Identifying Crime Prediction Strategies in Schools Using the Internet of Things (IOT)

Document Type : Original Article

Author

Assistant Professor, Department of Law, Naragh Branch, Islamic Azad University, Naragh, Iran.

Abstract

Purpose: The establishment and implementation of information and communication technology (ICT) in combating crime have made some societies more modern and innovative in crime detection. Accordingly, societies equipped with modern capabilities have greater opportunities. To enhance identification and intervention in this area, it is essential to propose a system that utilizes social tools from the Internet of Things to support the identification of criminals and the prediction of crime in the real world. One of the most critical areas for implementing the Internet of Things (IoT) to predict crimes is within schools. Students represent both the most vital and vulnerable components of the educational system and the national capital of any country. Therefore, it is essential to leverage modern technologies for crime prediction in schools. The purpose of this study is to investigate the role of the Internet of Things in predicting crimes in schools.
Method: This article is focused on its purpose and employs quantitative methods and surveys for implementation. The statistical population for this research consists of experts and criminologists in Tehran in 2023. The sample size was determined using the formula for an indefinite population. In a pilot study involving 30 questionnaires, the variance of the original sample was found to be 0.32. Ultimately, 157 individuals were selected as the sample using a simple random sampling method. The instrument used in this research is a researcher-developed questionnaire focused on the Internet of Things in schools, specifically regarding crime prediction. The data collected were analyzed using statistical inference tests and structural equation modeling.
Findings: The structural relationship analysis revealed that the direct effect of "Internet of Things capabilities" on crime prediction, with a coefficient of 0.81, indicates that these capabilities can significantly contribute to predicting crime in schools. In second place, the "Internet of Things applicability in identifying crime patterns, with a coefficient of 0.63, significantly influences crime prediction in schools. Finally, the "Internet of Things functional requirements in schools, with a coefficient of 0.52, also impacts crime prediction in educational settings. Overall, these modeling results align with the inferential findings from testing the hypotheses.
Conclusion: By using the Internet of Things in environmental design and maintenance, we can enhance local safety and environmental control, identify crime patterns, increase police awareness of criminal activity, conduct crime analysis, and utilize artificial intelligence algorithms. This approach enables us to accurately predict the occurrence of crimes in specific locations and take proactive measures to prevent them. Therefore, specialized systems and infrastructures for the development of the Internet of Things (IoT) in schools and other critical and sensitive locations should be utilized effectively, and appropriate platforms should be established to support their development. Therefore, planners, policymakers, and criminologists can leverage real data to significantly manage crime prevention processes in schools, thereby reducing the incidence of crime as the infrastructure develops.

