Seminar Topics

Monitoring & Analysing Mobility in Smart Cities

The Smart City concept involves gathering large amounts of data from different sensor sources (Vehicle counters, public transport, cell tower data, etc), the challenge lies in fusing the different data to value. A theme of interest here is analysing, predicting how the inhabitants of a city move about, as this helps make better decisions to avoid traffic congestion, etc.

• RQ: How to manage the fact that sensors cannot observe 100% of mobility events in a city?
• RQ: How far ahead can predicitons about mobility be made, what influences this?
• RQ: How to determine the "weak link" data sources in sensor fusion?
• BuScope: Fusing Individual & Aggregated Mobility Behavior for "Live" Smart City Services
• [El-Tawab'20] A Framework for Transit Monitoring System Using IoT Technology: Two Case Studies
• RQ: Does context detection and monitoring conflict with Android frameworks energy-saving & security policies?
• [Chatterjee'20] Detecting Mobility Context over Smartphones using Typing and Smartphone Engagement Patterns
• SimRa: Using crowdsourcing to identify near miss hotspots in bicycle traffic

Topics of Chinmaya Dehury

email: chinmaya.dehury@ut.ee

C1: A short review on clustering algorithms for edge computing environment

Edge computing refers to bringing the computing capacity to the edge of the network. The edge devices are equipped with a limited computing capacity. When we talk about edge computing, we need to think about the characteristics of edge data, edge resources, edge devices and many more. On the other hand clustering strategies in IoT and wireless communications are applied mostly to reduce the energy consumption and further improve the network resource utilisation. In this topic, the student needs to go through the algorithms/strategies that are specifically designed for clustering the edge data or the devices or the users.

The student need to answer following:

• What are clustering algorithms present in computer science, e.g. K-Means, Affinity Propagation (AP), Mean-shift, Spectral clustering, etc. ?
• The general working principles of some (2-3) popular clustering algorithms?
• Comparison of clustering algorithms? (features, applicability, limitations, advantages, are they lightweight, scalability, reliability, adaptive and dynamic, etc,)

Some articles to start investigating:

• Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments, https://www.mdpi.com/1424-8220/21/16/5395
• Task Classification and Scheduling Based on K-Means Clustering for Edge Computing, https://doi.org/10.1007/s11277-020-07343-w
• A Comprehensive Survey of Clustering Algorithms, https://doi.org/10.1007/s40745-015-0040-1

C2: Evolution of Abstraction and its role in edge-cloud continuum

The evolution of abstraction in computer science is a fascinating journey that has played a major role in the advancement of computing paradigms. Abstraction involves simplifying complex systems by isolating relevant details while hiding unnecessary complexities. It has evolved over the years, enabling programmers and computer scientists to work at higher levels of understanding and productivity. The student will focus on following questions:

• General evolution of abstraction starting from Machine language -> Assembly language -> high-level prog. language-> …..-> declarative/domain specific language-> etc. (10%)
• What are the layers of abstraction in infrastructure management (45%)
• Layers of abstraction in an edge device (45%)

To get a basic understanding you may follow below research articles:

• The Evolution of Abstraction in Programming Languages https://apps.dtic.mil/sti/citations/ADA059394
• A good post to read: “History Of Increasing The Level Of Abstraction”, https://www.progressivegardening.com/weather-data/history-of-increasing-the-level-of-abstraction.html
• Abstraction in Computer Science Education: An Overview , https://files.eric.ed.gov/fulltext/EJ1329311.pdf

C3: A systematic survey of edge-cloud continuum simulator

DogOnt ontology aims at offering a uniform, extensible model for several IoT devices, e.g. temperature sensors, CO2 sensor, and many more. The ontology allows you to describe the device location, device capabilities, interface, technology-specific features, etc. OWLready2 is a Python library for managing the classes, subclasses, entities, instance, and properties of dogont ontology. This topic focuses on creating the data models that are aligned with the dogont ontology

• What are the simulators available in the market that target edge-cloud computing continuum, or any specific computing environment?
• Compare the features of those simulators.

