over budget

SuBChain: Subsea Data Ledger

$19,992.00 Requested
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Community Review Results (1 reviewers)
Addresses Challenge
Feasibility
Auditability
Problem:

<p>Developers lack access to proper subsea survey data to train ML algorithms. A shared ledger of autonomous contributors is the solution!</p>

Yes Votes:
₳ 97,130,410
No Votes:
₳ 60,577,866
Votes Cast:
609

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Detailed Plan

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<u>Why do we need to enhance data sharing of subsea inspection images and videos?</u>

Most of the companies and entrepreneurs working in the subsea Inspection, Maintenance and Repair (IMR) field have been struggling with proper access to subsea images and videos for years and years. Still today, there is no database compiling this data at a worldwide basis and a huge part of interesting subsea images and videos remains unused, stored in hard disks inside forgotten dusty warehouses, with no further use possible.

Based on recent industry trends of robotics, artificial intelligence and machine learning, autonomous vehicles, internet of things, etc., many companies, research centers, universities, and entrepreneurs have been developing innovative data-processing tools to enhance their product developments. These players have been looking forward to better understanding how to combine different technologies (i.e., computer vision, machine leaning, blockchain, etc.) with their underwater robots working in the Oil & Gas and Offshore Wind Energy markets, where the need to reduce operating expenditures continues to challenge owners and operators to improve efficiencies and enhance safety in maritime operations.

Many of the players mentioned above are currently developing their own AI-based image recognition tools to enable deeper insight on offshore assets integrity condition. For that purpose, they intend to use their Remote Operated Vehicles (ROVs) to effectively collect, process, and perform advanced data analytics, enabling data-driven decision-making tasks on site. For instance, the automatic detection of key features and/or anomalies could use Machine Learning / Deep Learning algorithms trained by relevant data in thousands of hours of survey videos, which unfortunately are not available yet at a global basis. Traditional offshore Oil & Gas assets' structures are somehow standardized (particularly in offshore wind farms where all turbines have the same structure), presenting enough regularity to allow patterns to be learned by the ML algorithms.

Fortune Business Insights has estimated that the subsea Inspection, Maintenance and Repair (IMR) market size was USD 7.19 billion in 2018 and is projected to reach USD 17.33 billion by 2026, with decommissioning, revitalization works, and increasing focus on unmanned vehicles being the major drives for market growth ( <https://www.fortunebusinessinsights.com/industry-reports/offshore-inspection-repair-maintenance-market-100405> ). Based on the afore-mentioned gap identified in this subsea market, we are proposing herein a Blockchain distributed ledger which has the potential to become the new solution for a worldwide decentralized subsea data network of underwater inspection players mutually contributing to one another.

<u>Why using blockchain?</u>

Most of the classic authentication methods and centralized security mechanisms used in shared databases require a trusted third-party, whose systems sometimes lack security in design, are not able to defend against cyberattacks, face resource constraints, etc. Besides that, ensuring that data transmitted across networks are kept secure and private without high computational cost remains a challenge. The current settlement and reconciliation processes involved in these data transactions could be solved using Blockchain technology, which will impact the cost of verification of these transactions in the distributed ledger proposed herein.

Blockchain will facilitate decentralized storage for recording underwater inspection data and secure processing and sharing of this data among different entities. Blockchain technology is the ideal solution for this application which require decentralized access control, storage, and distributed trust. Blockchain will provide support in terms of data security and integrity. The secure routing of underwater inspection imaging data through the hierarchical topology of the Blockchain platform will ensure the legitimacy of all data sources to all platform participants, allowing them to use this data at their own interest and benefits, as previously explained.

<u>SuBChain, a Distributed Subsea Data Ledger</u>

SuBChain aims at becoming the standard Blockchain solution for a leveraged distributed, decentralized peer-to-peer network to enable subsea inspection data to be shared and stored securely and inexpensively without relying on any intermediary authorities, while still ensuring data privacy.

The participants of this Blockchain platform will be processing and validating underwater inspection data prior to confirming the inclusion of this data to the system via consensus mechanism, eliminating the need of intermediaries' involvement to process this data, preventing fraudulent activities, and ensuring data immutability, transparency, and operational resilience of the ledger by leveraging public key infrastructure to authenticate, authorize entities, and encrypt subsea data records.

Tokenization will help bootstrap the network by providing platform users with the incentives to contribute their resources to build and sustain it. The role of the Tokens will be played in incentivizing the growth, operations, and security of the platform, by helping it expand its user base and honestly reward underwater inspection professionals who contribute to the ledger with their digital assets.

