Journal of Intelligent Strategic Management

Journal of Intelligent Strategic Management

Designing a secure model based on cloud computing for smart meter reading using the Internet of Things

Document Type : Original Article

Authors
Keyvan Aghaeibadr 1
hasan mehrmanesh 2
Arefeh Fadavi Ashghari 2
1 PhD Student, Department of Information Technology Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Department of Industrial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
bumara.3.2.11235564.312878790899841
Abstract
The aim of this research is to design a secure model based on cloud computing for smart meter reading as the Internet of Things. First, library studies are conducted and then a model is designed that receives data from smart meters and stores it in a cloud computing system, which encounters a volume of big data. In this model, the goal is to determine the network load in the first stage and then separate conventional data from unconventional data. This is the basis for security and prevention of theft from the network. Conventional data indicates theft from the network, while conventional data indicates no theft. First, load prediction is performed using machine learning algorithms and it has been shown that the random forest algorithm is 95% capable of predicting the network load based on 4 specified input variables. Then, the degree of conventionality or unconventionality of the predicted data was determined using the convolutional neural network algorithm, which has shown that the convolutional neural network algorithm is able to predict and classify conventional and unconventional data with the least error and distinguish them from each other. Therefore, this algorithm provides reliable results regarding theft from the energy network through smart meters.
Keywords
cloud computing
smart meter
Internet of Things

Subjects

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Journal of Intelligent Strategic Management
Volume 3, Issue 4 - Serial Number 8
Winter 2025
Pages 137-156