Designing a Big Data–Based Smart Command and Control Model for the Islamic Republic of Iran Army

Document Type : research Artichel

Authors

1 Security Faculty of the National Defense University of the General Staff of the Republic of Iran

2 Khatam al-Anbia Air Defense Academy

3 Shahid Sattari Aeronautical University

4 Deputy Minister of Information and Cyber ​​Defense of the Islamic Republic of Iran Army

Abstract

The smart command and control model is a modern military concept that leverages big data and advanced analytics to enhance decision-making and operational effectiveness. In this regard, machine learning algorithms can be employed to develop a big data–driven command and control system for the Islamic Republic of Iran Army. Such an approach involves applying reinforcement learning algorithms for intelligent decision-making, utilizing neural networks for pattern recognition and behavior prediction, and employing big data analytics methods to extract actionable insights from massive military datasets. For instance, predictive models can be used to anticipate military attacks, while reinforcement learning algorithms may optimize the allocation of military resources. The objective of this study is to design a big data–based smart command and control model for the Islamic Republic of Iran Army. The research adopts an applied-developmental orientation and a descriptive-analytical method. Data were collected through a library study, supplemented by questionnaires and interviews, in two stages: a qualitative phase (Delphi method) and a quantitative phase (survey). The statistical population consisted of 63 individuals selected through a census approach. The proposed model encompasses four dimensions, including three components across three layers (application, data, and infrastructure), and one data processing and analytics component within the data-service platform dimension resulting in a total of 10 components and 60 indicators. The model’s validity was confirmed through Cronbach’s alpha (greater than 0.676), composite reliability, and factor loading coefficients.

Keywords

Defence Studies
Volume 23, Issue 90
September 2025

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