AIoTtalk: A SIP-Based Service Platform for Heterogeneous Artificial Intelligence of Things Applications

作     者：Yang, Shun-Ren Lin, Yi-Chun Lin, Phone Fang, Yuguang 

作者机构：National Tsing Hua University Department of Computer Science The Institute of Communications Engineering Hsinchu30013 Taiwan Taiwan Semiconductor Manufacturing Corporation Design & Technology Platform Hsinchu300096 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong 

出 版 物：《IEEE Internet of Things Journal》 (IEEE Internet Things J.)

年 卷 期：2023年第10卷第16期

页      面：14167-14181页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work of Shun-Ren Yang and Phone Lin was supported in part by the National Science and Technology Council (NSTC), Taiwan, under Grant 110-2221-E-007-008, Grant 111-2221-E-007- 040-MY3, Grant 111-2221-E-002-126-MY3, and Grant 111-2622-8-002-028 and in part by the Ministry of Education Higher Education Sprout Project 

主　　题：Real time systems 

摘      要：Recently, several Internet of Things (IoT) service platforms have been proposed to facilitate IoT application deployment. These platforms typically utilize the lightweight MQTT or CoAP application protocol, optimized for massive IoT applications. Unfortunately, these protocols are not suitable for the emerging, more sophisticated Artificial Intelligence of Things (AIoT). Session initiation protocol (SIP), in contrast, is a signaling and controlling protocol for real-time multimedia sessions, and has been viewed as a better candidate to provide a full range support of different broadband, critical, and industrial AIoT applications. However, there exists no generic SIP-based AIoT service platform that supports creations and operations of heterogeneous AIoT applications with various quality of service. This article presents the first SIP-based AIoT service platform, AIoTtalk, that enables rapid development of scalar and multimedia AIoT applications. Moreover, we deploy an experimental testbed and two real SIP-based AIoT applications to demonstrate the applicability and the performance of our AIoTtalk under both the cloud and edge scenarios. The experimental results show that, together with accurate model predictions and edge-virtualization auto scaling, AIoTtalk guarantees low latency and high quality of experience for messaging and streaming-based AIoT applications. © 2014 IEEE.