GR-342 Integration of Blockchain in Computer Networking: Overview, Applications, and Future Perspectives for Software-defined Networking (SDN), Network Security and Protocols

The rapid advancement and increasing complexity of computer networks have created a need for robust, secure, and scalable solutions to manage and protect network resources. Blockchain, an emerging distributed ledger technology, offers enhanced security, transparency, and privacy preservation, making it a promising solution for addressing networking challenges. This paper presents a comprehensive survey of blockchain integration in computer networking, focusing on its potential applications, benefits, and future perspectives in Software-defined Networking (SDN), network security, and networking protocols. We identify that blockchain\u27s tamper-proof nature could significantly improve network security by mitigating risks associated with centralized control and single points of failure. The integration of blockchain in computer networking has the potential to increase trust and transparency among network participants, as it allows for secure, verifiable, and auditable transactions and communication. Blockchain also can streamline the management of Software-defined Networking (SDN) by enabling decentralized and automated network control, resource allocation, and orchestration. We also find that utilizing blockchain can address network challenges, such as mitigating DDoS attacks, enhancing intrusion detection and prevention, and securing routing protocols. However, we identify potential limitations of blockchain integration in computer networking, such as scalability challenges arising from the growing size of the distributed ledger and increasing network traffic. We emphasize the need for further research in optimizing consensus mechanisms, enhancing scalability and privacy preservation techniques interoperability, and facilitating standardization of networking protocols and practices

GR-136 - Students Certification Management (SCM): Hyperledger Fabric-Based Digital Repository

Abstract: The higher education sector has been heavily impacted financially by the economic downturn caused by the pandemic that has resulted a decline in student enrollments. Finding cost-effective novel technology for storing and sharing student\u27s credentials among academic institutions and potential employers is a demand. Within the current conventional approach, ensuring authentication of a candidate’s credentials is costly and time-consuming which gives burdens to thousands of prospective students and potential employees. As a result, candidates fail to secure opportunities for either delay or non-submission of credentials all over the world. Blockchain technology has the potential for students\u27 control over their credentials; degrees and transcripts for instance that will allow seamless streamlining of the sharing of educational records during changing and transferring schools, higher education, or even employment processes when need to show credentials. To implement the novel idea, we conduct a preliminary survey, study the existing applications, and investigate the feasibility of a Blockchain-based system to exploit the potential. Based on our findings, we propose a Students Certification Management System (SCM) by adopting Emerging Hyperledger Fabric that will offer a universal, tamper-evident, immutable, and secure educational certificate storing and sharing network. Our primary aim is to construct the proposed system into an educational certificate repository network using consortium blockchain for different entities including, (i) educational institutes to manage the network (ii) students and authorized third parties to access verifiable digital certificates and transcripts. Initially, we introduce an advanced architectural framework of the proposed system that has the potential in improving data flow between academic institutions, students, and potential employers. For ensuring transparency, each attempt in storing, sharing, and accessing credentials by the authenticated users within the proposed network shall be stored in the ledger which is secure and non-corruptible. Our future direction is to implement the architectural framework into an educational certification repository network within a private blockchain network.Department: Software Engineering and Game Design and DevelopmentSupervisor: Dr. Hossain Shahriar Dr. Maria ValeroTopics: Software Engineerin

GR-53 An Investigation on Non-Invasive Brain-Computer Interfaces: Emotiv Epoc+ Neuroheadset and Its Effectiveness

Neurotechnology describes as one of the focal points of today’s research around the domain of Brain-Computer Interfaces (BCI). The primary attempts of BCI research are to decoding human speech from brain signals and controlling neuro-psychological patterns that would benefit people suffering from neurological disorders. In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset and present experimental studies to identify six emotional parameters using brain signals by experimenting the neuroheadset among three human subjects.Advisors(s): Prof. Maria Valero Prof. Hossain ShahriarTopic(s): Other (explain in the comments section

GC-250 Object Detection and Tracking: Deep Learning-based Framework with Euclidean Distance, IoU, and Hungarian Algorithm

