The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform

Domenico Scotece - One of the best experts on this subject based on the ideXlab platform.

  • ISCC - A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Paolo Bellavista, Marianna Paradisioti, Periklis Chatzimisios, Luca Foschini, Domenico Scotece
    Abstract:

    Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our Architectural Proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.

Paolo Bellavista - One of the best experts on this subject based on the ideXlab platform.

  • ISCC - A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Paolo Bellavista, Marianna Paradisioti, Periklis Chatzimisios, Luca Foschini, Domenico Scotece
    Abstract:

    Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our Architectural Proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.

Massimiliano Rak - One of the best experts on this subject based on the ideXlab platform.

  • interoperable grid pkis among untrusted domains an Architectural Proposal
    Grid and Pervasive Computing, 2007
    Co-Authors: Valentina Casola, Jesus Luna, Oscar Manso, Nicola Mazzocca, Manel Medina, Massimiliano Rak
    Abstract:

    In the last years several Grid Virtual Organizations -VOs- have been proliferating, each one usually installing its own Certification Authority and thus giving birth to a large set of different and possibly untrusted security domains. Nevertheless, despite the fact that the adoption of Grid Certification Authorities (CAs) has partially solved the problem of identification and authentication between the involved parties, and that Public Key Infrastructure (PKI) technologies are mature enough, we cannot make the same assumptions when untrusted domains are involved. In this paper we propose an architecture to face the problem of secure interoperability among untrusted Grid-domains. Our approach is based on building a dynamic federation of CAs, formed thorough the quantitative and automatic evaluation of their Certificate Policies. In this paper we describe the proposed architecture and its integration into Globus Toolkit 4.

  • GPC - Interoperable grid PKIs among untrusted domains: an Architectural Proposal
    Advances in Grid and Pervasive Computing, 1
    Co-Authors: Valentina Casola, Jesus Luna, Oscar Manso, Nicola Mazzocca, Manel Medina, Massimiliano Rak
    Abstract:

    In the last years several Grid Virtual Organizations -VOs- have been proliferating, each one usually installing its own Certification Authority and thus giving birth to a large set of different and possibly untrusted security domains. Nevertheless, despite the fact that the adoption of Grid Certification Authorities (CAs) has partially solved the problem of identification and authentication between the involved parties, and that Public Key Infrastructure (PKI) technologies are mature enough, we cannot make the same assumptions when untrusted domains are involved. In this paper we propose an architecture to face the problem of secure interoperability among untrusted Grid-domains. Our approach is based on building a dynamic federation of CAs, formed thorough the quantitative and automatic evaluation of their Certificate Policies. In this paper we describe the proposed architecture and its integration into Globus Toolkit 4.

Luca Foschini - One of the best experts on this subject based on the ideXlab platform.

  • ISCC - A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Paolo Bellavista, Marianna Paradisioti, Periklis Chatzimisios, Luca Foschini, Domenico Scotece
    Abstract:

    Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our Architectural Proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.

Marianna Paradisioti - One of the best experts on this subject based on the ideXlab platform.

  • ISCC - A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance
    2019 IEEE Symposium on Computers and Communications (ISCC), 2019
    Co-Authors: Paolo Bellavista, Marianna Paradisioti, Periklis Chatzimisios, Luca Foschini, Domenico Scotece
    Abstract:

    Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our Architectural Proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.