ADDAI: Anomaly Detection using Distributed AI

TL;DR

ADDAI is a distributed AI-based anomaly detection system for IoT that offers high speed, scalability, privacy, and reduced communication overhead, achieving 98.4% accuracy in predictions.

Contribution

The paper introduces ADDAI, a novel distributed AI framework for IoT anomaly detection that improves performance and privacy while reducing communication costs.

Findings

Achieves 98.4% prediction success rate.

Reduces communication overhead by half.

Ensures robustness and scalability in IoT systems.

Abstract

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.