Object tracking is an important basis for the logistics industry where multiple packages are moved on conveyor belts at a time. Accurate datasets and efficient benchmarks are a few of the several problems for both object detection and tracking for training the deep learning-based framework. Preparing 100% accurate correspondence between objects throughout different frames by assigning human annotated unique_attributes to train framework efficiently over ground truth data. In this research, we develop an (i) OpenCV-based framework that allows the user to assign human-annotated identification between objects and (ii) a novel application for object detection and tracking. We utilize the assigned attributes to train the deep learning model accurately and adopt various evaluation parameters including euclidean distance, intersection over union (IoU), and scale-invariant feature transform (SIFT) to measure the accuracy of an object correspondence or tracking. We also adopt the Hungarian algorithm to increase the efficiency in determining correspondences between objects and apply our framework to human-annotated ground truth datasets comprising ~1,000 images and the same amount of JSON files. Our demonstration achieved 94.53 % accuracy in object detection, finding correspondence, and object tracking. In future studies, we are aiming to apply a neural network to draw a comparison of identified accuracy

GR-100 - Non-Invasive Monitoring of Human Hygiene using Vibration Sensor and Classifiers

Abstract: Personal hygiene is how people take care of their bodies. Maintaining hygiene practice reduces the spread of illness and the risk of medical conditions. With the current pandemic situation, practices like washing hands and taking regular showers have taken major importance among people, especially for senior populations that live alone at home. Having an understanding of the human hygiene habits of our seniors is fundamental to monitoring health conditions.This research work presents the concept and idea of a noninvasive monitoring system for human hygiene using only vibration sensors. The approach is based on a geophone, a digitizer, and a cost-efficient computer board (raspberry pi). We capture the vibration of the water flow while people perform activities in the bathroom (open faucet, flush toilets) and kitchen (open kitchen sink). Results show that our approach can distinguish from these different activities with an accuracy higher than 90%. With this approach, we hope to start a new tendency of monitoring people activities without using cameras or other privacy-invasive methods.Department: Information TechnologySupervisor: Dr. Maria ValeroTopics: IoT/Cloud/Networkin

Students Certification Management (SCM): Hyperledger Fabric-Based Digital Repository

The higher education sector has been heavily impacted financially by the economic downturn caused by the pandemic that has resulted a decline in student enrollments. Finding cost-effective novel technology for storing and sharing student\u27s credentials among academic institutions and potential employers is a demand. Within the current conventional approach, ensuring authentication of a candidate’s credentials is costly and time-consuming which gives burdens to thousands of prospective students and potential employees. As a result, candidates fail to secure opportunities for either delay or non-submission of credentials all over the world. Blockchain technology has the potential for students\u27 control over their credentials; degrees and transcripts for instance that will allow seamless streamlining of the sharing of educational records during changing and transferring schools, higher education, or even employment processes when need to show credentials. To implement the novel idea, we conduct a preliminary survey, study the existing applications, and investigate the feasibility of a Blockchain-based system to exploit the potential. Based on our findings, we propose a Students Certification Management System (SCM) by adopting Emerging Hyperledger Fabric that will offer a universal, tamper-evident, immutable, and secure educational certificate storing and sharing network. Our primary aim is to construct the proposed system into an educational certificate repository network using consortium blockchain for different entities including, (i) educational institutes to manage the network (ii) students and authorized third parties to access verifiable digital certificates and transcripts. Initially, we introduce an advanced architectural framework of the proposed system that has the potential in improving data flow between academic institutions, students, and potential employers. For ensuring transparency, each attempt in storing, sharing, and accessing credentials by the authenticated users within the proposed network shall be stored in the ledger which is secure and non-corruptible. Our future direction is to implement the architectural framework into an educational certification repository network within a private blockchain network

Machine Learning-Oriented Predictive Maintenance (PdM) Framework for Autonomous Vehicles (AVs): Adopting Blockchain for PdM Solution

