The Experts below are selected from a list of 4752 Experts worldwide ranked by ideXlab platform

Enrico Pontelli - One of the best experts on this subject based on the ideXlab platform.

  • AAMAS Workshops (Visionary Papers) - A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Autonomous Agents and Multiagent Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • a realistic dataset for the Smart Home Device scheduling problem for dcops
    Adaptive Agents and Multi-Agents Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.

Hanan Hibshi - One of the best experts on this subject based on the ideXlab platform.

  • ask the experts what should be on an iot privacy and security label
    IEEE Symposium on Security and Privacy, 2020
    Co-Authors: Pardis Emaminaeini, Yuvraj Agarwal, Lorrie Faith Cranor, Hanan Hibshi
    Abstract:

    Information about the privacy and security of Internet of Things (IoT) Devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT Devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers—a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts’ rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT Device (Smart Home Device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.

William Kluegel - One of the best experts on this subject based on the ideXlab platform.

  • AAMAS Workshops (Visionary Papers) - A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Autonomous Agents and Multiagent Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • a realistic dataset for the Smart Home Device scheduling problem for dcops
    Adaptive Agents and Multi-Agents Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.

Pardis Emaminaeini - One of the best experts on this subject based on the ideXlab platform.

  • ask the experts what should be on an iot privacy and security label
    IEEE Symposium on Security and Privacy, 2020
    Co-Authors: Pardis Emaminaeini, Yuvraj Agarwal, Lorrie Faith Cranor, Hanan Hibshi
    Abstract:

    Information about the privacy and security of Internet of Things (IoT) Devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT Devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers—a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts’ rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT Device (Smart Home Device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.

William Yeoh - One of the best experts on this subject based on the ideXlab platform.

  • AAMAS Workshops (Visionary Papers) - A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Autonomous Agents and Multiagent Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • a realistic dataset for the Smart Home Device scheduling problem for dcops
    Adaptive Agents and Multi-Agents Systems, 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.

  • A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: William Kluegel, Muhammad A Iqbal, William Yeoh, Ferdinando Fioretto, Enrico Pontelli
    Abstract:

    The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating Smart Devices schedules across multiple Homes as a multi-agent system, (ii) detail the physical models adopted to simulate Smart sensors, Smart actuators, and Homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.