Seminar Topics

(Supervisors marked in parenthesis)

Internet of Things

Topic supervisor: Chii Chang

IP = Intermediate Presentation

Amazon Alexa vs. Google Home vs. OpenHab -- Taken by Priit Paluoja

Comparing the three smart home platforms.

• [IP:1] Introduction and system architecture.
• [IP:2] Connectivity and comparability in networking, protocols, OS etc.
• [IP:3] Development complexity and community supports.

IoT Standards

Review and compare the standards described in the URL below: https://www.postscapes.com/internet-of-things-protocols/

W3C Web of Things (WoT) (Mohan Liyanage)

Today, various IoT solutions exist. Although almost all of them provide their RESTful API for the integration needs, it makes development complex and fragmented. The fragmentation issue motivates the leading industrial standard organisation W3C (who standardised World Wide Web) to start the Web of Things standardisation.

• [IP:1] WoT: Architecture https://w3c.github.io/wot-architecture/
• [IP:2] Thing Description https://w3c.github.io/wot-thing-description/
• [IP:3] Protocol Binding Templates https://w3c.github.io/wot-binding-templates/ and WoT Scripting API https://w3c.github.io/wot-scripting-api/

Web of Thing Model & EVRYTHNG Platform

Topic type: Research & Development. In this topic, the student needs to deliver the follows:

• [IP:1] WoT Model http://model.webofthings.io
• [IP:2] EVRYTHNG Platform https://evrythng.com
• [IP:3] Comparison https://webofthings.org and W3C WoT standard

Multi-access Edge Computing (MEC) APIs

In this topic, the student needs to deliver the follows:

• [IP:1] Introduction of MEC and use cases http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing
• [IP:2] Principles of MEC APIs http://www.etsi.org/deliver/etsi_gs/MEC/001_099/009/01.01.01_60/gs_MEC009v010101p.pdf
• [IP:3] MEC Management http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing

AI (Artificial Intelligence) & ML (Machine-Learning) in Digital Twin (Satish Srirama)

Digital Twin is a term to describe AI/ML-based IIoT solutions. In this topic, student has following tasks:

• [IP:1] Main idea, elements and use cases of Digital Twin.
• [IP:2] Data involved in Digital Twin and system architectures/models.
• [IP:3] Studying what are the AI & ML schemes used in Digital Twinning.

Blockchain-based Internet of Things besides Cryptocurrancy (Satish Srirama)

Blockchain is the core of the popular cryptocurrency - Bitcoin. Recent IoT approaches have been applying blockchain technology to support the decentralised IoT system architecture. Besides cryptocurrency and security aspects, what blockchain provides? The goal of this study is to investigate the research projects, commercial services and general use cases of Blockchain-based IoT applications/services.

• [IP:1] Introduction of Blockchain-based IoT systems.
• [IP:2] A comparison of Blockchain-based IoT system architectures and models from different domains.
• [IP:3] A comparison of open source software tools for implementing blockchain-based IoT. Demonstration.

Suggested demo tool: Flowchian https://github.com/flowchain

Mozilla IoT & Web Thing API (W3C member submitted specification) (Mohan Liyanage)

In this project, student will perform practical experiment with Mozilla WebThing API on Raspberry Pi. https://iot.mozilla.org/about/ https://iot.mozilla.org/wot/

• [IP:1] Introduction of Mozilla IoT.
• [IP:2] Running WoT Gateway on Raspberry Pi. Connecting sensors or actuators with WoT Gateway
• [IP:3] Connecting WoT Gateway with WoT Cloud

CISCO Fog Computing Server Testing

Experiments on CISCO Fog Computing Server with IR800 CISCO Industrial Integrated Router.

Telia interested projects -- Chii Chang and Satish Srirama (Responsible persons)

• 5G and IoT
• 5G and Smart Cities

Mobile Computing

Mobile IoT: Wi-Fi RTT (IEEE 802.11mc) and other in-door localization options

Supervisor: Jakob Mass

Localization of smartphones, robots, etc. is a vital feature for many applications in the mobile and IoT domain. While outdoor localization is commonly provided by GPS, no single technology has become widespread for in-door localization. Android 9 has introduced Wi-Fi RTT support in its APIs. In this topic, the student will study how Wi-FI RTT works, evaluate its capability (accuracy, equipment cost, ..). Additionally an overview of contemporary alternatives is expected.

A Mobile web server with the server-less architecture

Supervisor: Mohan Liyanage

Recently, the Function as a Service defined a very lightweight system that combined with server-less architecture. Instead of running a complete server on a mobile device, it might be possible to use the server-less architecture that is mainly focused on individual functions. In this work, you should merge the existing Mobile Web Server (Android based) with the server-less architecture.

Cross-platform Mobile development - Flutter, React Native, ..

