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Open theses topics for the 2021/2022 study year

List of Supervisors

Making #smartenvironment smarter with #fog – #cloud computing platform

A list of Updated Thesis Topics can be found in this Google Doc: https://docs.google.com/document/d/14SPgOTF6f8nw8bpxl-3RMbDxwELranK1_bYBK2jzoy8/edit?usp=sharing

Contact: chinmaya.dehury@ut.ee, Delta building r3040

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Serverless, IoT , Edge and Fog Computing

(Shivananda Poojara, poojara@ut.ee Delta R3033)

1. Predictive maintainance of SD Cards in IoT devices

The memory card faults or persistent failureness is major problem in IoT devices. There are several factors that implicitly effect the failureness of teh SD cars such as deployment of devcies in harsh envrionemnts, development of bad blocks and malware attacks, etc,. So an aim of theses is to setup IoT devices test bed using RPIs, try to collect failure data by emulating the scenarios and design solutions for prediction of the system using machine learning algorithms.

2.  Autoscaling of serverless data pipelines in fog environments fog/edge computing environments.

 In large scale IoT deployments, handling the fast movement of data is crucial. Most of the data movements are triggered by events with uncertainty. This fast inflow of events carrying data, needs to be handled efficiently to enact the QoS requirements.  This can be achieved using various data flow engines such as Apache Nifi and stateless serverless functions. An aim of the given topic is to design novel auto scaling techniques to handle uneven requests of  serverless functions to minimize the service time, latency and other QoS expectations. Scalability can be achieved within the cluster of fog nodes.

3. Usage based inusrance using IoT

Insurance policies historically have been based mostly on how much you drive, but with advanced telemetry and sensor data, it is possible to incorporate actual driving behaviors into insurance risk models. These behaviors include acceleration/deceleration, speed compared to speed limits, and types of driving, such as commuting on freeway compared to commuting on surface streets. Insurers base policies on observed driving behaviour, which means that safe drivers can be rewarded with lower insurance premiums. This can be achieved using IoT technology. So an aim of the theses is to look in to IoT devices and its ecosystem for usage based inusrance sector

4. COSCO: Container Orchestration Using Co-Simulation for Fog Computing Environments

The aim of this thesis is to explore COSCO(a fog computing tool) and understand the different container orchestration algorithms in this tool and propose solutions to address the challenges faced in data-oriented workflow-based deployments in edge fog applications. The COSCO is built with efficient machine learning algorithms for container placement with various QoS parameters such as energy, latency, etc.

5. Investigation of various managed serverless data pipelines

The goal of the topic is to investigate and benchmark functional and nonfunctional metrics for serverless data pipeline mechanisms provided by Google cloud, Azure Cloud, and AWS cloud services. 

6. Investigation of opensource serverless platforms

The aim of the thesis is to study the existing investigation of the opensource serverless platforms such as OpenFaaS, Nuclio, Knative, Fission, and Kubeless. The following would be research questions that need to address.

  • What are the architectural components used to design these systems?
  • How do throughput and latency behaviors on various types s work workloads?
  • How do auto-scaling works?

7. Study on edge anaytic frameworks

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Internet of Things topics

Knowledge graphs for cataloging and making sense of Smart City data (B, M) (Pelle Jakovits)

Nowadays it is quite common for new buildings to have hundreds or even thousands of sensors that generate data. The same can be said for cities, where data about different systems, devices, vehicles, and people are being sensed and collected. Often this data is stored in a technical manner that is the most convenient for the actual hardware devices and networks that are used, but which makes it difficult to understand for humans and complicated to reuse in other applications. The goal of this topic is to investigate how to build both human- and machine-understandable data models for cataloging the data that is being collected in Smart Buildings (E.g. Delta building) and Smart Cities (e.g. Tartu), with the aim to simplify the understanding and reusability of the data.

Synthetic IoT data generator for large scale IoT Device simulation (M) (Pelle Jakovits)

This topic is related to the Cyber defence simulation of Internet of Things and Mobile Networks in the Cyber Range project.
The student should evaluate extending open source tool for generating real-like IoT data based on existing captured data traces and “play it back” to simulate real data in an IoT network. The goal is to design and create a solution which processes existing data traces and can generate similar behaving data stream with high-volume and frequency. It should also support customizing the generated data stream, including volume and frequency, structure (like ratios between the different types of sub-streams), randomizing certain fields of records.

Reliability and performance of industrial IoT platforms (B, M) (Pelle Jakovits)

The goal of this topic is to analyse the reliability and performance of IoT platforms (e.g open-source IoT platforms, cloud services like Cumulocity, Amazon IoT, IBM IoT) focusing on solutions that support large use cases (e.g not home automation but rather Smart City, devices deployed over large geographical locations). The main aspects to focus on are the performance, stability and the scope of features (e.g device integration, integration with external services, data processing and analytics, extensibility). The main questions to answer are:

  • Which of the solutions in the market are the most suitable for supporting large Smart City use cases.
  • How easy is to integrate new IoT devices.
  • Are open source IoT frameworks mature enough for production systems and are they feasible alternatives to cloud-managed subscription-based IoT platforms.

Real-time Smart Building data visualization (B) (Pelle Jakovits)

The goal of this topic is to study approaches for creating so-called Digital Twins of large Smart buildings (e.g. Delta ) and design an approach for visualizing the state of the building based on the data collected from the building automation system and other sensors deployed there.

Real-time Smart City data visualization (B/M) (Pelle Jakovits)

The goal of this topic is to study approaches for creating so-called Digital Twins of whole cities and design an approach for visualizing the state of Tartu city based on the data collected from city sensors and services.

A practical approach for an open data portal for sharing smart city data in Tartu (B) (Pelle Jakovits)

The goal of this thesis is to study best practices for publishing open data; approaches for designing solutions for sharing historical and real-time data as open datasets; and to build a working prototype for sharing Smart City data of Tartu city. The solution should be able to handle data that is being updated continuously by sensors or other devices and systems deployed in the city and give the data owner tools and means to control how the data is published.

Evaluating the solar energy production potential of buildings in Smart Cities (B/M) (Pelle Jakovits)

The goal of this topic is to analyze data collected about the energy production of buildings in cities (e.g. Tartu); the estimated potential of maximum energy production; types, sizes, positions, roof inclination of buildings, etc. The results of the thesis could be an interactive dashboard providing an overview of differences between the energy potential of different city areas, building types, etc.; suggestions for changing how the buildings are currently being designed and built; pipelines for performing a similar analysis in other cities.

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Large Scale Data Processing

Stream data processing on resource constrained devices (B/M) (Pelle Jakovits)

With the ever increasing amount of data that needs to be collected from IoT data sources, it becomes more and more expensive to simply stream all the data to a cloud-side data processing platform. Depending on specific scenarios, it may be beneficial to (pre-)process the data as close to its source as possible. However, there are limitations on how powerful computing resources are available near the data sources. The goal of this thesis is to evaluate existing solutions for streaming data processing which allow performing part of the data processing nearer to the source, give an overview of their usability, advantages and disadvantages and analyse their effectiveness in comparison to more classical stream data processing frameworks such as Apache Spark or Storm.

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