| Title | Lightweight Fire Detection System Using Hybrid Edge-Cloud Computing |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ahmed, Homam, Jie, Zhu, Usman, Muhammad |
| Conference Name | 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET) |
| Keywords | 5G communication, 5G mobile communication, cloud computing, composability, Computational modeling, Computer architecture, Deep Learning, edge computing, edge detection, fire detection, Image edge detection, Internet of Things, Metrics, neural network accelerator, object detection, pubcrawl, Real-time Systems, resilience, Resiliency, Scalability, security |
| Abstract | The emergence of the 5G network has boosted the advancements in the field of the internet of things (IoT) and edge/cloud computing. We present a novel architecture to detect fire in indoor and outdoor environments, dubbed as EAC-FD, an abbreviation of edge and cloud-based fire detection. Compared with existing frameworks, ours is lightweight, secure, cost-effective, and reliable. It utilizes a hybrid edge and cloud computing framework with Intel neural compute stick 2 (NCS2) accelerator is for inference in real-time with Raspberry Pi 3B as an edge device. Our fire detection model runs on the edge device while also capable of cloud computing for more robust analysis making it a secure system. We compare different versions of SSD-MobileNet architectures with ours suitable for low-end devices. The fire detection model shows a good balance between computational cost frames per second (FPS) and accuracy. |
| DOI | 10.1109/CCET52649.2021.9544351 |
| Citation Key | ahmed_lightweight_2021 |
Lightweight Fire Detection System Using Hybrid Edge-Cloud Computing