If you wish to propose your own topic in a related field, please feel free to do so and contact us!

Open theses topics for the 2020/2021 study year

List of Supervisors

Fog Computing for processing IoT Applications

(Mainak Adhikari,

1. Model-Based IoT Interoperability on Edge/Fog Computing In the Internet of Things (IoT)

There is a clear need for a high level of interoperability between independently developed systems, often from different applications. Here, we want to design an on-demand, and low-latency based strategy for the IoT in a Fog/Edge environment.

2. Deep Reinforcement Learning for Offloading IoT applications in Edge Computing

A new era for the development of RL mechanism has introduced, called Deep Reinforcement Learning (DRL) to train the offloading gateway devices with different QoS parameters which improves the performance and learning speed. In this work, we want to prepare an intelligent IoT gateway for finding an optimal computing node and also decide when to offload the application on Fog/Edge environment.

3. Quality Testing for validating functional and non-functional requirements in Serverless Environment

Quality testing tools is used to validate non-functional requirements such as business logic encoded in microservices and serverless FaaS and data pipelines. A challenge is to be able to automate the process of inference of representative workloads from given traces and historical data accounting for advanced properties of the data .

4. Auto-scaling and Resource Provisioning of Data Pipeline in Serverless Environment

The auto-scaling can measure the capability of the cloud servers and scaling out or scaling down the resources automatically based on the status of the requests. It addresses two research challenges: i) cost efficiency by allocating the required resources and ii) time efficiency by allocating the applications to the available resources with minimum deployment time.

Go to top

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

(Chinmaya Kumar Dehury,

1.  Monitoring and controlling smart home appliances using IoT devices

This mainly focuses on controlling the smart home appliances to save energy consumption and ultimately reduces the cost in the smart home environment. For this, a student needs to study the pattern of the power usage of each device that is equipped with the IoT device. Based on the pattern, the designed algorithm needs to predict whether to switch off or on the home appliances.

The student needs to use the existing machine learning tools to analyze the power usage pattern.

2.  Reinforcement learning in cloud resource distribution

Reinforcement learning is one of three ML paradigms. Here a software agent takes actions by understanding the environment and its experience. For example, finding a path from one location to other, solving a knight-prince problem, etc. There are several frameworks to address different kinds of problem. In this topic, we will study different RL frameworks and will follow the RL approach in order to distribute the cloud resource among different services/users.

  • Understanding the fundamental concept of Reinforcement Learning and Cloud resource distribution
  • Survey of different RL frameworks (such as OpenAI Gym, DeepMind Lab, Amazon SageMaker RL, Dopamine, etc.)
  • Apply the RL approach to distribute the cloud resources among services/users

3.  1-Shot integration of IoT, Fog, and Cloud

Single window system to configure and deploy data flow processors, execution stacks such as Function as Service(FaaS) at Fog and Cloud environment. The main objective of the project is to develop a toolset that can configure the fog environment with cloud connection along with necessary softwares required for data driven execution using various commands. 

This is more of a software package oriented thesis topic where the user needs to be involved in further development of an existing project.

4.  AI-powered cloud usage controlling system in smart home environment

In a smart home environment, we are using cloud computing for powerful processing and storage of big data that are generated from IoT devices. Further we are also using Fog to handle the data in a more real time manner.

This thesis topic mainly focuses on efficient utilization of cloud and fog. An AI model needs to be developed to decide when to use fog computing and when to use cloud computing.

5.  (Already Taken) Pipeline approach for handling data in hybrid cloud

The student will focus on development of the data pipelines using TOSCA language for Google, Amazon cloud. The data pipeline should also be compatible with serverless platforms. Some TOSCA-based data pipelines are developed for private cloud, such as University’s OpenStack cloud. Apache Nifi can be used as the underlined technology.

6.  Predicting Cloud service demands.

As we know, most of the frequently used apps such as Instagram, Twitter, Spotify, etc are deployed on cloud environment. Sometimes the usage of such applications is very high and sometimes the usage is very low. But can we predict how heavy an app will be used in the next few hours? In short, what would be the future demand for a cloud-based service? This is the question, we will answer in this topic.

  • Find out how the cloud resources are allocated to an app/service.
  • Gather the dataset related to the resource usage of different cloud-based applications
  • Apply AI tools to predict and verify the result using the dataset.

7.  Understanding Cloud usage data.

In this topic, we will look into the cloud server usage data, such as number of VMs deployed, percentage of server usage, resource utilization of VMs and physical servers etc. We will gather the data from different cloud service providers, such as Google, Delft University of technology, etc.

  • Gathering the related dataset from 4-5 cloud service providers.
  • Understand the data and their limitations.
  • Apply ML/Scientific tools to understand how the cloud servers are performing.
  • Analyze the data to acquire hidden information

Go to top

Large Scale Data Processing

(Pelle Jakovits)

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.

(Already taken) IoT data analytics for detecting anomalous devices and situations (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 goal is to design a solution for analyzing the IoT data streams for detecting outliers, potential anomalous situations and suspicious devices.

From SQL queries to Structured Streaming applications (B) (Pelle Jakovits)

Structured Streaming is a new stream processing abstraction built on top of the Apache Spark SQL engine. The goal of this topic is to study this stream data processing approach and compare its usability, fault tolerance and performance to more classical streaming approaches. The thesis should give an overview of its advantages and disadvantages, demonstrate how to adapt typical stream processing applications to it and investigate how easy it would be to take arbitrary Spark SQL, Dataframe or Hive SQL based applications and convert them into Streaming applications using Spark Structured Streaming.

Optimizing the performance of Apache Spark Streaming applications (M) (Pelle Jakovits)

The goal of this topic is to investigate what characteristics have a significant effect on the performance of Spark Streaming applications and provide guidelines and best practices on how to create and configure Streaming applications in Apache Spark to achieve optimal performance in different scenarios.