Some of the references to start with:

• A scalable simulator for cloud, fog and edge computing platforms with mobility support, https://www.sciencedirect.com/science/article/pii/S0167739X23000511
• Edge Computing Simulation Platforms: A Technology Survey https://link.springer.com/chapter/10.1007/978-3-030-71906-7_2
• A good repository to follow: “awesome-edge-computing” https://github.com/qijianpeng/awesome-edge-computing
• IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments (https://arxiv.org/abs/2109.05636)
• EdgeCloudSim: An environment for performance evaluation of edge computing systems (https://onlinelibrary.wiley.com/doi/full/10.1002/ett.3493)
• Edge Computing Simulation Platforms: A Technology Survey (https://link.springer.com/chapter/10.1007/978-3-030-71906-7_2)

(Taken) C4: Generative AI for Cloud Infrastructure Automation

Generative AI refers to a subset of artificial intelligence techniques and models that are designed to generate new content that is typically similar to, or in some cases entirely different from, existing data. Generative AI models learn from a dataset and then generate new data samples that exhibit patterns and characteristics present in the training data. Generative AI can be applied in cloud infrastructure automation to enhance various aspects of cloud management and operations, including resource provisioning and scaling, cost optimization, security and anomaly detection, infrastructure configuration management, predictive maintenance. The research questions student needs to ans:

• What do you mean by cloud infrastructure automation?
• Can generative AI be applied in cloud infrastructure automation? If yes, how?

Some of the references to start with:

• Some webpost:
  • https://azure.microsoft.com/en-us/blog/scale-generative-ai-with-new-azure-ai-infrastructure-advancements-and-availability/
• Get acquainted with cloud infrastructure automation

(Taken) C5: Intelligence discoverability and observability in edge infrastructure

Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. - Wikipedia
Observability is the ability to measure the internal states of a system by examining its outputs. - Splunk
This topic would focus on the Discoverability and Observability aspects of edge computing infrastructure (including edge intelligence, edge knowledge, IoT data, edge services, edge devices, etc). Some of the precise questions, the student needs to focus are:

• Q: What is the difference between Discoverability and Observability in general?
• Q: What can be made discoverable and observable in edge computing?
• Q: How do you see these two terms in the context of Edge computing?

Some resources to start with:

• Discovery systems in ubiquitous computing (https://ieeexplore-ieee-org.ezproxy.utlib.ut.ee/document/1626210)
• Edge-to-Edge Resource Discovery using Metadata Replication (https://zenodo.org/record/5851025/files/27.pdf)

C6: X Discovery: A short survey

Discoverability mechanism lets an entity X on a network be discoverable. The entity X can be a service, a device, a network or knowledge. For instance, the service discovery protocol is used by a client device to find out about the services it can use on a server device. On the other hand, edge computing allows the IoT device generated data or client data to be processed by the nearest computing device present at the periphery of the network. This topic would focus on the study of discovery systems in edge computing, especially the study of device, service, and knowledge discovery at the edge. Some of the questions the student need to focus on, are:

• What do you mean by X discoverability?
• How the X discovery works at the edge, where X can be device, service, or knowledge?

Some of the references to start with:

• Device Discovery in D2D Communication: A Survey (https://ieeexplore.ieee.org/abstract/document/8835011)
• Service Discovery (https://www.dfki.de/~klusch/i2s/SD_essay_klusch2013.pdf)
• Towards Service Discovery and Invocation in Data-Centric Edge Networks (https://ieeexplore.ieee.org/abstract/document/8888081)
• Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective (https://ieeexplore.ieee.org/abstract/document/9208715)

More topics

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Your topic

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Topics of Shivananda R Poojara

Topic 1: Edge Impulse- An MLOps Platform for Tiny Machine Learning

Edge Impulse is a cloud-hosted machine learning operations platform explicitly designed to develop ML models tailored to embedded and resource-constrained devices. This platform offers a range of automated features to streamline the training, validation, and deployment processes on edge devices with minimal hassle.

The following points to be covered:

• Architecture of Edge Impulse and its eco-system.
• Sample applications using Edge-Impulse(for example-Predictive maintenance)
• How does Edge Impulse support reducing the model complexities to run the models in edge devices?

Related research articles to start with:

 Edge Impulse

Topic 2: Intelligence at the extreme edge using TinyML

The swift expansion of Information and Communication Technology (ICT) has hastened the widespread implementation of IoT applications across various sectors, including smart factories. In these contexts, there is a growing emphasis on processing data in close proximity to the source devices to enable faster decision-making. The concept revolves around utilizing nearby servers to execute Machine Learning algorithms. However, these algorithms demand significant memory and CPU resources for both training and execution, which presents a challenge when dealing with devices that have limited resources. Conversely, a collection of methods and tools is designed to address this challenge, facilitating the creation of memory and CPU-efficient ML models that can seamlessly integrate into diverse embedded system architectures. One noteworthy platform within this landscape is tinyML. Hence, the primary objective of this investigation is to explore the tinyML framework or toolkit, specifically tailored for the development of Machine Learning applications that prioritize edge computing.

