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Description
As we already know, fresh water is essential to human life as it sustains and replenishes our bodies. Water sustainability is clearly an important issue that need to be addressed in our world of growing demand and shrinking resources. The ASU Future H2O program seeks to make a difference in the development of water sustainability programs by performing experiments that convert urine into reusable water. The goal is to make reusable water processes become inexpensive and easily accessible to local businesses. This promises a significant environmental impact. In order to make the process of development more efficient we can combine engineering technology with scientific experimentation. As an engineering student and an advocate of water sustainability, I have a chance to design the front-end platform that will use IoT to make the experimental process more accessible and effective. In this paper, I will document the entire process involved in the designing process and what I have learned.
ContributorsTran, Phung Thien (Author) / Boscovic, Dragan (Thesis director) / Boyer, Treavor (Committee member) / School of Earth and Space Exploration (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Blockchain technology enables a distributed and decentralized environment without any central authority. Healthcare is one industry in which blockchain is expected to have significant impacts. In recent years, the Healthcare Information Exchange(HIE) has been shown to benefit the healthcare industry remarkably. It has been shown that blockchain could help to improve multiple aspects of the HIE system.
When Blockchain technology meets HIE, there are only a few proposed systems and they all suffer from the following two problems. First, the existing systems are not patient-centric in terms of data governance. Patients do not own their data and have no direct control over it. Second, there is no defined protocol among different systems on how to share sensitive data.
To address the issues mentioned above, this paper proposes MedFabric4Me, a blockchain-based platform for HIE. MedFabric4Me is a patient-centric system where patients own their healthcare data and share on a need-to-know basis. First, analyzed the requirements for a patient-centric system which ensures tamper-proof sharing of data among participants. Based on the analysis, a Merkle root based mechanism is created to ensure that data has not tampered. Second, a distributed Proxy re-encryption system is used for secure encryption of data during storage and sharing of records. Third, combining off-chain storage and on-chain access management for both authenticability and privacy.
MedFabric4Me is a two-pronged solution platform, composed of on-chain and off-chain components. The on-chain solution is implemented on the secure network of Hyperledger Fabric(HLF) while the off-chain solution uses Interplanetary File System(IPFS) to store data securely. Ethereum based Nucypher, a proxy re-encryption network provides cryptographic access controls to actors for encrypted data sharing.
To demonstrate the practicality and scalability, a prototype solution of MedFabric4Me is implemented and evaluated the performance measure of the system against an already implemented HIE.
Results show that decentralization technology like blockchain could help to mitigate some issues that HIE faces today, like transparency for patients, slow emergency response, and better access control.
Finally, this research concluded with the benefits and shortcomings of MedFabric4Me with some directions and work that could benefit MedFabric4Me in terms of operation and performance.
When Blockchain technology meets HIE, there are only a few proposed systems and they all suffer from the following two problems. First, the existing systems are not patient-centric in terms of data governance. Patients do not own their data and have no direct control over it. Second, there is no defined protocol among different systems on how to share sensitive data.
To address the issues mentioned above, this paper proposes MedFabric4Me, a blockchain-based platform for HIE. MedFabric4Me is a patient-centric system where patients own their healthcare data and share on a need-to-know basis. First, analyzed the requirements for a patient-centric system which ensures tamper-proof sharing of data among participants. Based on the analysis, a Merkle root based mechanism is created to ensure that data has not tampered. Second, a distributed Proxy re-encryption system is used for secure encryption of data during storage and sharing of records. Third, combining off-chain storage and on-chain access management for both authenticability and privacy.
MedFabric4Me is a two-pronged solution platform, composed of on-chain and off-chain components. The on-chain solution is implemented on the secure network of Hyperledger Fabric(HLF) while the off-chain solution uses Interplanetary File System(IPFS) to store data securely. Ethereum based Nucypher, a proxy re-encryption network provides cryptographic access controls to actors for encrypted data sharing.
To demonstrate the practicality and scalability, a prototype solution of MedFabric4Me is implemented and evaluated the performance measure of the system against an already implemented HIE.
Results show that decentralization technology like blockchain could help to mitigate some issues that HIE faces today, like transparency for patients, slow emergency response, and better access control.