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.

Distributed Serverless Data Processing in IoT networks (M)  (Pelle Jakovits)

The goal of this topic is to study how efficiently Serverless technologies can be utilized to process data streams in multi layer (Fog computing)  IoT networks in a distributed manner and compare the efficiency, reliability and security of this approach in comparison to the typical Cloud centric data processing.

Service mesh based management of data streams in IoT networks (M) Pelle Jakovits)

Service mesh solutions (such as Istio) have a potential to greatly reduce the complexity of managing distributed IoT applications and their data. The goal of this thesis is to investigate open source service mesh solutions and to design a proof-of-concept data flow management solution for controlling the data flows inside distributed IoT applications.

Go to top

Applied IoT, System and Security topics

(Alo Peets,

  1. Create smart home, office, city demo use-cases that would be displayed in our new IoT lab in DELTA building in 2020. Exact ideas, hardware and outcome should be negotiated and agreed upon with supervisor. – BSc/MSc
  2. Bring Your own topic related to IoT solutions, applied security, IT systems management (devops), personalized applied medicine, real world big data analysis. – BSc/MSc

Supervised by Alo Peets,

Go to top

Serverless and Fog Computing

(Shivananda Poojara, Delta R3033)

1.  Autoscaling of serverless data pipelines in fog environments.og/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.

2. Adaptive serverless function deployments in mobile fog environments.

An IoT data processing consists of different data operations handled by individual serverless functions or groups of interdependent functions(normally called as function chaining). This function chaining can affect the latency and service time at a higher rate in mobile IoT environments. So, the aim of the topic is to develop adaptive algorithms(using machine learning techniques) to deploy function replicas near to data source or near to dependent function source.

3. Federation of FaaS platforms across edge/fog/cloud environments.

An aim of the given topic is to design a broker system to manage the federation of different FaaS platforms in the fog environment and also it should orchestrate with cloud FaaS providers. The broker system should be built in with an efficient faas-federation strategy to minimize the execution cost, service time and other QoS requirements of an IoT application. This can be achieved by efficient allocation of serverless functions to different faas providers in real time processing across edge environments. The faas providers may include local faas clusters such as OpenFaaS, Lean OpenWhisk or commercial faas providers like aws lambda or azure functions.

Go to top

Internet of Things topics

IoT smart city dashboard for emulated IoT networks (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 goal is to design a visual smart city dashboard which visualizes the state of emulated IoT devices and their data streams. The dashboard will be used to visualize the state of simulated exercises.

IoT data analytics for real-time visitor count estimation in the DELTA building (B, M) (Pelle Jakovits)

The Delta Building is a new building to house the Institute of Computer Science. Its construction is to be finished in 2020. There are plans for a number of different modern sensors to be placed in the building. The Computer Graphics and Virtual Reality lab’s students are working on a real-time visualization of the people and activities inside the building. For that purpose there is a desire to know how many people occupy each room (including the hallways) at any given moment. The goal of this topic is to study the state-of-the-art of sensor analytics or image processing (or fusion) and to create a usable approach for real-time visitor count estimation in lecture rooms.

Remote management of containers in IoT Devices (B) (Pelle Jakovits)

The goal of this topic is to investigate how to utilize cloud based IoT platforms (such as Cumulocity) to manage a large number of IoT computing devices, such as Raspberry Pi’s. Student should create software for integrating any computer running Docker with Cumulocity IoT platform. Such software should display information about the currently running containers, support deploying and configuring Docker containers, remote management of their life-cycle and executing arbitrary commands inside the deployed containers.

Automatic integration of IoT devices using MQTT based Smart Agents (M) (Pelle Jakovits)

The goal of this topic is to seamlessly integrate MQTT “speaking” devices to an IoT platform (Such as Cumulocity) by using an intermediate Agent or Middleware which takes care of authentication, data delivery and synchronization, IoT platform configuration and other tasks and issues related to device integration. Using an intermediate agent also has a potential for augmenting the data and services that are provided by IoT devices — for example by injecting additional information about the current location, state and service quality of such devices.

Systematic comparison and evaluation of Open Source Internet of Things platforms (B, M) (Chinmaya Dehury, Pelle Jakovits)

The goal of this topic is to compare existing open source IoT platforms to commercial platforms (e.g cloud services like Cumulocity, Amazon IoT, IBM IoT) focusing on projects that support large use cases (e.g not home automation but rather Smart City, devices deployed over large geographical locations). Main aspects to focus on are the scope of features (e.g device integration, integration with external services, data processing and analytics, extensibility), performance and stability. Main question to answer is: are open source IoT frameworks mature enough for production systems and are there feasible alternatives to cloud-managed subscription based IoT platforms.

Universal Home hub (B) (Pelle Jakovits)

The goal of this topic is to investigate the feasibility and cost of building universal home hubs in comparison to depending on commercial brand home hubs. Each different home automation brand (e.g Philips or Fibaro) comes with own hub, which makes integrating devices from different brands a complicated approach and may require deploying many different hubs in the same rooms, cluttering the space. Some sub topics to focus on would be: home automation possibilities on open platforms, cloud Home Robots be used as home automation hubs.

Wireless vs wired home automation (B) (Pelle Jakovits)

Most new off-the-shelf Home automation IoT devices use Wireless connections, while typical building automation systems utilize wired connections. The goal of this topic is to investigate what are the disadvantages and advantages of wireless home automation devices, investigate their inter-connectivity, protocols, security, etc. One of the main research questions to answer would be: how many devices can a standard size homes/rooms support before the wireless networks quality starts degrading and what parameters can improve or degrade the quality and performance of such networks.

Go to top