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Alohali, B. (2017). Detection protocol of possible crime scenes using Internet of Things (IoT). In: Cybersecurity breaches and issues surrounding online threat protection (pp. 175-196). IGI Global.
Asgharinezhad, S., Rezghi Shirsavar, H. & Khanzadi, K. (2024). Investigating the Status of Internet of Things Development in Schools based on the Future Research. Sociology of Education, 10(1): 152-160. [in persian]
Barnett, J.A. (2020). Examining School Safety and Security: A Situational Crime Perspective. The University of Southern Mississippi, Honors Theses.
Cario, R. (2002). Early psychosocial intervention in the prevention of criminal behavior. Legal Research Quarterly, 5(35-36): 267-304. [in persian]
Chinaemerem, C.E. & Ao, A. (2023). An Intelligent Crime System Using Internet of Things [IoT] and Web Technological Tools for Instance Crime Reporting and Notification. IRE Journals, 9(6): 165-174.
Darabi, Sh. (2018). Crime prevention in the democratic model of criminal policy. Second edition. Mizan. [in persian]
Ebrahimi, M., Tadayon, M. & Sayad Haghighi, M. Trust Management in Internet of Things: Review, Analysis and Establishment of Evaluation Criteria. Signal and Data Processing, 18(2): 3-28. https://doi.org/10.52547/jsdp.18 [in persian]
Ebrahimi, Sh. (2022). Prevention of Recidivism through Artificial Intelligence; Requirements and Limitations. Criminal Law Doctrines, 19(23): 33-54. https://doi.org/10.30513/cld.2023.4345.1701 [in persian]
Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):
1645-1660.
Jangra, M. & Kalsi, S. (2019). Naïve Bayes approach for the crime prediction in Data Mining. International Journal of Computer Applications, 178(4): 33-37.
Kounadi, O., Ristea, A., Araujo, A. & Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime science, no. 9: 1-22.
Mahmoodi Janaki, F. & Ghorchi Beigi, M. (2009). Environmental design and crime prevention. Law Quarterly, 39(2): 345-367. [in persian]
Malasowe, B.O., Aghware, F.O., Okpor, M.D., Edim, E.B., Ako, R.E. & Ojugo, A.A. (2024). Techniques and Best Practices for Handling Cybersecurity Risks in Educational Technology Environment (EdTech). NIPES-Journal of Science and Technology Research, 6(2): 293-311.
Malasowe, B.O., Akazue, M.I., Okpako, E.A., Aghware, F.O., Ojie, D.V. & Ojugo, A.A. (2023). Adaptive Learner-CBT with Secured Fault-Tolerant and Resumption Capability for Nigerian Universities. International Journal of Advanced Computer Science and Applications, 14(8):
135-142. https://doi.org/10.14569/IJACSA.2023.0140816
Mazhar, M.S., Saleem, Y., Almogren, A., Arshad, J., Jaffery, M.H., Rehman, A.U. & Hamam, H. (2022). Forensic analysis on internet of things (IoT) device using machine-to-machine (M2M) framework. Electronics, 11(7): 1-23.
Mohammad Nasl, Gh. (2014). Crime prevention through environmental design. Mizan Legal Foundation. [in persian]
Nath, S.V. (2006). Crime pattern detection using data mining. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops (pp. 41-44). IEEE.
Piza, E.L., Welsh, B.C., Farrington, D.P. & Thomas, A.L. (2019). CCTV surveillance for crime prevention: A 40‐year systematic review with meta‐analysis. Criminology & public policy, 18(1): 135-159.
Qanbari, M. (2023). Identifying and tracking criminals through the Internet of Things. Karagah, 17(63): 1-23. https://doi.org/10.22034/det.2023.1273131.1387 [in persian]
Sadeghi, H. & Naser, M. (2020). Providing a legal framework for accountability in the operation of "Internet of Things" tools in the context of e-government. Iranian Journal of Public Policy, 6(3): 81-103. https://doi.org/10.22059/jppolicy.2021.79493[in persian]
Saeed, R.M. & Abdulmohsin, H.A. (2023). A study on predicting crime rates through machine learning and data mining using text. Journal of Intelligent Systems, 32(1).
Sardana, D., Marwaha, S. & Bhatnagar, R. (2021). Supervised and unsupervised machine learning methodologies for crime pattern analysis. International Journal of Artificial Intelligence and Applications (IJAIA), 12(1): 43-58.
Thongsatapornwatana, U. (2016). A survey of data mining techniques for analyzing crime patterns. In: 2016 Second Asian Conference on Defence Technology (ACDT) (pp. 123-128). IEEE.
Tundis, A. & Mühlhäuser, M. (2019). The role of Information and Communication Technology (ICT) in modern criminal organizations. In: Organized Crime and Terrorist Networks; Routledge: London, UK.
Veena, K., Meena, K., Teekaraman, Y., Kuppusamy, R. & Radhakrishnan, A. (2022). C SVM classification and KNN techniques for cyber crime detection. Wireless Communications and Mobile Computing, no. 8:1-9.
Watkins, N.J. (2015). Situational Crime Prevention in Schools: Implications for Victimization, Delinquency, and Avoidance Behaviors. Doctoral dissertation. College of Humanities and Social Sciences: George Mason.
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