IDF No 2675 Blockchain based Electricity Market Trading Platform

Blockchain based Electricity Market Trading Platform

Technology Category/Market

Technology: Blockchain based Electricity Market Trading Platform

Category: Blockchain

Industry: Trading Platform

Application: commercial sector

Market: The global market size estimated at USD 4.8 Billion in 2022 and is expected to hit around USD 69 Billion by 2032, poised to grow at a compound annual growth rate (CAGR) of 68% from 2023 to 2032.

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Problem Statement

  • Evolution of electric distribution networks has led to the rise of distributed energy resources, resulting in P2P trading concepts.
  • Blockchain technology is used to optimize power demand and supply in the electricity market.
  • Developments include a multi-energy P2P trading platform with seven layers, a PC-DA for order generation, and a blockchain-based decentralized electricity market trading platform.
  • Conventional systems lack a decentralized and distributed network approach, requiring a central aggregator for transactions.
  • Existing solutions are unreliable and ineffective, necessitating a blockchain-based decentralized electricity trading platform with reduced computational burden.

Technology

Decentralized Electricity Trading Platform Architecture:

  • Prosumers access blockchain network for peer-to-peer transactions.
  • Layers enable transaction based on consensus.
  • Provides a decentralized electricity trading platform.

The layers of the decentralized electricity trading platform includes

  • (i) A smart contract layer having decentralized self-executing smart contracts for receiving the real-time power demands and supply from one or more prosumer and is configured to perform one or more transaction,
  • (ii) A Physical and Optimization Layer (POL) in communication with the contract layer, to perform optimizations for reaching a consensus between prosumers
  • (iii) A monetary settlement layer (MSL) in communication with the digital contract layer to execute monetary transaction after execution of one or more smart contracts.

Key Features/Value Proposition

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Research Lab

Prof. Shanthi Swarup K

Department of Electrical Engineering

Intellectual Property

  • IITM IDF Ref. 2675

  • Application No: 202341070142

Technology Readiness Level

TRL- 3

Experimental proof of concept

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IDF No 2159 A System For Blockchain-based Micro-blogging And A Method Thereof

A System For Blockchain-based Micro-blogging And A Method Thereof

Categories for this Invention

Category- Blockchain

Industry Classification:

  • NIC (2008)- 6201- Computer programming activities ; 631- Data processing, hosting and related activities; web portals
  • NAICS (2022)- 516210 Media Streaming Distribution Services, Social Networks, and Other Media Networks and Content Providers
  • Applications- securely posting and managing content; digital advertising for targeting the right audience, efficient data management, data safety, and content management; smart contracts

  • Market drivers:

    The global blockchain technology market size is projected to grow from $27.84 billion in 2024 to $825.93 billion by 2032 with a CAGR of 52.8%.

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Problem Statement

  • The existing social networking systems comprise a centralized mechanism which configure service providers to be the sole authority to control user data and user content in the social networking platform. This raises concerns on the security and accessibility of user data.
  • Decentralized platforms models such as federated platforms and equal rights providing social networking platforms either do not devolve rights completely or face problems in data routing and storage.
  • There is a need for a highly secure system and method for publishing and managing content in a decentralized social networking platform, that is devoid of the problems mentioned above

Technology

  • The system comprises a distributed network of mobile devices wherein each mobile device comprises a client application such as a microblogging application over a blockchain node.
  • The blockchain network may comprise a light TCP/IP server suitable for mobile devices and a consensus algorithm such as Stellar Consensus Protocol (SCP).
  • Each mobile device functions as a blockchain node. Further, the blockchain node comprises the client application along with an application program interface (API) such as, REST API, a business logic module, a blockchain network , a consensus protocol and a database
  • The content regulation mechanism employs one or more federated machine learning techniques along with blockchain level consensus protocol to validate the user generated content, before publishing the user content in the microblogging platform.
  • Finally, based on the maximum type of consensus received from all the blockchain nodes a decision is made by the client application, to either publish or drop the user generated content.
  • A smart contract may be generated between user/blogger and the sponsor with essential conditions for rewards. When the essential conditions in the smart contract are met payment is activated from the sponsor’s wallet. These rewards may be claimed at physical stores using a smart card.

