Seminar Topics

TOPICS are being updated

Full list of topics will be announced during the first seminar.

Topics of Jakob Mass

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)

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

Mobile Computing

Energy Consumption on Android

Energy consumption analysis is a fundamental issue for smartphones, as they are batter-powered devices. Proper energy consumption measurement helps create energy-efficient software. There are mainly 2 approaches to measuring energy consumption: 1) direct measurement , where the electricity consumption is measured using a power meter or sensor and 2) indirect measurement, where the energy consumption is estimated in software based on the smartphone state, which apps are running, what are the CPU-s doing. This topic explores the different approaches, their usefulness and limitations.

• RQ: Which challenges limit indirect measurement methods compared to direct measurement methods?
• RQ: Do the best indirect methods rely on large historic datasets?
• Energy Consumption Measurement Frameworks for Android OS: A Systematic Literature Review
• E-Android: A New Energy Profiling Tool for Smartphones

Mobiles in Edge Computing: Code Offloading and Applications

A requirement for systems which enable offloading of mobile device code/tasks to other (non-mobile) devices such as fog nodes is an execution environment that matches the original runtime (e.g. Android runtime). A typical solution is to use virtual machines or containers, but in case of mobile such as Android or iOS, these approaches are not always feasible. Another challenge is deciding what to offload, when and to which machine. This topic explores the possible approaches and system designs of the runtime/environment for code offloading in Android.

• RQ: What limitations are imposed on offloaded code and why? I.e. what kind of code can/can't be offloaded.
• Android Unikernel: Gearing mobile code offloading towards edge computing
• RQ: How to model & predict execution time of offloaded tasks?
• RQ: What is the impact of the phones network (cellular, Wi-Fi) on task offloading?
• ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing

(already taken) Mobile-based Femtocloud

The idea of a femtocloud is use a group of mobile devices and form a cluster out of them that can provide cloud-like services to other devices. This topic investigates the feasibility of such ideas in Android, challenges and limitations of such systems.

• RQ: What programming models are employed for developing apps for femtoclouds?
• Workload management for dynamic mobile device clusters in edge femtoclouds
• Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge
  • Article is heavily oriented towards formal model

Distributed Mobile Application

In a distributed mobile application, different parts of a single application are run on several mobile devices, the parts communicate and interact, forming a whole experience. This topic studies the design and implementation of such a system on Android and what types of applications can be run in this way.

• RQ: How to manage discovery and networking with peers in a distributed Android framework?
• RQ: What are characteristics make an application suitable for being built as a distributed collaborative android application?
• Mobile collaborative computing on the fly

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

Smart Home control

Recently, voice-based interfaces have become popular for smart homes, in addition to GUI-based interfaces. Meanwhile, context-based functionality is a key aspect of smart home systems, allowing the system to make decisions based on the situation of the home (what are the sensor values? what time is it? which persons are home?) This topic looks at how the voice-based information can be used for determining who is present in the home and to provide access control based on that.

• RQ: How can the different kinds of contextual factors be used and managed for access control and automation?
• Context aware access control for home voice assistant in multi-occupant homes

Smart Cameras System

Modern network-connected cameras increasingly employ additional functionality besides just capturing images/video and networking / storing it. Face recognition, event detection are examples of edge processing taking place on smart cameras. This topic studies novel system designs where smart cameras communicate between themselves and with the edge network.

• RQ: To what degree can "smart cameras" still function in case of infrastructure (fog nodes, cloud) failure ?
• RQ: How are privacy-related concerns addressed in these systems?
• CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge

Computation Offloading for Vehicles

Modern vehicles are equipped with computing and communication modules, that enable 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

Topics of Chinmaya Dehury

email: chinmaya.dehury@ut.ee

1. What is Blockchain?

Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. Beyond this sentence, the student needs to find out what blockchain is. The student will also give a short demo on the blockchain. For some basic concepts, the student may refer :

• Puthal, Deepak, et al. "Everything you wanted to know about the blockchain: Its promise, components, processes, and problems." IEEE Consumer Electronics Magazine 7.4 (2018): 6-14.

2. Blockchain in Edge computing

In edge computing, the intelligence is embedded onto the edge devices. For large data processing capacity and in-depth analysis, generally, the data is sent to the fog environment and then the cloud servers, which is far from the sensor’s location. This topic focuses on the current progress on blockchain integration with edge computing for secure processing and transmission of the data from the sensor to the cloud.

The articles you may refer :

• - Du, Yao, Zehua Wang, and Victor Leung. "Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues." Future Internet 13.2 (2021): 48.
• - Qiu, Chao, et al. "Bring Intelligence among Edges: A Blockchain-Assisted Edge Intelligence Approach." GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE, 2020.

3. Making a contract smart with blockchain.

Smart contracts are simply programs stored on a blockchain that run when predetermined conditions are met. They typically are used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary’s involvement or time loss. They can also automate a workflow, triggering the next action when conditions are met.

• RQ1: Are the traditional contracts not smart enough? What makes a contract smart.
• RQ2: Where can we use smart contracts?
• RQ3: Where smart contracts can NOT be used?

Some research articles you may refer:

• Luu, Loi, et al. "Making smart contracts smarter." Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.
• Christidis, Konstantinos, and Michael Devetsikiotis. "Blockchains and smart contracts for the internet of things." Ieee Access 4 (2016): 2292-2303.

(Already taken) 4. Variants of DevOps

DevOps culture is gaining popularity in the IT industry. Thanks to its ability to put the development and operation in one basket. This enables the IT industry to shorten the software delivery time. The same approach is adopted in other fields with new names such as DataOps, ModelOps (AIOps, MLOps), etc.

• RQ1 : Where else the DevOps approach is adopted?
• RQ2 : What are the differences/similarities between DevOps and DataOps?