Supervisor: Jakob Mass

Cross-platform mobile development allows to build and deploy applications to different platforms (Android, iOS), while developing a single codebase. One of the modern cross-platform framework is Flutter by Google. In this topic, Flutter-based software development is explored in contrast to the native Android (and iOS) development, highlighting the benefits and gaps in taking a cross-platform approach.

Note: previous Android or other Mobile development experience required!

Cloud Computing -- Satish Srirama (Responsible person)

1. Cloud resource management and scheduling
   Cloud infrastructure providers mostly rely on either static VM provisioning policies, which allocate a fixed set of physical resources to VMs using bin-packing algorithms, or dynamic policies, capable of handling load variations through live VM migrations and other load balancing techniques. These policies can either be reactive or proactive, and typically rely on knowledge of VM resource requirements, either user-supplied or estimated using monitoring data and forecasting. Study these techniques and implement your own methods on CloudSim.
   Z. A. Mann. Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms. ACM Computing. Surveys. 48, 1, Article 11 (August 2015).
   Calheiros, Rodrigo N., et al. "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and experience 41.1 (2011): 23-50.
2. Multi-Cloud and cloud interoperability solutions
   Studying TOSCA (Topology and Orchestration Specification for Cloud Applications) and its implementations such as OpenTosca and Cloudify
3. Data pipelines on the cloud
   AWS Data pipelines
   Apache nifi
4. Sustainability in clouds
   Dynamic allocation and reallocation of resources
   Models for green clouds
5. Blockchains in cloud computing
   Where the advances in blockchain will assist cloud computing?
6. Graph data processing with Apache Giraph
   Study Graph data processing with MapReduce and targeted frameworks such as Apache Giraph.
7. Serverless computing
   Study the emerging research trend in cloud computing domain. Identify and propose solutions for the challenges.

Real-time data processing -- Pelle Jakovits (Responsible person)

1. Orchestrating complex Data Pipelines processing real-time IoT data. Student should investigate existing Data pipeline orchestration frameworks (such as Apache Nifi) and resent literature on this topic, which concentrate on managing IoT data flows fusing data from a large number of geographically distributed data sources and which may require deploying data processing tasks at different distances from the data sources (Fog Computing scenario).
2. Real time vs micro-batching in streaming data processing: performance and guidelines Typically, stream processing frameworks buffer incoming data and process them in batches. But newer stream processing frameworks (such as Apache Storm) allow processing any incoming data objects in real time. Task of the student is to give an overview of the newest advances in Stream processing, compare the performance of real-time vs micro-batching engines for different use-cases. The student should also investigate which data or use case specific characteristics should be considered when choosing between the respective streaming data processing approaches. In addtition, student should also look into Structured Streaming, which is a new stream processing abstraction built ontop of the Spark SQL engine.
3. Stream data processing on resource constrained devices - With the increasing amount of data to be collected and processing from IoT data sources, it becomes more and more expensive to simply stream all data for cloud-side data processing. Depending on specific scenarios, it may be beneficial to pre-process the data as close to its source as possible. However, there are typically limitations on how much or how powerful computing resources are available in such cases. Student should study existing solutions which aim to solve such issues, give an overview of them and demonstrate example scenarios and solutions if possible.
4. Visualizing streaming data Student should perform a literature study and present in seminar what are the newest advances, best practices and available solutions for visualizing large scale streaming data. In the case of available open source visualization tools suitable in the context of this topic, student should demonstrate real-time data visualization on a an illustrative scenario.

Cloud Computing Frameworks -- Pelle Jakovits (Responsible person)

1. Docker performance aspects when running large number of small docker containers.
2. Docker based device integration in Cumulocity: issues and challenges
3. Real-time event processing in Cumulocity: limitations, issues and performance.
4. Viability of Serverless - Performance of FaaS cloud applications in comparison to micro-service and monolithic applications in real life scenarios
5. Service-mesh based security of cloud applications - using service-mesh and security policies to secure cloud applications composed of micro-services

Edge analytics, 5G IoT hardware and Smart Cities (Alo Peets)

Real life-usage of 5G IoT hardware LPWAN (NB-IoT, SigFox, LoRa etc.. ) and real-life testing results

• [IP:1] Description and selection of two (2) LPWAN hardware for further testing
• [IP:2] Technical description how to connect and use LPWAN devices (description of actually completed tasks)
• [IP:3] Presentation of results and analysis how to implement LPWAN devices in smart solutions

Review of edge-analytics use-cases in smart cities

• [IP:1] Extensive review of research papers and internet searches of smart cities around the world (present few most interesting cases)
• [IP:2] Deep technical analysis of smart city solutions and use-cases that use edge-analytics or could be impoved by using edge-analytics
• [IP:3] Review paper and comparison table (list) of smart city usecases that illustrate how edge-analytics could(have) improve(d) smart-city solutions