Seminar Topics

Internet of Things -- Satish Srirama, Mohan Liyanage and Chii Chang (Responsible persons)

1. Smart Cities and the Internet of Things

In this topic, student will study what are the promising application use cases/scenarios for IoT-based smart cities.

2. A Study on Service Description Approaches in the Internet of Things

Various approaches have been introduced for description of the Internet of Things (IoT) services/resources. From the Service-Oriented Architecture (SOA) perspective, WSDL, WADL, DPWS were used. SensorML, SenML, were proposed for sensor devices. UPnP was long introduced for general office devices. Recent IETF CoAP (RFC7252) & CoRE standards also introduce their approaches for describing resources. Bluetooth has its own protocol and approach such as UUID and many different ports to describe bluetooth devices and resources. Moreover, we haven’t mention about the JSON-based service descriptions. It is now becoming impossible to rely on one single standard to enable autonomous machine-to-machine (M2M) communication because there is no global common standard for machine readable metadata. In this topic, student will study and compare all the service/resource description languages for IoT.

3. A Framework for Trustworthy Internet of Things

Security is one of the major challenges in the Internet of Things (IoT). Although various security-related works have been done for IoT, existing works were only based on the classic network security-aspect. For instance, “Cryptography alone cannot solve protecting information in IoT problem as internally compromised nodes can generate bogus information and still authenticate it using valid cryptographic” (Lize, Jingpei, & Bin, 2014). Further, the centralized solutions are not feasible for IoT since fundamentally, IoT is based on distributed environment. Assuming there can be a central management party to govern the entire environment is not realistic. Hence, IoT requires a feasible distributed trust strategy to overcome the drawback of existing security models......(see detail)

4. The ONE Simulator for DTN Protocol Evaluation

Delay Tolerant Networking (DTN), based on the store-carry-and-forward routing principle. If the next hop is not immediately available for the current node to forward a message, the node will store the message in its buffer, carry it along while moving, and forward it to other appropriate nodes until the node gets a communication opportunity which helps to forward this message farther. The ONE is a simulation environment that is capable of generating node movement using different movement models, routing messages between nodes with various DTN routing. In this topic, student will provide a comprehensive study of DTN routing algorithms with ONE simulator.

5. A Literature Survey on the Internet of Things Middleware

Student will study and compare the recent European Commission IoT projects.

6. Smart Display for room occupancy and schedule In this practical project, the student designs and implements an Android-based smart display for presenting information regarding a rooms occupancy. The smart display syncs data from sources such as Google Calendar, adds other useful information such as a weather report and displays this information in a nice UI. Such a smart display is placed near the entrance of a room in an office for example, allowing for people to update room occupancy details online, etc.

7. A survey on mobile cyber physical systems (CPS) for the internet of things (IoT)

Modern mobile CPS consists with massive number of sensors/actuators that generate a large amount of data for the processing and decision making in industry and academic organizations. The communication between the physical world and the cyber world is mobile communication and that is unreliable with the mobility. In such a situation there are many challenges occurred when up and running reliable system to provide un-interrupted services. During the past years, many research works have been done to identify the challenges and to provide some solutions. In this work, we divided the challenges that are referred from literature into three main categories as:

• Accessibility – student should read some papers explain what’s ‘accessibility’? Why accessibility is an important research direction? What are the challenges it faces. (one student)
• Reliability - student should review some papers explain what’s ‘reliability? Why it is an important research direction and about the challenges it faces. (one student)
• Adaptability - how it can help to improve the IoT/CPS? What are the challenges involved? (one student)

Mobile Computing -- Jakob Mass (Responsible persons)

Continuous Integration for Android The student is expected to conduct a literature survey on the topic of continuous integration (CI) solutions for iOS and Android, such as Greenhouse CI. The most prominent CI solutions should be analysed and compared in depth, and where possible, tried out first-hand.

Mobile Cloud -- Satish Srirama (Responsible persons)

1. Augmented reality (Google Maps, GPS, info from Wikipedia or other sources)

- wifi/mobile network enabled phone gets info from gps, google streetview, match image, show information about place in phone)

- image recognition from live stream. A computer vision algorithm for looking direction detection in real-time.

2. NFC developments, possibilities, applications

"semantic city" - develop nfc app and system so tags placed in city and according to liking some of the places (by nfc touch) app suggests other places nearby to go visit or see, map

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

• IoT value chain, its' components and value distribution
• Compare the IoT platforms in a matrix by their functionality
• Home automation ecosystems - Apple HomeKit, Google Nest, etc
• Home automation possibilities on open platforms
• Home automation possibilities on commercial platforms
• Wireless vs wired home automation - inter-connectivity, protocols, security, etc
• Possibilities for universal Home hub - pros and cons of open hubs vs controlled ecosystems
• Sensors and actuators in home environment - inter-connectivity, standards, security
• Data models - international industry standards, compatibility, etc
• Home Robots as Home automation hub

Hadoop cloud computing projects -- Pelle Jakovits (Responsible person)

