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  • Blockchain based Proxy Re-Encryption Scheme for Secure IoT Data Sharing

    appsOther research product2019 Ireland English IEEE
    Authors: Manzoor, Ahsan; Liyanage, Madhusanka; Braeken, An; et al.;

    handle: 10197/11694

    The 2019 IEEE International Conference on Blockchain and Cryptocurrency, Seoul, South Korea, 14-17 May 2019 Data is central to the Internet of Things IoT ecosystem. Most of the current IoT systems are using centralized cloud-based data sharing systems. Involvement of such third-party service provider requires also trust from both sensor owner and sensor data user. Moreover, the fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain based proxy re-encryption scheme. The system stores the IoT data in a distributed cloud after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and the data user without the involvement of a trusted third party. It also uses an efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. The proposed system is implemented in an Ethereum based testbed to analyze the performance and security properties. Enterprise Ireland SECUREConnect, 6Genesis Flagship

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  • Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming

    appsOther research product2020 Ireland English
    Authors: Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; +2 Authors

    handle: 10197/12206

    The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre

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2 Research products

  • Blockchain based Proxy Re-Encryption Scheme for Secure IoT Data Sharing

    appsOther research product2019 Ireland English IEEE
    Authors: Manzoor, Ahsan; Liyanage, Madhusanka; Braeken, An; et al.;

    handle: 10197/11694

    The 2019 IEEE International Conference on Blockchain and Cryptocurrency, Seoul, South Korea, 14-17 May 2019 Data is central to the Internet of Things IoT ecosystem. Most of the current IoT systems are using centralized cloud-based data sharing systems. Involvement of such third-party service provider requires also trust from both sensor owner and sensor data user. Moreover, the fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain based proxy re-encryption scheme. The system stores the IoT data in a distributed cloud after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and the data user without the involvement of a trusted third party. It also uses an efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. The proposed system is implemented in an Ethereum based testbed to analyze the performance and security properties. Enterprise Ireland SECUREConnect, 6Genesis Flagship

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    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
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  • Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming

    appsOther research product2020 Ireland English
    Authors: Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; +2 Authors

    handle: 10197/12206

    The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre

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    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
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