IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management

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Aim:

To design and implement a distributed Internet of Things framework called IoT-guard an efficient resource constrained methodology to prevent and predict crime events in a smart home environment.

Existing System:

In existing system various cyber-physical systems widely adopt the use of intelligent video surveillance, for automatic and accurate identification of events and objects in a target scene. IVS enables video analytics to predict and interpret the activity of a scenario without human intervention. Meanwhile, with the development of artificial intelligence (AI) and machine learning (ML), surveillance applications and security procedures are being improved with enhanced functions and accuracy. Protective services and authorities often fail to respond to crime incidents efficiently. Therefore, in most cases, when an event occurs, authorities visit the location of the incident, retrieve the content manually from the camera, and then proceed to identify relevant footage either by watching the full length of the video or by processing it through specialized video analytics algorithms.

Proposed System:

We are going to implement IoT-guard, an event-driven edge-fog-integrated video surveillance framework, to perform real-time security management by aiding in crime prevention and predicting crime events at an SHE. The proposed IoT-guard approach provides a three layer architectural framework that orchestrates event-driven edge devices in an SHE and DL-implemented fog computing nodes to address increasing human security concerns. The system also provides an alert by sending the crime data instantly to the police or protective service, and thus, it ensures a quick response.