Real-Time IoT Analytics at Edge at Aarhus University on FindAPhD.com

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Assoc Prof Qi Zhang, Assoc Prof Panagiotis Karras No more applications being accepted Funded PhD Project (Students Worldwide)

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Aarhus Denmark Communications Engineering Data Science Internet Of Things Machine Learning

Department of Electrical and Computer Engineering, Aarhus University

About the Project

Online application details please visit Aarhus University webpage: https://phd.tech.au.dk/for-applicants/apply-here/saeropslag/re-advertisement-real-time-iot-analytics-at-edge/

The proliferation of the Internet of Things (IoT) and the ever-increasing massive IoT data provide unprecedented opportunities for innovations. How to extract value from the massive IoT data has gained significant interest among researchers and industry. Conventionally, historical analytics is often used to obtain insights from the mining of historical data for diagnostic and descriptive purposes. On the other hand, real-time IoT analytics promises to realize proactive and predictive analytics by analyzing IoT data as soon as it enters the system in a predefined timeframe; that is becoming a new trend in IoT data analytics and has applications in diverse verticals, such as smart home, industrial IoT, smart grid, E-health, smart transportation and many others. Real-time IoT analytics at the network edge would significantly reduce the analytics response time and save the bandwidth to forward all the data to the cloud. However, the analytics capability of edge computing is not as powerful as that of cloud computing. Therefore, the question is not how to perform analytics on massive IoT data, but rather how to perform analytics on the right data.

In this project, we will develop an edge analytics framework for real-time IoT data analytics to address the limitations of existing data-center-based analytics:

We will develop tailor-made techniques for real-time IoT stream data analytics at Edge, leveraging our previous expertise in data engineering.
We will study the relationship between sensor data representation, data storage architecture, and data analytics, to understand their impact on latency, accuracy, scalability, and fault tolerance.
We will optimize the sensor data representation and compression not only for transmission but also for facilitating and accelerating data analytics.
We will develop network methodologies to facilitate edge and cloud collaboration for real-time data analytics.