Bassa - Restructure the API code and make improvements

Google Summer of Code 2020


Welcome to the SCoRe Lab Google Summer of Code (GSoC) 2020 project ideas page. We are a dynamic and enthusiastic nonprofit research group pioneering in Internet of Things (IoT), Embedded Systems, Computer Security, Software Tools and 'Wireless Adhoc and Sensor Networks' and is one of the best groups in South Asia. This is the 5th time that we are participating in the GSoC, we will use this page to develop possible project ideas that have on the above mentioned areas. Please note that anyone who is interested can participate in this process. You do not have to be a GSoC student or mentor to suggest possible project ideas. You can also talk to us about possible project ideas and we are happy to improve or heip you with them. Please keep in mind that projects need to be realistically something that is able to be functionally completed by a student working full time for about eight weeks. Thanks!

Mailing list: score-community@googlegroups.com

Gitter Channel: https://gitter.im/scorelab/

Suggested Proposal Template

Important Guidelines on Submitting Ideas

There are some important guidelines to submit ideas, please read these carefully before adding your ideas;:

  • There is a fixed time period for implementing and coding your ideas.
  • Come up with attainable goals and you will be able to complete what you set out to do. You can always contact our mentors and community and get an idea about the workload and whether you might be able to complete them.
  • You are free to come up with your own ideas. The ideas should be about Internet of Things (IOT), Embedded Systems, Computer Security, Software Tools and 'Wireless Adhoc and Sensor Networks’. Also if you love to work on any of these subjects but do not have an idea you can always contact us.
  • Lets Talk! The best way to solve problems that you might have is to contact our mentors and also our community. This will help you to not get bogged down in your ideas and to move on.
  • We encourage you to do documentation so that we can keep track of your progress and also help you if things are not going according to plan. Although not compulsory we have a strong belief that this method can cut down your time to code and also the workload of the mentors drastically.

Project list for GSoC 2020

  • S-Chain - Blockchain Based Tracking System

    S-Chain is a food origin/transport tracking application on top of blockchain. If you buy a pack of Tea from a Shop there is no option to find or guarantee the origins of the tea leafs that made them who processed them and all sort of other details. Even if we do so there is no way we can do so without a central authority. But due to the emergence of the block chain we can do all above without a central governing body. What we provide is the implementation of the blockchain technology for this process by using our available S-Chain platform. This software platform, has implemented this blockchain-enabled traceability using Hyperledger Sawtooth. So the customer can authenticate the origin of the product.

    Expected Results: Implementing Hyperledger Sawtooth Blockchain base backend API. Mobile App to add tracking data, Admin dashbord.
    Required Knowledge: Hyperledger Sawtooth. Python, NodeJs, ReactJs, React Native
    Github: https://github.com/leopardslab/S-Chain
    Possible Mentors: TBD
  • Raven - Highly available and scalable cloud-native API monitoring tool

    A cloud-native API monitoring tool to monitor the health of 3rd party APIs. APIs can be grouped by spaces. A target represents APIs to be monitored. Monitoring reports should be generated. The initial version will be based on AWS stack.

    • Uptime
    • Response time
    • Latency

    Further region-based monitoring can be implemented to provide drilled down results for the metrics mentioned above.

    Expected Results: Implementing authentication module. APis for spaces, targets and metrics. Report generation for metrics. Unit testing.
    Required Knowledge: Go Programming Language, NodeJS Docker, Basic Blockchain knowledge, Hyperledger Fabric
    Github: https://github.com/leopardslab/3Mon
    Possible Mentors: Rajika Imal
  • OpenMF - Create web clinet for OpenMF

    OpenMF is an opensource forensic tool for Android smartphones that helps digital forensic investigator throughout the life cycle of digital forensic investigation. Services provided by Androphsy includes.

    • Digital forensic case and evidence management
    • Raw data acquisition – physical acquisition and logical – file system level acquisition
    • Meaningful evidence extraction and analysis support
    • Evidence presentation

    Expected Results:
    • Create web clinet for OpenMF
    • Test the implementation
    • Write documentation

    Required Knowledge: Python, Android
    Github: https://github.com/scorelab/OpenMF
    Possible Mentors: TBA
  • Decentralized Access Manager for Industrial IoT

    There are multiple stakeholders engaging in IIoT systems. For example in the energy and power industry energy prosumers, consumers, regulators, traders, and community are few of the stakeholders taking part. These stakeholders must trust each other to carry out business processes smoothly and it will maximize the profit and efficiency of the entire industry. Currently, access control in IIoT systems is based on centralized approaches that introduce extra friction during interoperability. The objective of this project is to implement a decentralized access control mechanisms based on smart contracts to take over the control of asset management from central parties.