• RQ1 : What is TinyML framework and its architecture?
• RQ2 : What is Reformable TinyML?
• RQ3 : Taxonomy of Reformable TinyML?

Related research articles to start with: Intelligence at the Extreme Edge: A Survey on Reformable TinyML TinyML foundation A review on TinyML: State-of-the-art and prospects

Topic 3: Fledge Fledge stands as an open-source framework and a collaborative community dedicated to addressing the industrial edge's needs, with a primary focus on critical operations, predictive maintenance, situational awareness, and safety. Folloiwng points to be covered in the seminar:

• Architecture of Fledge
• Samples use-cases and their implementation

Related research articles to start with: Fledge

Topic 4: Serverless Simulators The main goal of this topic is to list and describe the various FaaS simulators. The FaaS Simulators are one way of estimating the prior cost and performance metrics without running on real serverless cloud platforms, which helps the developers test and tune the workloads.

• RQ1 : Why FaaS simulators are necessary?
• RQ2 : List and explain the architecture of various FaaS Simulators available.

Related research articles to start with: SimFaaS: A Performance Simulator for Serverless Computing Platforms

Some of the other topics would be:

• Digital twin model for IoT applications
• COSCO: Edge and Cloud Computing Coupled Simulator
• Is edge computing reality or hype?
• ekuiper: Lightweight IoT data streaming analytics engine for edge computing

Topics of Pelle Jakovits

[Already Taken] Topic P.1. Traceable data sharing in IoT and Smart Cities

• RQ1 : How to trace the origin of IoT data to be able to prove when and where exactly data was produced?
• RQ2 : How to trace the origin of things (in IoT) and produce moving along supply chains using IoT technologies and data?

Related research articles to start with:

• Li, Chunpei, et al. "Efficient and traceable data sharing for the Internet of Things in smart cities." Computers and Electrical Engineering 103 (2022): 108389. https://www.sciencedirect.com/science/article/pii/S0045790622006061

Topic P.2: Sensor Data fusion in Internet of Things and Smart Cities

A large number of sensors have and are being deployed in IoT networks and Smart Cities. However, it may not be feasible to deploy a new sensor every time a new type of observation/data needs to be detected. It is sometimes more feasible to use existing sensors and data to compute or extrapolate new observations - by performing data fusion. The goal of this topic is to study and give an overview of Data Fusion, how it is being applied in these domains and provide an overview of challenges and research gaps related to them.

Related research articles to start with:

• Billy Pik Lik Lau, Sumudu Hasala Marakkalage, Yuren Zhou, Naveed Ul Hassan, Chau Yuen, Meng Zhang, U-Xuan Tan, A survey of data fusion in smart city applications, Information Fusion, Volume 52, 2019, Pages 357-374, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2019.05.004

Topic P.3: Estimating City scale energy consumption and balance

Smart Cities aim to take advantage of data collected in cities and predictive models to improve the lives of citizens and to make more data-driven decisions. Solar energy balance is an aim of such cities to cover as much electricity needs as possible with local energy production. There have been works that investigated the production side of Tartu City, but this topic is aimed at investigating how to model and estimate more granular energy consumption in the city (mainly in buildings) at large scale.

• RQ1 : What methods can be applied to estimating Smart City energy consumption at different granularities (Whole city, city areas, city blocks)
• RQ2 : What specific data needs to be collected (and with what granularity and preciseness) to estimate Smart City energy consumption

Related research articles to start with:

• Constantine E. Kontokosta and Christopher Tull. A data-driven predictive model of city-scale energy use in buildings. Applied Energy, 197:303–317, 2017.
• Alessio Mastrucci, Olivier Baume, Francesca Stazi, and Ulrich Leopold. Estimating energy savings for the residential building stock of an entire city: A gis-based statistical downscaling approach applied to rotterdam. Energy and Buildings, 75:358–367, 2014.

Topic P.4: Improving the Smart City Data models and metadata

Or propose your own ideas

Some examples:

• Comparison of State-of-the-Art (SOTA) open-source IoT platforms, focusing on platforms that provide features for both device integration and data storage. The theoretical part involves a survey into the most popular SOTA platforms. The practical part involves setting up and demonstrating a selected candidate platform.
• Designing digital twins for Smart City visualization and monitoring
• Synthetic data generators for large-scale testing of IoT and Smart City systems