Key Features / Value Proposition

  • Embodiment, the decentralized microblogging platform may comprise the blockchain which may be a permissionless type, thereby ensuring rights for all the users in the blockchain network. Whereas, conventional decentralized federated models do not provide equal rights to users
  • Hence, each user is responsible for the storage and availability of their content/data. Advantageously, since the entire blockchain-based microblogging system is decentralized, there does not exist any single point of failure or data breach. Therefore, the broker-less system provides secure personal data and anonymity protection to the users’ content. Whereas, conventional social networking systems store data in centralized servers which if compromised can lead to mass privacy violations and data breaches.
  • The smart-contract based content monetization mechanism between sponsor and bloggers prevents any concerns associated with trust, manipulation, violation of contract, etc. Conversely, conventional platforms that rely on traditional contracts and legal systems for agreements between the creators and sponsor are unable to protect rights of small creators when the contract terms are breached due to expensive litigation and dispute resolution processes.

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Research Lab

Prof. Prabhu Rajagopal

Department of Mechanical Engineering

Intellectual Property

  • IITM IDF Ref.2159
  • IN 530474 Patent Granted

Technology Readiness Level

TRL 2

Technology Concept formulated

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IDF No 2297 Method for Image Reconstruction using Unsupervised Deep Learning and System thereof

Method for Image Reconstruction using Unsupervised Deep Learning and System thereof

Categories for this Invention

Technology: Image reconstruction using unsupervised deep learning techniques;

Industry & Application: Biomedical Engineering, Healthcare Industries, Magnetic Resonance Imaging(MRI) units, Medical Device;

Market: The global 3D reconstruction technology market is projected to grow at a CAGR of 11.6% during 2024-2029.

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Problem Statement

  • In the present era, various techniques like nuclear imaging, magnetic resonance imaging, computerized tomography scan which may be used to obtain images of internal structures of objects or patients.
  • However, these techniques subject to various trade-offs between speed, efficiency & quality of reconstruction.
  • Hence, there is a need to address said issues.

Technology

  • Present Invention explains about a system & method for image reconstruction using fully unsupervised deep learning techniques.
  • Further it explains that a system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions.. like a method for training a neural network for image reconstruction.
  • The method includes the following steps depicted in the smart chart hereinbelow:

STEP 1

  • Calculating loss function (L(X,X′)) & projection data error (X-X′) based on actual projection data (X) & reference reconstructed projection data (X′);

STEP 2

  • Updating one or more parameters of the neural network based on the calculated loss function (L(X,X′));

STEP 3

  • Reconstructing an image (Y′) by processing the actual projection data error (X-X′) using the one or more updated neural network parameters;

STEP 4

  • Transforming the reconstructed image (Y′) into new reference reconstructed projection data (X′);

STEP 5

  • Iteratively performing steps S1-S4 for a predefined number of cycle for calculating an optimum loss function (L(X,X′));

STEP 6

  • Generating one or more optimum parameters of the neural network using the optimum loss function (L(X,X′));

STEP 7

  • Updating the neural network with the one or more optimum parameters, & Transforming the reconstructed image (Y′) by processing the projection data (X) using a trained neural network.

Key Features / Value Proposition

Technical Perspective:

  • Facilitates a software framework for image reconstruction by combing the Deep Learning (DL) & the Iterative Reconstruction (IR) techniques.
  • Provide fast, fully unsupervised & robust image reconstruction technique.
  • Advantageous to reconstruct tomographic images without any noise/blur artifacts & allows reconstruction from the truncated data without the need for prior truncation correction.
  • The present techniques do not restrict the solution space by using regularization term in the loss function.

Industrial Perspective:

  • Efficient cost-effective solution and applicable in the medical imaging system to reconstruct the image.
  • Provide speedy solution.
  • Facilitates high quality of reconstructed image as shown in fig 2.
  • Easily installed on the system that in operation causes the system to perform the action of reconstruction of image.

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Research Lab

Prof. Balaji Srinivasan

Department of  Mechanical Engineering,

Prof. Ganapathy Krishnamurthi

Department of  Engineering Design

Intellectual Property

  • IITM IDF Ref. 2297

  • IN Patent No: 485152

Technology Readiness Level

TRL-4

Proof of Concept ready, tested in lab.

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