Seminar Topics

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?
• 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

Infrastructure and System Design for Edge & IoT

IoT System Development Framework

Designing and implementing IoT systems can be complex, as it involves activities at the cloud-level (storage, analysis, processing tasks), client-level (applications and user-facing services) and device-level (hardware, sensor/actuator device software). Usually, the technologies for these 3 levels are significantly different with little overlap. Some frameworks aim to unify this picture and aim to give a single set of tools, programming languages for designing IoT systems at all the levels.

• RQ: What kind of use cases benefit from using a single unified language for the 3 IoT solution parts (device side, the cloud side, and the client side), and in what are possible drawbacks for other kinds of applications?
• TinyLink 2.0: integrating device, cloud, and client development for IoT applications

Applications for Mobile, IoT, Edge & Miscellanous

Computation Offloading for Vehicles

Modern vehicles are equipped with computing and communication modules, that enable connecting with each other and road-side infrastructure. This has lead to research in various ways how computational tasks can be distributed between nodes in a vehicular scenario. This topic tries to survey the field where Fog/Edge Computing and Vehicles meet, and identify applications which benefit more from using the Fog/Edge approach.

• RQ: What types of offloading are prevalent for Vehicular scenarios?
• RQ: What characterizes applications used in vehicular computational offloading?
• Computation Offloading for Vehicular Environments: A Survey

Identifying Business Process activities from IoT Sensor Data

IoT device data enable the opportunity of fine-grained monitoring, analysis of business process. IoT can also helps detect when some-sub activity of a process is started or ended (e.g. washing machine has finished wash cycle and now starts drying cycle). But in practice, reliably detecting and segmenting IoT data to corresponding process steps is not easy. This topic studies the literature to find what are the state of the art methods for discovering activities from raw IoT sensor data (supervised, unsupervised machine learning, visualization-based)

• RQ: How can sensor data be associated with process activity executions to potentially automate the detection of activities from sensor data?
• Method to Identify Process Activities by Visualizing Sensor Events

Smart Networking, Security for IoT

Backscatter networking for Internet of Things

In backscatter radio communication, a device transmits data to a receiver by modulating and reflecting a signal from a third, external signal source, as opposed to generating the signal itself and sending it directly to the receiver. This approach is useful mainly due to the decreased energy requirements as the transmitter does not have to generate a radio signal itself, only remodulate it. As a result, this technology is also interesting for IoT devices. This topic studies the existing prototypes &solutions of backscatter networking for IoT Backscatter radio communication exploits the reflectedor backscattered signals to transmit data, where thebackscattered signals can be the reflection of ambientradio frequency (RF) signals, the RF signals from thededicated carrier emitter

• RQ: What are the limitations and drawbacks of using back-scatter networking for IoT?
• Gateway over the air: towards pervasive internet connectivity for commodity IoT

(more backscatter papers in mobisys'20 conference)

Topics of Chinmaya Dehury

email: chinmaya.dehury@ut.ee

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 think about the characteristics of edge data, edge resource, 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 utilization. In this topic, student needs to go through the algorithms/strategies that specifically designed for cluestering the edge data or the devices or the users.

• 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

Intelligence discoverability and obeservability 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).

Mapping Dogont ontology with smart home data

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.

Some sources:

• DogOnt - Ontology Modeling for Intelligent Domotic Environments, DOI: 10.1007/978-3-540-88564-1_51, http://iot-ontologies.github.io/dogont/
• An ontology design pattern for IoT device tagging systems, https://ieeexplore.ieee.org/document/7356558

Modelling languages for edge computing

DogOnt ontology aims at offering a uniform, extensible model for several IoT devices, e.g. temperature sensors, CO2 sensor, and many more. Similarly, DTDL from Azure, allows you to create, modify and visualize the smart city or smart building data such as City objects, parking, environment, ports, streetlights, people, places etc. This topic would focus on looking at different modelling languages (similar to Dogont or DTDL) available for smart city.

Ref:

• https://github.com/Azure/opendigitaltwins-dtdl
• https://github.com/Azure/opendigitaltwins-smartcities
• https://docs.microsoft.com/en-us/azure/digital-twins/overview

A systematic survey on Edge computing simulator

Edge computing refers to bringing the computing capacity to the edge of the network. The edge devices are equipped with a limited computing capacity. A minimal version of the intelligence is deployed on the edge devices. Edge computing use cases are autonomous vehicles, smart grid, predictive maintenance, traffic management, smart homes, etc. There are several simulators for edge computing, such as EdgeCloudSim, iFogSim, etc. This topic will mainly focus on reviewing the simulating tools available in the market.

