages and challenges of IoT in smart cities. For this purpose, the distance metric between communication nodes in a smart city is considered as a fundamental parameter for energy consumption management. Also, another parameter, referred to as the ‘remaining battery charge of each transmitting node’ is engaged. This allows for an appropriate decision to be made regarding the selection of a node for transmission, taking into account the distance and the remaining battery charge of a node. Therefore, the distance and the battery charge of the nodes are examined by a fuzzy logic system; by assigning a number between zero and one hundred to each node, the best candidate node is identified from among the nodes. In this way, each node with a higher assigned number is considered a more suitable candidate for data transmission. Throughout the network, some nodes are selected as agents that undertake the task of transmitting their own data and that of their neighboring nodes. The selection of these agent nodes is a fundamental challenge that has infinitely many solutions. For this purpose, the Grey Wolf Optimizer algorithm has been used to find optimal values for the agent nodes. The issue of selecting multiple agents in a smart city for the best agent transmission is a multi-agent problem. By defining various objectives such as bandwidth, throughput, and time, it transforms into a multi-objective problem. Finally, by comparing the simulation results of proposed method with two other methods, the findings showed that the proposed method offered better performance for the various defined scenarios. Thus, by considering distance and utilizing a multi-agent, multi-objective system, the remaining battery charge can provide acceptable results in the field of energy consumption in smart cities, especially in countries with energy challenges.
Keywords
smart city
internet of things
energy consumption management
intelligent selection of agent nodes
Subjects
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Energy Engineering and Management
Volume 15, Issue 2
Winter 2026
Pages 56-69