Publications tagged with Big data

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Publications tagged with "Big data"

  1. A WSN Energy-Aware approach for air pollution monitoring in waste treatment facility site: A case study for landfill monitoring odour
    Campanile, L., Iacono, M., Lotito, R., & Mastroianni, M. (2020). A WSN Energy-Aware approach for air pollution monitoring in waste treatment facility site: A case study for landfill monitoring odour [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 526–532. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089477488&partnerID=40&md5=13ea9ca38f15c5b885ef7e501067010c
    Abstract
    The gaseous emissions derived from industrial plants are generally subject to a strictly program of monitoring, both continuous or one-spot, in order to comply with the limits imposed by the permitting license. Nowadays the problem of odour emission, and the consequently nuisance generated to the nearest receptors, has acquired importance so that is frequently asked a specific implementation of the air pollution monitoring program. In this paper we studied the case study of a generic landfill for the implementation of the odour monitoring system and time-specific use of air pollution control technology. The off-site monitoring is based on the deployment of electronic nose as part of a specifically built WSN system. The nodes outside the landfill boundary do not act as a continuously monitoring stations but as sensors activated when specific conditions, inside and outside the landfill, are achieved. The WSN is then organized on an energy-aware approach so to prolong the lifetime of the entire system, with significant cost-benefit advancement, and produce a monitoring-structure that can answer to specific input like threshold overshooting. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
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  2. Privacy regulations challenges on data-centric and iot systems: A case study for smart vehicles
    Campanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Privacy regulations challenges on data-centric and iot systems: A case study for smart vehicles [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 507–520. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089476036&partnerID=40&md5=c18dd73c221ec312a330521bf03d332e
    Abstract
    Internet of Things (IoTs) services and data-centric systems allow smart and efficient information exchanging. Anyway, even if existing IoTs and cyber security architectures are enforcing, they are still vulnerable to security issues, as unauthorized access, data breaches, intrusions. They can’t provide yet sufficiently robust and secure solutions to be applied in a straightforward way, both for ensuring privacy preservation and trustworthiness of transmitted data, evenly preventing from its fraudulent and unauthorized usage. Such data potentially include critical information about persons’ privacy (locations, visited places, behaviors, goods, anagraphic data and health conditions). So, novel approaches for IoTs and data-centric security are needed. In this work, we address IoTs systems security problem focusing on the privacy preserving issue. Indeed, after the European Union introduced the General Data Protection Regulation (GDPR), privacy data protection is a mandatory requirement for systems producing and managing sensible users’ data. Starting from a case study for the Internet of Vehicles (IoVs), we performed a pilot study and DPIA assessment to analyze possible mitigation strategies for improving the compliance of IoTs based systems to GDPR requirements. Our preliminary results evidenced that the introduction of blockchains in IoTs systems architectures can improve significantly the compliance to privacy regulations. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
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  3. Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating
    Mainenti, G., Campanile, L., Marulli, F., Ricciardi, C., & Valente, A. S. (2020). Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 533–540. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089519717&partnerID=40&md5=bf7cc36e86c1988dd85e04c2fce06de1
    Abstract
    In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
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