Related research articles:

• A Survey of DevOps Concepts and Challenges
• From Ad-Hoc Data Analytics to DataOps

5. Your topic

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

Topics of Pelle Jakovits

State-of-the-art solutions for Multi-organisational Smart city Data Marketplaces

The ability to share data between different organizations is necessary for applications and services to take full advantage of IoT networks, devices, and data. Building Smart Cities requires integrating, analyzing, and sharing data generated in the city belonging to a large number of organizations in the city. There have been quite a few attempts to create marketplaces where companies, data providers, and users can share and even sell their data but there are not many existing successful platforms. The goal of this topic is to investigate what are the current state-of-the art data marketplaces in this domain and also give an overview of previous attempts. Investigate what are the main challenges in creating data marketplaces for sharing real-time data streams.

• RQ1 : How ready are the existing platforms for supporting large-scale smart city scenarios?
• RQ2 : How well is real-time-streaming data supported?

Related research articles to start with:

• Spiekermann, Markus. "Data marketplaces: Trends and monetisation of data goods." Intereconomics 54.4 (2019): 208-216.
• Koutroumpis, Pantelis, Aija Leiponen, and Llewellyn DW Thomas. The (unfulfilled) potential of data marketplaces. No. 53. ETLA Working Papers, 2017.

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

(already taken) Monitoring failures in Smart-City data integrations

Being able to extract useful knowledge out of available data has become a key to success for many organizations and it is becoming more and more necessary for companies and institutions to share data between each other and use public data made available by other organizations. For example, building Smart Cities requires integrating, analyzing, and sharing data generated in the city belonging to a large number of organizations in the city. While technical solutions for building such multi-organizational data services have become mature, the ability to test, validate and debug data flows is trailing behind. For example, it is important to automatically detect faults in the data flows, such as missing data, broken integration services, outliers, etc. to avoid losing data or making wrong predictions. The goal of this topic is to study the current state-of-the-art in data monitoring focused on detecting failures in data streams and integrations.

• RQ1 : What data is needed to monitor data-stream reliability and quality?
• RQ2 : Are there practical and useable solutions for automatic detection of missing data, gaps, and broken integrations which do not depend on manual configuration of alerting rules?

An Overview of Data Quality Frameworks Monitoring Data Stream Reliability in Smart City Environments

(Already taken) Open Source Data Analytics platforms for smart-city-scale real-time data visualization and analytics

The goal of this topic is to study state-of-the-art OS solutions for large-scale streaming data visualization and analytics platforms and critically evaluate which of the would-be suitable to be used in Smart-City scenarios (using the example of Tartu City requirements) where there are a large number of individual devices that all may produce data with very varying intervals. The student should perform a literature search to study what are the features that such platforms should contain, give an overview of existing platforms, outline how well they fulfill the required requirements, and outline their advantages and disadvantages in the specified scenario.

• RQ1: What are the most important requirements for real-time data visualization and analytics platforms to be able to handle smart-city data and applications

(Already taken) Detection of faults and code smells in (Cloud) Infrastructure as Code (IaC) templates.

Infrastructure-as-code (IaC) is the means to automate the managing and provisioning of infrastructure needed for services and applications through machine-readable templates rather than manual configuration or interactive tools. As IaC has become more and more popular in Cloud platforms and on-premise platforms such as OpenStack or Kubernetes, it is critical to avoid accidental faults or misconfiguration before they are enacted as the result may be service failures or wasted money when the faults are detected later during deployment. The goal of this study is to investigate what are the state-of-the-art means for verifying the quality of IaC templates.

• RQ1: What is the state-of-the-art in IaC template quality/fault detection.
• RQ2: Can the approaches in code smells and programming faults be applied on IaC templates

Related articles to start with:

• The do’s and don’ts of infrastructure code: A systematic gray literature review - https://www.sciencedirect.com/science/article/pii/S0950584921000720

Topics of Shivananda R Poojara

Topic 1: Serverless data pipeline(SDP) for IoT and public cloud vendors The public cloud providers such as AWS, Google Cloud, and Microsoft Azure have different services that constitute data processing pipelines from edge to cloud in IoT environments. So, the objective of the topic is to study/explore those SDP services/platforms from different cloud vendors and compare w.r.t different aspects such as cost and etc. In the seminar, the following research questions could be attained

• RQ1 : What is SDP and relevance to IoT?
• RQ2 : List and describe different services to constitute SDP that are provided by the public cloud vendors such as Google Cloud, AWS, Microsoft Azure?
• RQ3 : Benefits and systematic comparison between platforms.

Topic 2:Usage based insurance 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.

• RQ1 : How IoT is used in this finance sector?
• RQ2 : What are different tools and techniques used?
• RQ3 : How does IoT data is managed ?

Related research articles to start with:

• Theses on usage based insurance

Topic 3: COSCO - Container Orchestration for fog computing Aim of this topic is to explore COSCO(a fog computing tool) and understand the diffrent container orchestartion algorithms in this tool. The COSCO built with efficient machine learning algorithms for containr placement with various QoS parameters such as energy, latenacy etc,.

• RQ1 : What is COSCO and its architecture?
• RQ2 : Study the GOBI and GOBI* algorithms for container placement in fog networks.
• RQ3 : How to deploy in real time test bed?

Related research articles to start with:

• COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Topic 4: Sensor fusion in autonomous vehicles The autonmous vehicles are becoming a pivotal technology that can revolutionize the future of transportation and mobility. The vehicles are integrated several set of sensors such as LiDAR, radar and cameras, including engine monitoring, geo-location sensors. So by multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles.

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

Related research articles to start with:

• [[ https://www.mdpi.com/1424-8220/21/6/2140 | Sensor fusion-Review]