Dynamic and Scalable Deployment of Edge Internet-of-Things Analytics

• Main Authors: Tsai, Pei-Hsuan, 蔡霈萱
• Other Authors: Hsu, Cheng-Hsin
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/q8p2n6

碩士 === 國立清華大學 === 資訊工程學系所 === 106 === Modern Internet-of-Things (IoT) applications produce a large amount of data and require powerful analytics approaches, such as using Deep Learning to extract useful information. Existing IoT applications transmit the data to resource-rich data centers for analytics. However, it may congest networks, overload data centers, and increase security vulnerability. In my thesis, I implement a platform, which adopts the concept of Fog Computing, integrating resources from data centers (servers) to end devices (IoT devices). It has two features: (i)dynamic deployment and, (ii) edge analytics. I launch distributed analytics applications among the devices to pre-process the data, rather than sending everything to the data centers. I analyze the challenges to implement such a platform and carefully adopt popular open-source projects to overcome the challenges. I then conduct comprehensive experiments on the implemented platform. The results show: (i) the benefits/limitations of distributed analytics, (ii) the importance of decisions on distributing an application across multiple devices, and (iii) the overhead caused by different components in my platform.