## 5 Conclusions  
The findings revealed that only 17% of the studies addressed monitoring and detection of crime and physical violence in urban environments. These studies primarily proposed automated systems for tracking violent incidents in public spaces, detecting property crimes such as theft and vandalism, recognizing aggressive behaviors and weapons, identifying intruders, and detecting hazardous objects in restricted areas. In contrast, studies on cybercrime, which accounted for 76%, focused on identifying anomalous patterns and complex cyberattacks, including botnet attacks, DoS/DDoS attacks, MQTT server intrusions, and malware infections. These efforts aimed to mitigate cyberattacks targeting IoT networks implemented in smart cities, which are particularly vulnerable due to the transmission of high-value data exploitable by cybercriminals. Most of the analyzed proposals on crime and cybercrime detection in IoT networks were published in the last four years (2021-2024). The results indicate that India and China are at the forefront of crime monitoring in smart cities, driven by substantial government investments in AI-integrated IoT technologies. These countries have established the world's largest systems for detecting crime patterns in urban environments, exemplified by India's Smart City Mission and China's Skynet Project. Both initiatives leverage IoT cameras and sensors to train sophisticated neural networks using large-scale datasets. Their success has led to highly efficient crime detection systems tailored to high-density urban populations, a characteristic shared by both nations, further reinforcing this study's findings. In the field of deep learning and IoT for public security, a variety of neural networks have been employed, with CNNs and LSTMs being the most widely used due to their pattern detection capabilities, often combined into hybrid architectures. These systems excel in incident prevention, automated surveillance, and threat prediction, achieving over 97.44% accuracy (average) in simulation environments, which supports their adoption in China and India for real-world security applications. However, despite these advancements, challenges persist, including dependence on high-quality training data, limited model replicability, and algorithmic biases, which may result in discrimination and violations of fundamental rights such as privacy, security, and personal integrity. Since these technologies rely on continuous data collection and monitoring, their deployment must be regulated to balance security measures with the protection of civil liberties. Future research should further explore the integration of IoT and AI for crime monitoring, particularly in areas where automated surveillance remains underdeveloped, such as domestic violence, child abuse, sexual violence, and gender-based violence. Expanding research into these critical applications could have a profound social impact, enabling proactive interventions and enhanced protection for vulnerable populations. However, the deployment of such advanced systems in public spaces must adhere to stringent ethical guidelines and legal frameworks to safeguard individual rights and societal values. Problem statement, P.P.-V.; methodology, C.S.-B., X.Q.-K., and P.P.-V.; literature review, C.S.-B. ; information organization and formal analysis, C.S.-B. and P.P.-V.; writing--original draft preparation, C.S.-B. ; writing--review and editing, C.S.-B., P.P.-V., X.Q.-K., and J.S.-H.; translation, P.P.-V. All authors have read and agreed to the published version of the manuscript. This research was conducted as a collaboration between the Pontificia Universidad del Ecuador Sede Esmeraldas (PUCESE) and the University of Granada. PUCESE is the research sponsor. No new data were created or analyzed in this study. Data sharing is not applicable to this article.