论文标题
Addai：使用分布式AI的异常检测
ADDAI: Anomaly Detection using Distributed AI
论文作者
论文摘要
在处理物联网（IoT），尤其是工业物联网（IIOT）时，两个明显的挑战浮现在脑海。首先是往返于IoT设备的大量数据流，其次是这些系统必须运行的快速步伐。边缘/云结构形式的分布式计算是克服这两个挑战的流行技术。在本文中，我们提出了Addai（使用分布式AI的异常检测），该Addai可以很容易地在地理上跨越以覆盖大量的物联网源。由于其分布性质，它保证了关键的IIOT要求，例如高速，稳健性，对单点的失败，低沟通开销，隐私和可扩展性。通过经验证明，我们表明沟通成本被最小化，并且性能在保持本地层的原始数据隐私的同时大大提高。 Addai为新的随机样本提供了预测，其平均成功率为98.4％，同时与将所有原始传感器数据卸载到云的传统技术相比，将通信开销降低了一半。
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.