    Expected Results: We will identify the main milestones during the initial study and that needed to be implemented during the summer. The deliverable would be the working smart contract implementation of the access control system along with the Wiki documentation.
    Required Knowledge: Go Programming Language, NodeJS Docker, Basic Blockchain knowledge, Hyperledger Fabric
    Github: https://github.com/scorelab/
    Possible Mentors: Tharindu Ranathunga, Pratik Dhanave
  • Senz - Create a Node.js module for senz clients

    Implement a Node.js module to use in node.js applications to give the ability to act as a Senz device and communicate with the Senz server. The module should work as a message parser between the application and the server.

    Expected Results: At the end of your summer, we expect that you will create a node.js module.. And users should be able to install and use the module in their node.js projects to communicate with the Senz server.
    Required Knowledge: Node.js, Scala
    Github: https://github.com/scorelab/senz
    Possible Mentors: Sumedhe Dissanayake
  • Dunner - Complete the tasks in Road Map of Dunner up to v1.0

    Dunner is a task runner tool like Grunt but used Docker images as CircleCI does. You can define tasks and steps of the tasks in your `.dunner.yaml` file and then run these steps with `Dunner do taskname`. We have several features planned in our road map, we need you to complete them one issue by one. See the list of features planned for v0.1 and v1.0 below.

    Expected Results: At the end of your summer, we expect that you will complete all the features planned up to v1.0. Also, you have to improve the wikis which are related to your development areas. Come up with a plan what features you are gonna do when as a timeline. Consider about improving the wikis also in your timeline.
    Required Knowledge: Goland, Docker, some idea about Task Runners, Linux
    Github: https://github.com/leopardslab/Dunner
    Possible Mentors: Milindu Kumarage, Rumesh Hapuarachchi
  • OpenIoE - Implement user interface for OpenIoE

    Currently, we are using the generated jHipster UI for OpenIoE. We need to implement a customized user interface for providing a better user experience. Angular or React can be used.

    Expected Results:
    • Rewrite the frontend with Angular or React including all current functionalities
    • Re-design the current interfaces
    • Write Frontend unit tests and component tests

    Required Knowledge: Angular or React, Spring Boot, ActiveMQ Artemis, MQTT, AMQP and Cassandra
    Github: https://github.com/scorelab/OpenIoE/issues/28
    Possible Mentors: Tharidu Fernando, Charith
  • OpenIoE - Implement authentication for message broker and rest API for OpenIOE

    Currently, OpenIOE does not implement any security mechanisms for publishing or subscribing to any registered sensors in the backend. In order to support more clients, MQTT authentication and GUID based URL routing can be used.

    Expected Results:
    • Implement authentication for OpenIOE message broker and rest API
    • Write unit tests and component tests
    • Integrate the build and test cases to TravisCI

    Required Knowledge: Spring Boot, ActiveMQ Artemis, MQTT, AMQP and Cassandra
    Github: https://github.com/scorelab/OpenIoE/issues/27
    Possible Mentors: Tharidu Fernando, Charith
  • EtherBeat - Design and implement an analyzing framework on top of EtherBeat

    Implement an analyzing framework which can provide users important information about Ethereum platform. This must utilize existing GraphQL schema to answer the queries. Eg;

    • How many transactions carried out by 'X' address?
    • How much ETH value 'Y' user has transferred last 30 day?
    • Transactions summery for block range
    • More

    Expected Results:
    • Design and implement an EtherBeat analyzing framework
    • Test EtherBeat analyzing framework with test cases
    • Write documentation

    Required Knowledge: GraphQL Js and ReactJs
    Github: https://github.com/scorelab/EtherBeat
    Possible Mentors: Ruwan Geeganage, Tharidu Fernando and Keshan Sodimana
  • Go Social - Implement Community App Using Go Social Framework

    Propose and develop community app using Go Social Framework. In the proposal you need to provide the app ui design, and use case of the app

    Expected Results:
    Required Knowledge: NodeJS, ReactJS, ReactNative
    Github: https://github.com/scorelab/Go-social
    Possible Mentors: Dinith
  • ChainKeeper - Optimized Blockchain Analytical Interface for ChainKeeper

    Chainkeeper is a web-based application which can be used to retrieve Bitcoin blockchain data via API support from BlockSci core blockchain data. In analytics perspectives, this approach is highly inefficient due to some constraints. Therefore, needs to build a library for ChainKeeper which acts as data and analytical interface for blockchain to retrieve relevant data by writing queries. There should be blockchain supportable classes such as Blockchain, Blocks, Transactions, Input, Output and Address with range support. This library needs to support python with more optimization techniques.

    Expected Results:
    • Evaluate existing blockchain analysis library and identify performance issues
    • Come up with a suitable design to develop an analysis library
    • Implementation of the Analysis Library
    • Evaluation results with terms of data and analysis
    • Documentation of the library

    Required Knowledge: C++, Blockchain, Bitcoin, BlockSci, Python, C++ Boost
    Github: https://github.com/scorelab/ChainKeeper
    Possible Mentors: Isuranga Perera, Sajitha Liyanage
  • ChainKeeper - Optimized Bitcoin blockchain parser for memory constrained devices

    Bitcoin blockchain is a huge data structure with 180+GB in size. Due to this huge size available Bitcoin parsers take several hours to parse the entire blockchain. As an example BlockSci parser takes 11 hours with an 8GB cache. Because of that most of the available Bitcoin parsers are inefficient on memory constrained devices. The goal of this project is to design and implement a Bitcoin parser(may support forks of Bitcoin as well) which can use available memory efficiently to reduce blockchain parsing time on memory constrained devices.