1. New Generation MapReduce - Apache Beam and Apache Tez When MapReduce first came out, it was very widely adopted. Since then, a number of higher level frameworks such as Pig or Hive have been introduced that simplify data processing but still use MapReduce internally. MapReduce is known to have many limitations, such as always having to write input, intermediate and output data to disks, and relatively slow configure and start up time. Apache Tez is designed to directly replace MapReduce for these higher level frameworks and Apache Beam is an open source alternative to MapReduce designed by google for dataflow processing. Student should give an overview of the conceptual changes that Tez and Beam introduce, describe their advantages and respective differences and demonstrate their usefulness with real examples from a chosen field of data science. Student can also choose to compare them to an earlier MapReduce alternative: Spark.
2. Real time & Large Scale data processing with Apache Storm Apache Storm is one of the first real time stream processing frameworks. Typically stream processing frameworks buffer incoming data and process them in batches, but Storm allows you to process any incoming data object in real time. Task of the student is to give an overview of the Apache Storm frameworks and its capabilities, compare it to other similar tools, implement some use cases to demonstrate its usefulness & impact.
3. SparkR - Scaling R scripts - Apache Spark is a distributed computing framework which has been designed to replace Hadoop MapReduce for large scale data processing. SparkR is an Spark API to scale the execution of R scripts in a cluster of machines. The goal of this topic is to study how efficiently SparkR can scale R scripts in a real cluster and to compare its capabilities and ease of use to other Parallel R solutions. Student should existing R scripts from other scientific fields, adapt them to SparkR, run them in a real cluster and analyze their performance.
4. Spark Mllib - Apache Spark is a distributed computing framework which has been designed to replace Hadoop MapReduce for large scale data processing. Spark MLlib is a machine learning library built on top of MapReduce. The goal of this topic is to study the capabilities of Spark MLlib library, compare its functionality to other parallel machine learning libraries and demonstrate its effectiveness with freely chosen use cases.
5. NEWT - A fault tolerant BSP framework on Hadoop YARN - NEWT is a HPC framework ontop of Hadoop YARN which addresses the MapReduce issues with more complex algorithms. It was developed in our group and is in a working prototype state. Student should try to use NEWT to implement one or two scientific algorithms on NEWT and measure its efficiency and parallel speedup in a cluster. One outcome could be also a tutorial on how to adapt algorithms to to NEWT.
6. HARP large scale processing framework – Student should study how HARP can be used to parallelize scientific computing algorithms or process large scale data.
7. Cloudera Impala is based on Google Dremel and aims to provide Real-Time queries ontop of Apache Hadoop. Impala raises the bar for query performance while retaining a familiar user experience. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. Furthermore, it uses the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive, providing a familiar and unified platform for batch-oriented or real-time queries. Student should study how Impala can be used to speed up MapReduce or Hive computations by replacing some of the computing tasks with Impala queries instead.

Cloud deployment -- Pelle Jakovits (Responsible persons)

1. Automatic deployment of scientific computing experiments using CloudML. CloudML enables the modelling deploying complex software systems in the cloud. CloudML is focused on enterprise service oriented applications and does not provide means to interact with already deployed system. The goal of this topic is to study what is needed to support executing scientific computing experiments on systems deployed with CloudML and how to integrate this approach with the CloudML engine.
2. An interactive not-so-random visualizer for CloudML. CloudML enables the modelling deploying complex software systems in the cloud. One of it's advantages is that the user retains the model that represents the deployed system, and thus can modify the model at any time and re-deploy it. The goal of this work is to improve the graphical user interface for CloudML which currently is in a prototype state, and to study how to keep track of changes that are made to the deployed system over time.
3. CloudML comparison to other cloud deployment managers CloudML enables the modelling deploying complex software systems in the cloud. The goal of this topic is to compare the functionality and ease of use of CloudML to other existing cloud deployment tools such as Cloudify, Puppet or Chef and to propose improvements to CloudML that would simplify it's real life use.

Fog Computing -- Chii Chang (Responsible persons)

1. Fog Computing - In the near future of smart city environments, the Internet of Things (IoT) devices will be utilised in various ubiquitous applications, such as Ambient Assisted Living, Augmented Reality, assisting smart vehicles and so on. All these applications require rapid responses for their computational tasks in which the classic distant Cloud computing systems cannot fulfil. Fog computing model utilising computational resources in the close proximity (e.g., Internet access points) of the users to provide the rapid response of their computational tasks, which were originally done at the Cloud-side. The Fog brings the Cloud to the ground. Such a model brings new opportunities to local wireless network providers. They can utilise their existing infrastructures to provide the same kind service as what classic Cloud services provide. Further, the urban applications deployed by the local businesses can also utilise Fog to enhance their Quality of Experience for their users. In this topic, student will study about Fog Computing, its potential and challenges.
2. iFogSim - iFogSim is a Fog computing simulator. Student who takes this topic can perform a practical study on the simulator and justify the advantages and disadvantages of the simulator.