Work

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

Year: 2025

Type: review

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.

Source: Future Internet

DOI: 10.3390/fi17040159

Authors: Chrisbel Simisterra-Batallas, Pablo Pico-Valencia, Jaime Sayago-Heredia, Xavier Quiñónez-Ku

Institutions: Pontificia Universidad Católica del Ecuador, Universidad de Granada

Language: English

Topic: Internet of Things and AI

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Peace, justice, and strong institutions, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,930