Some activities that could be encouraged via Tokens are constructive feedback on the inspection videos (with some monitoring to avoid "robot" users generating automatic activities to get those rewards), referrals of the platform to other professionals, etc. For example, writing positive reviews about the platform and freely marketing it to others will help attract more users and boost the platform's relevance.

The Tokenization should adopt a smart user-rewarding strategy. For example, independent professionals and operators can upload their data to the platform and get token-rewarded based on the number of times their videos are used by other users. Tokens can be assigned to the users each time their video is used or downloaded and then they can later redeem their tokens for money, thereby building a reputation system within the platform.

-> A more detailed version of this proposal can be checked in the attachments.

<u>Development Roadmap</u>

Phase 1 - Proof of Concept (PoC) – Nov/2021

  • Applying for Fund6
  • Subcontracting external developers to help us build the PoC as a prototype with all user personas: Admin, Users, Employees, etc. This PoC will be a web page that will in the future be connected to Cardano. Estimated delivery: Apr/2022.

Phase 2 - Complete Solution Build – May/2022

  • Demo the PoC to internal parties, collect comments and build a first MVP.
  • Start attaching the whole ecosystem onto Cardano.

Phase 2 will deliver a functional MVP that can then be used to demonstrate the PoV and attract investors and potential partners. Estimated delivery: Ago/2022.

Phase 3 – Final Setup – Sep/2022

  • Finish transferring the web page onto Cardano by Sep/2022
  • Make UX improvements and UI advanced design
  • Perform Quality Assurance (QA)

<u>Milestones</u>

  • <u>6 months after Funded</u> (from Nov/2021 to Apr/2022): After six months, the PoC is successfully validated and ready to start being integrated to the Cardano blockchain.
  • <u>10 months after Funded</u> (from May to Ago/2022): The first MVP will be launched by Jul/2022, allowing it to be stress-tested for real use.
  • <u>12 months after Funded</u> (from Sep to Oct/2022): The Distributed Subsea Data Ledger ("SuBChain") will start getting traction and growing adoption via a Beta version launched to a small set of users inside the Cardano community.

<u>KPIs</u>

  • Number of user subscribed to the wait list
  • Number of active users after the platform is launched
  • Number of transactions after the platform is launched
  • Revenue generated via transactions

<u>The Team</u>

  • Rodrigo Castro is an Electrical Engineer from the O&G and Energy industry with +15 years' experience in R&D, Disruptive Innovation, Business Analytics, Artificial Intelligence, and Project & Operations Management. He has worked for leading players of the offshore engineering market, like TechnipFMC and SBM Offshore, and on subsea robotics companies like Cybernetix and Forssea Robotics. Rodrigo has a Master in Oil & Gas & Energy, an MBA in Business Analytics (having travelled the world as a Global Ambassador for HULT International Business School (Boston / Dubai / London), and extension courses at MIT Sloan School of Management (MIT Computer Science & Artificial Intelligence Laboratory (CSAIL)) focused on AI and its implications for Business Strategy, Applied Business Analytics, Blockchain Technologies for Business Innovation and Application, and Implementation of Industry 4.0 for Leading Change in Manufacturing and Operation. His full work experience and educational background can be accessed here:
    <https://www.linkedin.com/in/rodrigo-castro/>
  • Victor Corcino is a Chemical Engineer having worked in the O&G industry for the past 7+ years in companies like Braskem and TechnipFMC, with solid background in detailed engineering, R&D, Life of Field and conceptual design projects. He holds a Master in Chemical and Biochemical Processes Engineering, in which he has worked with Computational Simulation applied to O&G equipment. He is a member of the Flow Assurance Technical Committee at SPE Brazil Section, supporting the organization of events, knowledge sharing, national networking improvement and expertise exchange between Academy and Industry. Victor has also completed courses in Artificial Intelligence, Data Science and Machine Learning, and he is currently taking a Doctor of Science degree in Computational Fluid Dynamics, Machine Learning and Uncertainty Quantification at the Federal University of Rio de Janeiro. His full work experience and educational background can be accessed here:
    <https://www.linkedin.com/in/victorcorcino/>

We are also in the process of reaching out to developers for them to take charge of some of the developments that needs to be done in order to deliver this proposal, especially Haskell developers.

<u>Budget Breakdown</u>

  • Project Design: $1800
  • DApp architecture: $585
  • UI/UX, Frontend and Backend Development: $12330
  • Infrastructure: $500
  • Blockchain Integration: $585
  • Project Management: $3240
  • Contingency: $952

-> A more detailed version of the budget can be checked in this proposal's attachments.

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