A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

作者：Verma, Shikhar^*; Kawamoto, Yuichi; Fadlullah, Zubair Md.; Nishiyama, Hiroki; Kato, Nei

来源：IEEE Communications Surveys and Tutorials, 2017, 19(3): 1457-1477.

DOI：10.1109/COMST.2017.2694469

• The Internet-of-Things (IoT)
• real-time analytics
• data center network
• hyper-convergence
• edge analytics network

With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the "real-time IoT analytics" in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.

• 出版日期2017