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Author

Antorweep Chakravorty

Bio: Antorweep Chakravorty is an academic researcher from University of Stavanger. The author has contributed to research in topics: Big data & Smart grid. The author has an hindex of 9, co-authored 34 publications receiving 314 citations.

Papers
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Proceedings ArticleDOI
23 May 2013
TL;DR: An approach to achieve data security & privacy through out the complete data lifecycle: data generation/collection, transfer, storage, processing and sharing is proposed.
Abstract: A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle: data generation/collection, transfer, storage, processing and sharing.

91 citations

Proceedings ArticleDOI
05 Jan 2017
TL;DR: The potential for blockchain based solutions to disrupt the world of social networking is presented and a user centric blockchain supported social media network that enables users to control, trace and claim ownership of every piece of content they share is offered.
Abstract: This paper presents the potential for blockchain based solutions to disrupt the world of social networking. We offer Ushare, a user centric blockchain supported social media network that enables users to control, trace and claim ownership of every piece of content they share. Harnessing peer-to-peer capabilities of the blockchain technology allows a truly decentralized, secure, anonymous and traceable content distribution network. Ushare consists of four key components: the blockchain, a hash table with encrypted content shared by a user, a Turing complete relationship system to control the the maximum number of shares performed by user's circle members and a local personal certificate authority that manages the user's circles and encrypts data to be shared before it is broadcasted to the network.

75 citations

Journal ArticleDOI
31 May 2021-Sensors
TL;DR: In this paper, the authors present a unified blockchain-based system for energy asset transactions among prosumers, electric vehicles, power companies and storage providers, where assets encapsulating an identifier or unique information along with value are modeled as non-fungible tokens (NFT), while those representing value only are modelled as FT.
Abstract: Renewable energy microgeneration is rising leading to creation of prosumer communities making it possible to extract value from surplus energy and usage flexibility. Such a peer-to-peer energy trading community requires a decentralized, immutable and access-controlled transaction system for tokenized energy assets. In this study we present a unified blockchain-based system for energy asset transactions among prosumers, electric vehicles, power companies and storage providers. Two versions of the system were implemented on Hyperledger Fabric. Assets encapsulating an identifier or unique information along with value are modelled as non-fungible tokens (NFT), while those representing value only are modelled as fungible tokens (FT). We developed the associated algorithms for token lifecycle management, analyzed their complexities and encoded them in smart contracts for performance testing. The results show that performance of both implementations are comparable for most major operations. Further, we presented a detailed comparison of FT and NFT implementations based on use-case, design, performance, advantages and disadvantages. Our implementation achieved a throughput of 448.3 transactions per second for the slowest operation (transfer) with a reasonably low infrastructure.

36 citations

Journal ArticleDOI
10 Feb 2019-Sensors
TL;DR: An evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically and illustrates that the proposed method achieves better solutions on the considered scenarios.
Abstract: The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naive and simple predictors.

36 citations

Journal ArticleDOI
21 Apr 2020
TL;DR: It is demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables.
Abstract: Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.

34 citations


Cited by
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Proceedings ArticleDOI
13 Mar 2017
TL;DR: This paper shows that the proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability, and presents simulation results to highlight that the overheads are insignificant relative to its security and privacy gains.
Abstract: Internet of Things (IoT) security and privacy remain a major challenge, mainly due to the massive scale and distributed nature of IoT networks. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead that is not suitable for most resource-constrained IoT devices. In our previous work, we presented a lightweight instantiation of a BC particularly geared for use in IoT by eliminating the Proof of Work (POW) and the concept of coins. Our approach was exemplified in a smart home setting and consists of three main tiers namely: cloud storage, overlay, and smart home. In this paper we delve deeper and outline the various core components and functions of the smart home tier. Each smart home is equipped with an always online, high resource device, known as “miner” that is responsible for handling all communication within and external to the home. The miner also preserves a private and secure BC, used for controlling and auditing communications. We show that our proposed BC-based smart home framework is secure by thoroughly analysing its security with respect to the fundamental security goals of confidentiality, integrity, and availability. Finally, we present simulation results to highlight that the overheads (in terms of traffic, processing time and energy consumption) introduced by our approach are insignificant relative to its security and privacy gains.

1,340 citations

Journal ArticleDOI
TL;DR: A comprehensive classification of blockchain-enabled applications across diverse sectors such as supply chain, business, healthcare, IoT, privacy, and data management is presented, and key themes, trends and emerging areas for research are established.

1,310 citations

Book ChapterDOI
20 Dec 2013

780 citations

Journal ArticleDOI
TL;DR: This paper presents a model of the outward transmission of vehicle blockchain data, and gives detail theoretical analysis and numerical results that have shown the potential to guide the application of blockchain for future vehicle networking.
Abstract: The rapid growth of Internet of Vehicles (IoV) has brought huge challenges for large data storage, intelligent management, and information security for the entire system. The traditional centralized management approach for IoV faces the difficulty in dealing with real-time response. The blockchain, as an effective technology for decentralized distributed storage and security management, has already showed great advantages in its application of Bitcoin. In this paper, we investigate how the blockchain technology could be extended to the application of vehicle networking, especially with the consideration of the distributed and secure storage of big data. We define several types of nodes such as vehicle and roadside for vehicle networks and form several sub-blockchain networks. In this paper, we present a model of the outward transmission of vehicle blockchain data, and then give detail theoretical analysis and numerical results. This paper has shown the potential to guide the application of blockchain for future vehicle networking.

286 citations