Autonomous Vehicles (AVs) refers to smart, connected and multimedia cars with technological megatrends of the fourth industrial revolution (Industry 4.0) and have gained huge strive in today\u27s world. AVs adopt automated driving systems (ADS) technique that permits the vehicle to manage and control driving points without human drivers by utilizing advanced equipment including a combination of sensors, controllers, onboard computers, actuators, algorithms, and advanced software embedded in the different parts of the vehicle. These advanced sensors provide unique inputs to the ADS to generate a path from point A to point B. Ensuring the safety of sensors by limiting maintenance costs has become a major challenge for AVs community. The predictive maintenance (PdM) approach has the potential to address the AVs failures. In this paper, we propose a novel, conceptual, and high-level domain-specific software architecture for the machine learning-oriented predictive maintenance (PdM) framework that shall enable predicting early malfunctioning, quality, safety, and performance deficiencies of AVs. The novel framework collects the data from sensors and major equipment and stores the collected data in immutable and transparent blockchain technology. Collected data shall be validated, extracted, and classified by adopting machine learning (ML) techniques. ML module shall predict the possible malfunctioning of the sensors while providing potential solutions from the stored data in the blockchain network. In this paper, our effort was to conduct a feasibility study, elicit and specify all the requirements for the proposed framework. In future research, we aim to extend the conceptual work and implement a prototype in real-world scenarios

Analyzing Robotics Software Vulnerabilities

Robots are widely used in our day-to-day life in various domains. For example, eldercare robots, such as CareO-Bots [1]are used to perform household tasks and provide mobility assistance [2]. Amazon uses manufacturing robots to accomplish manufacturing labor activities, such as welding and assembling equipment [2]. According to the International Data Corporation, spending on robotics is expected to reach USD 241.4 billion by the end of 2023 [4]. However, malicious users can exploit security vulnerabilities in hardware and software components of robotics systems to conduct security attacks and cause malfunction, i.e., deviate robots from their expected behaviors. Security attacks on robots can have serious consequences such as (i) bottlenecks and shutdowns in the assembly line, (ii) disruption in the food supply chain, (iii) incorrect treatment for patients, and (iv) unwanted military attacks injuring or killing civilians and military personnel [2]. Researchers [3] have observed a lack of awareness amongst practitioners related to security issues that can exist in robotics systems. Using qualitative analysis, the project aims to determine the software vulnerabilities that commonly appear in robotics systems. In this work in progress, we plan to discuss our initial findings using Robotics Vulnerability Database (RVD) repositories [5] the following questions – (i) what are the most frequent security vulnerabilities in robotics systems? (ii) what types of components are affected by the vulnerabilities? (iii) what categories of vulnerabilities exist and severity for robotics systems

A Comparative Study on Blockchain-based Electronic Health Record Systems: Performance, Privacy, and Security Between Hyperledger Fabric and Ethereum Frameworks

Traditional data collection, storage, and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Addressing the challenges of conventional database techniques and improving the overall aspects of EHR application, blockchain technology is being evaluated to find a possible solution. Blockchain refers to an emerging distributed technology and incorruptible database of records or digital events which execute, validate, and maintain by a ledger technology to provide an immutable architecture and prevent records manipulation or alterations. However, there are multiple frameworks emerged in recent years where identifying the advantages and limitation is crucial. This thesis focuses on (i) introducing electronic health records systems using two widely used blockchain frameworks, Hyperledger Fabric and Ethereum. (ii)aims to provide a comparative study on both frameworks from the performance, privacy, and security perspectives. Based on two different introduced EHR systems, we identify the strength and weaknesses of both frameworks and present the challenges and limitations of these systems. According to a comparative study, the Hyperledger Fabric framework demonstrates advanced features including private and consortium networks that can facilitate EHR systems from both security and performance perspectives. Taking the experience into consideration, we aim to extend our study in software engineering domain to evaluate the limits to developing blockchain-based software applications and highlight the way to improve current SE practices in future studies