ADDAI: Anomaly Detection using Distributed AI
This addresses the need for efficient and private anomaly detection in industrial IoT systems, though it appears incremental as it builds on existing distributed computing techniques.
The paper tackles the challenges of massive data and high-speed operation in industrial IoT by proposing ADDAI, a distributed AI system for anomaly detection that achieves a 98.4% average success rate in predictions and reduces communication overhead by half compared to traditional cloud offloading.
When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace at which these systems must operate. Distributed computing in the form of edge/cloud structure is a popular technique to overcome these two challenges. In this paper, we propose ADDAI (Anomaly Detection using Distributed AI) that can easily span out geographically to cover a large number of IoT sources. Due to its distributed nature, it guarantees critical IIoT requirements such as high speed, robustness against a single point of failure, low communication overhead, privacy, and scalability. Through empirical proof, we show the communication cost is minimized, and the performance improves significantly while maintaining the privacy of raw data at the local layer. ADDAI provides predictions for new random samples with an average success rate of 98.4% while reducing the communication overhead by half compared with the traditional technique of offloading all the raw sensor data to the cloud.