Some papers to follow:

• 1: IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments (https://arxiv.org/abs/2109.05636)
• 2: EdgeCloudSim: An environment for performance evaluation of edge computing systems (https://onlinelibrary.wiley.com/doi/full/10.1002/ett.3493)
• 3: Edge Computing Simulation Platforms: A Technology Survey (https://link.springer.com/chapter/10.1007/978-3-030-71906-7_2)

Your topic

You may come with your own topic.....

Topics of Pelle Jakovits

(Already taken, Ziya) Topic P.1: Practical Edge Mesh

Traditional approaches for the Internet of Things (IoT) data processing have focused on collecting data originating from different devices in private data centers or in clouds and processing it centrally. Full dependence on remote cloud services can lead to the lack of autonomy of applications in case of network degradation or failures. For example, Smart city applications that depend on logic being executed in remote clouds will not be resilient in accident and disaster scenarios when cloud services may not be accessible anymore. This has driven the implementation of Edge and Fog computing platforms, where some data processing tasks or other cloud services are moved closer to the data sources to reduce data sent to the cloud and to improve data privacy. The goal of this topic is to investigate a new type of Edge computing model called Edge Mesh and study what benefits it brings in comparison to cloud-centric data collection and processing, and typical Edge computing approaches.

• RQ1 : What is Edge Mesh, how does it differ from usual approaches to Edge computing and Fog computing?
• RQ2 : How is Edge Mesh different from Mist computing?
• RQ2 : What are its benefits in comparison to cloud-centric data collection and processing, and Edge computing.

Related research articles to start with:

• On the Road from Edge Computing to the Edge Mesh

(Already taken, Timothé) Topic P.2: Current state of Serverless Big Data Processing

Serverless has been considered a computing model for lightweight event-based applications and has historically been considered not suitable for Large scale data processing. The goal of this topic is to investigate whether this has recently changed with the addition of new approaches and solutions, or whether this claim still holds true.

• RQ1 : Is the serverless model suitable for big data processing?
• RQ2 : What characteristics of the serverless model are the main bottlenecks when large-scale data needs to be processed and queried.

Related research articles to start with:

1. Hellerstein, Joseph M., et al. "Serverless computing: One step forward, two steps back." arXiv preprint arXiv:1812.03651 (2018).
2. Aimer Bhat, Heeki Park, and Madhumonti Roy. 2021. Evaluating Serverless Architecture for Big Data Enterprise Applications. In 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies (BDCAT '21) (BDCAT '21). Association for Computing Machinery, New York, NY, USA, 1–8. DOI:https://doi.org/10.1145/3492324.3494169

Topic P.3: Open Source Fog computing platforms: Eclipse ioFog

Traditional approaches for Internet of Things data processing have focused on collecting data originating from different devices in private data centers or in clouds and processing it centrally. Full dependence on remote cloud services can lead to the lack of autonomy of applications in case of network degradation or failures. For example, Smart city applications that depend on logic being executed in remote clouds will not be resilient in accident and disaster scenarios when cloud services may not be accessible anymore. This has driven the implementation of Edge and Fog computing platforms, where some data processing tasks or other cloud services are moved closer to the data sources to reduce data sent to the cloud and to improve data privacy. The goal of this topic is to investigate existing open-source Edge and Fog computing frameworks and to give an overview of their architectures, involved challenges, and the current state-of-the-art in the field. The student should particularly focus on Eclipse ioFog as the reference framework.

• RQ1 : What are the main barriers in industry adopting the Fog and Edge computing models in comparison to using cloud-centric approach?
• RQ2 : What are the main aspects of Eclipse ioFog, what makes it different from previous apoproaches for designing Fog computing platforms?
• RQ3 : Are platofms like Eclipse ioFog already used in large scale industry?

Related research articles to start with:

• Toward a Reference Architecture Model for Industrial Edge Computing

Topic P.4: Cloud deployment modeling for data science and ML pipelines

Cloud deployment models have been used to describe how the cloud systems and services should be deployed together with software, infrastructure, network, and any other resources which are needed. TOSCA is one open standard language for such modelling, and is used in platforms like Cloudify. Cloud providers have their own custom models and frameworks for modeling and enacting such deployments. Furthermore, DevOps and Infrastructure as Code (IaS) methods have been applied to speed up such deployments, for example Ansible, Terraform, etc. The goal of this topic is to study the deployment modeling of ML pipelines and data science applications and pipelines in general.

