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Internet of Things and Deep Learning for Citizen Security: A Systematic Literature Review on Violence and Crime

Author

Listed:
  • Chrisbel Simisterra-Batallas

    (Engineering of Technology and Information, Pontificia Universidad Católica del Ecuador, Esmeraldas 080150, Ecuador)

  • Pablo Pico-Valencia

    (Software Engineering Department, University of Granada, 18071 Granada, Spain)

  • Jaime Sayago-Heredia

    (Engineering of Technology and Information, Pontificia Universidad Católica del Ecuador, Esmeraldas 080150, Ecuador)

  • Xavier Quiñónez-Ku

    (Engineering of Technology and Information, Pontificia Universidad Católica del Ecuador, Esmeraldas 080150, Ecuador)

Abstract

This study conducts a systematic literature review following the PRISMA framework and the guidelines of Kitchenham and Charters to analyze the application of Internet of Things (IoT) technologies and deep learning models in monitoring violent actions and criminal activities in smart cities. A total of 45 studies published between 2010 and 2024 were selected, revealing that most research, primarily from India and China, focuses on cybersecurity in IoT networks (76%), while fewer studies address the surveillance of physical violence and crime-related events (17%). Advanced neural network models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid approaches, have demonstrated high accuracy rates, averaging over 97.44%, in detecting suspicious behaviors. These models perform well in identifying anomalies in IoT security; however, they have primarily been tested in simulation environments (91% of analyzed studies), most of which incorporate real-world data. From a legal perspective, existing proposals mainly emphasize security and privacy. This study contributes to the development of smart cities by promoting IoT-based security methodologies that enhance surveillance and crime prevention in cities in developing countries.

Suggested Citation

  • Chrisbel Simisterra-Batallas & Pablo Pico-Valencia & Jaime Sayago-Heredia & Xavier Quiñónez-Ku, 2025. "Internet of Things and Deep Learning for Citizen Security: A Systematic Literature Review on Violence and Crime," Future Internet, MDPI, vol. 17(4), pages 1-30, April.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:4:p:159-:d:1627313
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    References listed on IDEAS

    as
    1. Panagiotis Stalidis & Theodoros Semertzidis & Petros Daras, 2021. "Examining Deep Learning Architectures for Crime Classification and Prediction," Forecasting, MDPI, vol. 3(4), pages 1-22, October.
    2. Mohammed Aljebreen & Fatma S. Alrayes & Sumayh S. Aljameel & Muhammad Kashif Saeed, 2023. "Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    3. Tanzila Saba & Amjad Rehman Khan & Tariq Sadad & Seng-phil Hong & Daqing Gong, 2022. "Securing the IoT System of Smart City against Cyber Threats Using Deep Learning," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-9, June.
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