Intelligent Fog-Edge Computing Framework for Emergency Detection and Response in Smart Homes

Abstract

The growing popularity of the use of Internet of Things (IoT) devices in smart homes has increased the demand in effective and prompt emergency detection mechanisms to keep people safe. Some of the problems associated with the traditional cloud-centric solutions include; high latency, limited bandwidth and privacy threats which prove to be fatal in running real time emergencies scenarios smoothly. This paper introduces a SMART fog-edge computing framework which aims to support real-time detection and response to emergencies at smart homes. Its architecture builds on a hierarchy in which sensor information passed to a local processor at the edge to classify events on a light AI model and calls upon a fog layer to undertake contextual examination and arrange response measures, such as sounding the alarms, throwing alerts, or tell smart actuators. The system is designed to make decisions and take action fast by using adaptive deep learning algorithms that will be most suited to edge devices and by using low-latency communication protocols. Simulation-based experimental findings prove that the performance is above 95 percent accurate in detecting the emergency and 60 percent lower response latency in comparison with the conventional cloud-based system with gains in the bandwidth and energy consumption. The presented solution has reflected the potential of the fog-edge computing approach as a scalable, privacy-preserving and real-time model of making the smart home environment safer and quicker to respond to any safety concerns.