Seminar Topics

Topics of Jakob Mass

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

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

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 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

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 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)

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.

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.

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.

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 monetization 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.

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

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

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: Investigation of opensource serverless platforms

The aim of the thesis is to study the existing investigation of the opensource serverless platforms such as OpenFaaS, Nuclio, Knative, Fission, and Kubeless. The following would be research questions that need to address:

• RQ1 : What are the architectural components used to design these systems?
• RQ2 : How do throughput and latency behaviors on various types s work workloads?
• RQ3 : How do auto-scaling work?

Related research articles to start with:

 Understanding Open Source Serverless Platforms: Design Considerations and Performance

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:

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