• RQ1 : Are current Deployment modelling (e.g. TOSCA, AWS solutions) sufficient to model the deployment and dynamic execution of ML and data science pipelines
• RQ12 : What (if any) extensions are required to TOSCA to support modelling the deployment and dynamic execution of ML and data science pipelines

Related research articles to start with:

• Chinmaya Kumar Dehury, Pelle Jakovits, Satish Narayana Srirama, Giorgos Giotis, Gaurav Garg, TOSCAdata: Modeling data pipeline applications in TOSCA, Journal of Systems and Software, Volume 186, 2022, 111164, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2021.111164.
• Falkenthal, M., Breitenbücher, U., Képes, K., Leymann, F., Zimmermann, M., Christ, M., ... & Kempa-Liehr, A. W. (2016, November). Opentosca for the 4th industrial revolution: Automating the provisioning of analytics tools based on apache flink. In Proceedings of the 6th International Conference on the Internet of Things (pp. 179-180).

Topic P.5: 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

Or propose your own ideas

Some examples:

• Designing digital twins for Smart City visualization and monitoring
• Comparison and ranking of open source Internet of Things platforms.
• Synthetic data generators for large-scale testing of IoT and Smart City systems

Topics of Shivananda R Poojara

Topic 1: Predictive maintenance of IoT devices System monitoring is a common activity to ensure good quality of service in applications deployed in edge and fog environments. However due to stochastic workloads and the deployed environment of devices may be prone to outages, faults, and errors(e.g., CPU or HDD faults). Here, once fault generated propagates over the system leading to performance degradation of the application and system. Insensitive IoT applications such as Healthcare and Industrial applications, a small failure is not desirable otherwise can lead to catastrophic failure. So, it's important to monitor the devices by logging the data and predicting the failures in advance. Predictive maintenance is a technique of monitoring and predicting the failures in a system. Memory card faults or persistent failures are a major problem in IoT devices. There are several factors that implicitly affect the failures of the SD card such as the deployment of devices in harsh environments, development of bad blocks and malware attacks, etc. The following would be research questions that need to address:

• RQ1 : What are different techniques used in predictive maintenance?
• RQ2 : Predictive maintenance techniques for IoT devices
• RQ3 : Howd PD techniques applied for predicting SD Card failures?

Related research articles to start with:

 Distributed Continuous-Time Fault Estimation Control for Multiple Devices in IoT Networks

Topic 2: Intelligence at the extreme edge using TinyML

Rapid growth in ICT accelerated the large-scale deployment of IoT applications ubiquitously in several domains, including smart factories. Here, more often, the requirement is to process the data near to source device for faster decisions. The idea is to leverage the nearby servers to run Machine Learning algorithms. But, these algorithms require huge memory and CPU to train and run, but this is inevitable in resource-contained devices. On the other side, a set of techniques and tools helps to build memory and CPU-efficient ML models that can fit various embedded system architectures. one such platform is tinyML. So, the topic aims to investigate the tinyML framework or tool to build edge-centric ML applications.

• 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: Sensor fusion in autonomous vehicles Autonomous vehicles are becoming a pivotal technology that can revolutionize the future of transportation and mobility. The vehicles are integrated with several sets of sensors such as LiDAR, radar and cameras, including engine monitoring, geolocation sensors. So multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles.

• RQ1 : What is sensor fusion and its advantages?
• RQ2 : How is sensor fusion is used in autonomous driving?
• RQ3 : Why is sensor fusion necessary?

Related research articles to start with:

• Sensor fusion-Review

Topic 4: Impact of Language Runtime on the Performance and Cost of Serverless Functions Function as Service, also known as Serverless Computing, is a new computing paradigm for deploying applications. The billing model is based on per usage of functions with no worries in managing the servers. The functions are designed using several languages runtimes such as go, python, rust, c#, etc. However, cost and performance depend on the runtime you using, so the topic aims to study the impact of language run time on performance and cost of the applications.

• RQ1 : Does language affect the performance of the application?
• RQ2 : Does language run time increase or decrease the cost in the clouds?
• RQ3 : which run-time are cost and yield better performance?

Related research articles to start with:

• An Investigation of the Impact of Language Runtime on the Performance and Cost of Serverless Functions

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