Finally, this research concluded with the benefits and shortcomings of MedFabric4Me with some directions and work that could benefit MedFabric4Me in terms of operation and performance.
ContributorsVishnoi, Manish (Author) / Boscovic, Dragan (Thesis advisor) / Candan, Kasim S (Thesis advisor) / Grando, Maria (Committee member) / Arizona State University (Publisher)
Created2020
Description
The use of spatial data has become very fundamental in today's world. Ranging from fitness trackers to food delivery services, almost all application records users' location information and require clean geospatial data to enhance various application features. As spatial data flows in from heterogeneous sources various problems arise. The study of entity matching has been a fervent step in the process of producing clean usable data. Entity matching is an amalgamation of various sub-processes including blocking and matching. At the end of an entity matching pipeline, we get deduplicated records of the same real-world entity. Identifying various mentions of the same real-world locations is known as spatial entity matching. While entity matching received significant interest in the field of relational entity matching, the same cannot be said about spatial entity matching. In this dissertation, I build an end-to-end Geospatial Entity Matching framework, GEM, exploring spatial entity matching from a novel perspective. In the current state-of-the-art systems spatial entity matching is only done on one type of geometrical data variant. Instead of confining to matching spatial entities of only point geometry type, I work on extending the boundaries of spatial entity matching to match the more generic polygon geometry entities as well. I propose a methodology to provide support for three entity matching scenarios across different geometrical data types: point X point, point X polygon, polygon X polygon. As mentioned above entity matching consists of various steps but blocking, feature vector creation, and classification are the core steps of the system. GEM comprises an efficient and lightweight blocking technique, GeoPrune, that uses the geohash encoding mechanism to prune away the obvious non-matching spatial entities. Geohashing is a technique to convert a point location coordinates to an alphanumeric code string. This technique proves to be very effective and swift for the blocking mechanism. I leverage the Apache Sedona engine to create the feature vectors. Apache Sedona is a spatial database management system that holds the capacity of processing spatial SQL queries with multiple geometry types without compromising on their original coordinate vector representation. In this step, I re-purpose the spatial proximity operators (SQL queries) in Apache Sedona to create spatial feature dimensions that capture the proximity between a geospatial entity pair. The last step of an entity matching process is matching or classification. The classification step in GEM is a pluggable component, which consumes the feature vector for a spatial entity pair and determines whether the geolocations match or not. The component provides 3 machine learning models that consume the same feature vector and provide a label for the test data based on the training. I conduct experiments with the three classifiers upon multiple large-scale geospatial datasets consisting of both spatial and relational attributes. Data considered for experiments arrives from heterogeneous sources and we pre-align its schema manually. GEM achieves an F-measure of 1.0 for a point X point dataset with 176k total pairs, which is 42% higher than a state-of-the-art spatial EM baseline. It achieves F-measures of 0.966 and 0.993 for the point X polygon dataset with 302M total pairs, and the polygon X polygon dataset with 16M total pairs respectively.
ContributorsShah, Setu Nilesh (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Blockchain, the technology behind the worldwide-known cryptocurrency Bitcoin, offers a new set of potential advantages and opportunities that various industries and institutions could use to enhance their processes. Although most research and development on blockchain has focused on applications for cryptocurrencies and the finance industry, relatively few analyses and assessments have been conducted on how it could provide tools to address social and environmental issues. This research, using interviews, literature review and examples of blockchain applications, explores how this technology can be employed to address sustainability issues under the framework of three UN Sustainable Development Goals: 2. Zero Hunger, 7. Affordable and Clean Energy, and 14. Life Below Water. The analysis shows that blockchain has the potential to support solutions to sustainability problems that need efficient traceability, trust, a unique ID, transparency, or a highly secure payment system. However, the technology should not be mistaken for a panacea for addressing sustainability issues in its current state because it is not yet mature and has not been sufficiently tested. Expansion of blockchain as an effective tool for helping solve sustainability challenges will require a greater understanding of the governance of blockchain, its scalability and its potential unintended consequences for the technology to become properly integrated into the decision-making progress.
ContributorsRomo, Maximiliano (Author) / Melnick, Robert (Contributor, Contributor) / Maynard, Andrew (Contributor) / Boscovic, Dragan (Contributor)
Created2019-04-17