Edge-Aware Smart Sensor Architectures Integrating IoT and Machine Learning for Intelligent Public Safety Networks

Every day the need for Real-time Intelligence, Low-latency and Large-scale Data Processing capabilities for adaptive Public Safety Systems will become more important as the environment continues to change. In order to address this need, this research makes a proposal for an Edge-enhanced Smart Sensor System that combines IoT devices and Machine Learning Technologies to create intelligent safety systems. The overall architecture of the Edge-enhanced Smart Sensor System is a combination of IoT Sensors (Location-specific), Edge-based Data Preprocessing (Low-latency, Scalable, Efficient) and using a CNN-GRU Model for multimodal processing (text, audio and video). The coordination of Edge and Cloud systems will result in Low-latency, Scalable and Efficient Processing Solutions to provide an Adaptive Intelligent System supporting Public Safety. The system is evaluated both in terms of detection performance and latency (96.4% and 285 ms, respectively), which are better than cloud-based systems. It also has consistent performance with growing sensor loads and noise, showing its resilience.