AItalk: a tutorial to implement AI as IoT devices. Issue 3 (1st May 2019)

• Record Type: Journal Article
• Authors: Lin, Yun‐Wei; Lin, Yi‐Bing; Liu, Chun‐You
• Is Part Of: IET networks. Volume 8:Issue 3(2019)
• Page Start: 195
• Page End: 202
• Publication Date: 2019-05-01
• Subjects: Internet of Things -- sensors -- learning (artificial intelligence); AItalk -- IoT devices -- IoT data -- Artificial Intelligence techniques -- smart applications -- significant coding effort -- AI mechanism -- network applications -- machine‐learning mechanism -- cyber IoT device -- machine‐learning capability -- IoT technology -- AI solution -- supervised machine‐learning -- AI algorithms -- IoT communication -- AI prediction time -- AI application development -- IoT application programmes; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2018.5182
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : In one of the recent trends of Internet of Things (IoT), the IoT data are manipulated by Artificial Intelligence (AI) techniques for smart applications. By including AI into existing IoT application programs, significant coding effort is required. This paper proposes a solution called AItalk to resolve this issue. Unlike traditional AI‐based IoT applications that tightly integrate the AI mechanism within the network applications, the novel idea of AItalk is to treat the machine learning mechanism as a cyber IoT device. Our solution allows seamless inclusion of machine learning capability to the existing IoT applications without any programming effort. The advantage of this approach is that we can decompose a complex AI application into simplified distributed modules connected by using the IoT technology, and therefore the AI solution can be built more effectively. Also, in our approach, data can be easily processed in real time for an AI application. Supervised machine learning naturally fits the IoT applications, where the sensors provide the features to the AI algorithms, and the remote controllers serve as the labels. We show an example that the overhead of the IoT communication in AItalk is less than 30 ms and the AI prediction time is less than 2 ms.