    Expected Results:
    • Evaluate existing Bitcoin parsers and identify performance issues
    • Come up with a suitable strategy to store parsed Bitcoin data
    • Design and implement the parser
    • Evaluation
    • Documentation

    Required Knowledge: C++, Blockchain, Bitcoin
    Github: https://github.com/scorelab/ChainKeeper
    Possible Mentors: Isuranga Perera, Sajitha Liyanage
  • DengueStop - Community app

    DengueStop provides a simple and effective way to report and discover dengue incidents around your area, with the help of community. Student has to create the mobile app and the backend for this project.

    Expected Results: You can go ahead and be creative about the milestones. We expect a fully functional mobile app that lets users mark the locations with the Dengue patients, at the end of the GSoC period.
    Required Knowledge: Flutter / React Native, NodeJS
    Github: https://github.com/scorelab/dengue-stop
    Possible Mentors: Oshan Mudannayake
  • DengueStop - Admin panel

    DengueStop provides a simple and effective way to report and discover dengue incidents around your area, with the help of community. Student has to create the adin panel and the backend in this project.

    Expected Results: You can go ahead and be creative about the milestones. We expect a fully functional admin panel that can view the locations of the Dengue patients at the end of the GSoC period.
    Required Knowledge: React, NodeJS
    Github: https://github.com/scorelab/dengue-stop
    Possible Mentors: Oshan Mudannayake
  • ImageLab - Implement a prototype version of ImageLab

    ImageLab is a standalone tool that lets the user play with OpenCV using its GUI. It is a tool for the students and researchers who are new to image processing. Its functionality is similar to Scratch (https://scratch.mit.edu) except that it allows used to interact with the OpenCV library.

    Expected Results: We expect you to create the basic protytope of ImageLab for this year GSoC. You can talk about your ideas with project mentors and fix a set of milestones before writing the proposal. The scope of the initial version of the project is up to you. We would like if you could cover all the basic image processing operations.
    Required Knowledge: Java, JavaFX, OpenCV
    Github:
    Possible Mentors: Oshan Mudannayake
  • LabelLab - Integrate ML module for LabelLab

    LabelLab is an image labelling tool for a researcher. It can label bulks of images in order to be used for machine learning tasks. LabelLab consists of a web app and a mobile app. The web app will allow the admin of the project to manage the image repositories while the mobile app will be used to label the images on the fly. At the moment web app lacks machine and deep learning module to create models with labeled data-sets.

    Expected Results: https://github.com/scorelab/LabelLab/milestone/3
    Required Knowledge: Experience with any of the ML libraries such as Tensorflow / Pytorch etc., Python, React, NodeJs
    Github: https://github.com/scorelab/LabelLab
    Possible Mentors: Oshan Mudannayake
  • LabelLab - Add multiple data-lableing feature for LabelLab

    LabelLab is an image labeling tool for a researcher. It can label bulks of images in order to be used for machine learning tasks. LabelLab consists of a web app and a mobile app. The mobile app will allow the admin of the project to manage the image repositories, label the images on the fly and to classify images using already trained models.

    Expected Results: https://github.com/scorelab/LabelLab/milestone/1
    Required Knowledge: Flutter, NodeJs, Python, Experience training ml models is desired but not essential
    Github: https://github.com/scorelab/LabelLab
    Possible Mentors: Udesh Kumarasinghe, Oshan Mudannayake
  • LabelLab - Revamp the web application

    LabelLab is an image labelling tool for a researcher. It can label bulks of images in order to be used for machine learning tasks. LabelLab consists of a web app and a mobile app. The web app will allow the admin of the project to manage the image repositories while the mobile app will be used to label the images on the fly.

    Expected Results: https://github.com/scorelab/LabelLab/milestone/2
    Required Knowledge: Python, React, NodeJs, Experience training ml models is desired but not essential
    Github: https://github.com/scorelab/LabelLab
    Possible Mentors: Oshan Mudannayake
  • TensorMap

    TensorMap will be a web application that will allow the users to create machine learning algorithms visually. TensorMap will support reverse engineering of the visual layout to a Tensorflow implementation in preferred languages. The goal of the project is to let the beginners play with machine learning algorithms in tensorflow without less background knowledge about the library.

    Expected Results: As this is a challenging project, we won’t define the exact milestones for GSoC. Our expected output from this year is to fix the bugs of the TensorMap prototype and get it to a state that is usable by the end-user.
    Required Knowledge: Tensorflow, ReactJS, NodeJS
    Github: https://github.com/scorelab/TensorMap
    Possible Mentors: Keshan Sodimana, Raveen Harith Perera

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