Proactive and data-centric Internet of Things-based fog computing architecture for effective policing in smart cities

Smart surveillance is crucial for improving citizen security and ensuring a sustainable environment for routine tasks, particularly within intelligent transportation systems (ITS). However, it can be costly and burden taxpayers. The lack of public interaction makes it difficult for police to arrest and conduct investigations. Additionally, incidents increase due to similar patterns, making smart surveillance essential for reporting and addressing these issues. Smart devices such as sensors or actuators installed on the roads and within vehicles are critical components of any smart surveillance and ITS framework. This integration enhances system agility and facilitates proactive rather than reactive responses. It empowers security agencies to plan more effectively and respond swiftly during emergencies. The incorporation of cloud computing capabilities transforms traditional surveillance and ITS operations. Employing the Internet of Things (IoT) with edge or cloud computing extensions, such as fog computing, modernizes the management of security gadgets for Field Forces. This study investigates a smart surveillance fog-enabled approach to reduce response times for aiding agencies within ITS. By optimizing individual journeys through an RFID-based passing system, incidents are reported promptly to the nearest field force, enhancing overall ITS efficiency. The proactive approach improves resource consumption (energy, CPU, and network usage) compared to traditional reactive methods. The fog-enabled experiments demonstrated a CPU efficiency of approximately 95.76%, significantly outperforming the Cloud-only deployment, achieving a maximum average efficiency of 92.12%. Experimental evaluations in a simulation environment demonstrate that the proposed method significantly outperforms conventional approaches, marking a substantial advancement in IoT-aided ITS.