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.
翻译:当处理物的互联网(IoT),特别是工业性IoT(IIoT)时,有两个明显的挑战在思想上突飞猛进。第一是大量数据流流流流到IoT设备,第二是这些系统必须运行的快速速度。以边缘/球状结构形式分布的计算是克服这两个挑战的一种流行技术。在本文中,我们提议ADDI(使用分布式AI进行非常规检测),可以很容易地从地理上覆盖到大量IoT来源。由于其分布性,它保证了IIOT的关键要求,如高速、稳健性、单点故障、通信管理低、隐私和可缩性。我们通过实验性证据显示通信成本最小化,并且业绩显著改善,同时保持当地层原始数据的隐私。ADADI提供了新随机样本的预测,平均成功率为98.4%,同时与将所有原始传感器数据从云中卸载的传统技术相比,通信量减少了一半。