Automated control design in a sensor and AI-based intelligence monitoring system for suspicious activity detection

Abstract

Background: In the modern digital landscape, intelligence monitoring systems integrating advanced sensor technology and artificial intelligence (AI) have become essential for enhancing public safety. These systems aim to not only observe but also recognize and respond to suspicious activities effectively and efficiently. Current literature highlights the transformative impact of IoT and AI in various sectors, offering significant improvements over traditional methods. Methods: This study explores the integration of sensor networks, AI-driven algorithms, and Internet of Things platforms. Data collection involves real-time inputs from devices such as cameras, PIR sensors, and microphones, analyzed through machine learning techniques to enhance detection precision. Findings: The systems demonstrate improved monitoring efficiency and have the capacity to operate autonomously, ensuring security across both public and private sectors. They offer long-term cost savings and overcome the limitations inherent in human-operated systems. Conclusion: These systems represent a significant advancement toward proactive and intelligent surveillance, enhancing public safety and security. Novelty/Originality of this article: The research underscores the novel integration of cutting-edge technologies in intelligence monitoring, establishing new benchmarks in adaptability and responsiveness, and setting the foundation for future advancements in cohesive and sustainable surveillance frameworks.