Seminar Topics
Full list of topics will be announced during the first seminar.
Topics of Deepsubhra Guha Roy
Email: deepsubhra.guha.roy@ut.ee / roysubhraguha@gmail.com
Blockchain aware Smart Framework Design for IoHT Data Orchestration
Nowadays, security is the prime parameter to choosing any application or framework. If we use that framework or application for daily use, we are actually sharing our personal data with that framework. To enhance the level of security, we can introduce a blockchain framework to design that framework to make it more secure. When we are going to share our health data with that environment, we should take care of the security aspect.
- RQ1: What is blockchain and how it will help to enhance security?
- RQ2: How can protect your IoHT data from hackers?
- RQ3: How will you audit your network is vulnerable or not?
- RQ4: How will you enhance the security of your framework?
Related research articles to start with:
Security Enhancement for IoT Data Flow through Smart Gateway using MQTT-SN
IoT is suffering from a small lifetime of participating nodes, packet loss during delivery, an end-to-end delay, security of data, and many more issues. One of the primary protocol of IoT is MQTT-SN. When we share our IoT data in the network, we will face several issues to transmit the data from sender to receiver. During that transmission time, how we will protect our precious data from others, it’s a major challenge.
- RQ1: How to enhance the data flow security using MQTT-SN?
- RQ2: What is the Smart gateway selection algorithm is meant?
- RQ3: What are the key parameters to enhance the security of IoT data?
Related research articles to start with:
Automatic application based Cloud Instance Creation and Code Migration for Computation Offloading
Life is very difficult without applications. But applications are very much resource hungry. When we want to run our applications, but we don’t have the resource, we must take the help of cloud instances to run those applications. But how will we create that in runtime? What parameters should we all take care of for computation offloading, how the code will be migrated to cloud instance, and what will be the cloud instance's specification? These questions will be in our minds.
- RQ1: How to select the optimized cloud instance for running the application?
- RQ2: What are the parameters that should be taken care of during an instance selection?
- RQ3: How the code migration and offloading will be done?
Related research articles to start with:
Topics of Jakob Mass
Smart Networking, Security for IoT
Embedded intelligence WiFi Routers (topic taken)
More capable Wi-Fi routers can run various services applications in addition to the basic networking functions. For example, the OpenWRT project allows installing various packags on a router, including servers, network traffic analyzers and so forth. This topic studies the possibility of fortify insecure IoT devices within the network with extra functionality run on the Wi-Fi router. Namely, the router can translate the local insecure traffic with the IoT device to secure external communication.
- RQ: Does the externalized security limit functionality of IoT devices?
- RQ: What is the performance overhead of this approach?
- SecWIR: Securing Smart Home IoT Communications via Wi-Fi Routers with Embedded Intelligence
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
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 Computing & IoT
Stream Processing (topic taken)
Stream Processing frameworks are a crucial part of IoT systems, they digest the flow of (raw) IoT data, transform and process it as necessary and forward the resulting information stream to other systems, databases. This topic is oriented towards comparing the performance differences of existing open-source stream processing frameworks, such as Apache NiFi, Apache Storm, Spark Streaming and others.
- RQ: What are the different fault-tolerance strategies for stream processing systems?
- Performance evaluation of real-time stream processing systems for Internet of Things applications
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 (topic taken)
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
Bluetooth Low Energy Beacons (taken)
Bluetooth Low Energy (BLE) technology is one of the popular wireless technologies used in IoT. BLE beacons are low-power tiny stand-alone devices that can other BLE devices (such as smartphones) can communicate with. This topic studies the existing applications and potential of using BLE beacons in IoT.
- RQ: Are battery-powered BLE beacons feasible for long term (1yr+) deployment?
- RQ: What are the existing use cases for indoor smart environments (home, office, factory) with BLE?
- BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities
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
Wearables
This topic studies how wearables (such as smart watches) can be used for more than just sensor data gathering and message relaying. One possibility is to pursue additional collaboration between the wearable and smartphone, for instance, offloading tasks to the mobile for saving energy on the wearable.
- RQ: What limits do wearable SDK platforms impose for implementing offloading mechanisms?
- RQ: What kind of tasks make sense for wearables to offload?
- DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Topics of Chinmaya Dehury
email: chinmaya.dehury@ut.ee
1. Vehicle as a data carrier.
Data are sent from one device to another, either over a wireless or wired network. Is it possible to transfer data from one point to another without any wireless or wired communication channel? In this topic, you will get a chance to investigate some alternative communication channels, such as the movement of vehicles acting as a communication channel. Such a communication channel poses several challenges. Some of the Research Questions (RQ) you will be addressing throughout the seminar are:
- RQ1 : What are the advantages and disadvantages of such alternative communication channels?
- RQ2 : Can we really apply this in the real world?
Related research article:
2. DevOps in real world
DevOps focuses on reducing the time between committing a code change and reflecting the change in the production. The success of this approach lies in the adoption of IT companies. However, what are the essential features DevOps is offering that causes such a massive success.
- RQ1 : You will investigate how and why companies adopted DevOps?
Related research article:
3. 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:
Topics of Pelle Jakovits
Smart city and IoT Data Marketplaces - Challenges and opportunities
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 main challenges of creating such data marketplaces, give an overview of the state-of-the art in this domain and introduce the most successful attempts.
- RQ1 : What are the main technological barriers in creating global data marketplaces?
- RQ2 : How ready are the existing platforms for supporting large scale smart city scenarios?
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:
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:
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?
- RQ2 : Benefits and systematic comparison between platforms.
Topic 2:IoT based Predictive Maintainance(PdM) in industrial applications.
Topic 3: Recent trends in health care edge analytics.
Topic 4: Evolution of edge analytics and an industry approach.
Topic 5: Role of devops in edge analytics.
Topics of Mainak Adhikari
- Power and Energy-efficient task offloading in hierarchical Fog-cloud Environment. Cloud Computing is a scale-based computing model that consists of thousands of servers and consumes an extremely large amount of electricity that will increase the cost of the service provider and harm the environment. However, the Fog devices are distributed globally with limited resource capacity and consume a minimum amount of energy while processing IoT applications. The energy consumption of the resources should directly proportional to the CO2 emission rate and temperature of the computing devices. This should also affect the environment. Moreover, unlike VM instances, the containers contain a minimum amount of resources that consume minimum energy. So, the energy-efficient offloading strategy is an important issue in Fog and cloud environment domain for reducing the energy consumption and minimizes the CO2 emission rate and temperature of the computing devices. One of the energy-efficient scheduling strategies is to place the IoT applications to the local computing devices with minimum delay and transmission time.
- RQ1: How to reduce computing and communication power consumption of real-time applications?
- RQ2: How to design an intelligent service deployment or resource provisioning strategy?
Related research articles to start with:
- Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges
- A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges
- IoT task Scheduling with dwindling resource requirements of the Fog devices. The main goal of the emerging technologies in a distributed environment is to utilize the resources efficiently of the computing devices. The services providers receive the sensed data from various sensors including several contiguous stages, each having a specific size and resource requirements. Each application associated with response time and a deadline. So, the main goal of the IoT task scheduling with dwindling resource requirements is to find an optimal computing device with sufficient resource capacity which should meet the deadline of the tasks with efficient resource utilization. For example, patient monitoring data should be offloaded to the local Fog devices for faster processing with a minimum delay within the QoS constraint.
- RQ1: How to find an intelligent service provisioning strategy that satisfies dwindling resource requirements of IoT applications?
- RQ2: How to meet the multiple Quality of Service constraints such as the deadline and cost of the IoT applications in Fog/Edge networks?
Related research articles to start with:
- Akka framework based on the Actor model for executing distributed Fog Computing applications
- Joint Computation Offloading and Scheduling Optimization of IoT Applications in Fog Networks
- Topology Control in Edge computing. Topology control is a fundamental research aspect in dynamic edge networks. An edge computing domain is made up of heterogeneous nodes that seamlessly interact with each other when reshaping the network typology. Thus, one of the most challenging tasks is to relocate the fog devices at optimal locations to repair or augment coverage or they can deploy or reallocate the static sensor nodes or objects. Both of these perspectives require an optimal approach to meet the objectives. NIMH/Dynamic/Greedy/deep learning algorithms help in self-relocation of the sensor nodes or the edge devices based on the user demands which reduces the latency and energy consumption of the data offloading and processing. Moreover, the NIMH/Dynamic/Greedy/deep learning algorithms find reliable communication networks for data transmission to suitable edge devices with quick succession. This policy reduces the overall transmission time of the real-time applications with minimum network overhead.
- RQ1: How to control the flow of data with an efficient topology control mechanism in dynamic edge networks?
- RQ2: How to design an intelligent topology control mechanism in dynamic edge networks?
Related research articles to start with: