The Experts below are selected from a list of 288912 Experts worldwide ranked by ideXlab platform

S Barbarossa - One of the best experts on this subject based on the ideXlab platform.

  • dynamic resource optimization for decentralized estimation in energy harvesting iot networks
    arXiv: Signal Processing, 2020
    Co-Authors: Claudio Battiloro, Paolo Di Lorenzo, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed Operating Level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios.

  • dynamic resource optimization for decentralized signal estimation in energy harvesting wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Paolo Di Lorenzo, Claudio Battiloro, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed Operating Level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

Claudio Battiloro - One of the best experts on this subject based on the ideXlab platform.

  • dynamic resource optimization for decentralized estimation in energy harvesting iot networks
    arXiv: Signal Processing, 2020
    Co-Authors: Claudio Battiloro, Paolo Di Lorenzo, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed Operating Level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios.

  • dynamic resource optimization for decentralized signal estimation in energy harvesting wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Paolo Di Lorenzo, Claudio Battiloro, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed Operating Level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

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

  • dynamic resource optimization for decentralized estimation in energy harvesting iot networks
    arXiv: Signal Processing, 2020
    Co-Authors: Claudio Battiloro, Paolo Di Lorenzo, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed Operating Level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios.

  • dynamic resource optimization for decentralized signal estimation in energy harvesting wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Paolo Di Lorenzo, Claudio Battiloro, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed Operating Level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

Radu Teodorescu - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Reduction of Voltage Margins by Leveraging On-chip ECC in Itanium II Processors ∗
    2014
    Co-Authors: Anys Bacha, Radu Teodorescu
    Abstract:

    Lowering supply voltage is one of the most effective approaches for improving the energy efficiency of microprocessors. Unfortunately, technology limitations, such as process variability and circuit aging, are forcing microprocessor designers to add larger voltage guardbands to their chips. This makes supply voltage increasingly difficult to scale with technology. This paper presents a new mechanism for dynamically reducing voltage margins while maintaining the chip Operating frequency constant. Unlike previous approaches that rely on special hardware to detect and recover from timing violations caused by low-voltage execution, our solution is firmware-based and does not require additional hardware. Instead, it relies on error correction mechanisms already built into modern processors. The system dynamically reduces voltage margins and uses correctable error reports raised by the hardware to identify the lowest, safe Operating voltage. The solution adapts to core-to-core variability by tailoring supply voltage to each core’s safe Operating Level. In addition, it exploits variability in workload vulnerability to low voltage execution. The system was prototyped on an HP Integrity Server that uses Intel’s Itanium 9560 processors. Evaluation using SPECjbb2005 and SPEC CPU2000 workloads shows core power savings ranging from 18 % to 23%, with minimal performance impact. 1

  • dynamic reduction of voltage margins by leveraging on chip ecc in itanium ii processors
    International Symposium on Computer Architecture, 2013
    Co-Authors: Anys Bacha, Radu Teodorescu
    Abstract:

    Lowering supply voltage is one of the most effective approaches for improving the energy efficiency of microprocessors. Unfortunately, technology limitations, such as process variability and circuit aging, are forcing microprocessor designers to add larger voltage guardbands to their chips. This makes supply voltage increasingly difficult to scale with technology. This paper presents a new mechanism for dynamically reducing voltage margins while maintaining the chip Operating frequency constant. Unlike previous approaches that rely on special hardware to detect and recover from timing violations caused by low-voltage execution, our solution is firmware-based and does not require additional hardware. Instead, it relies on error correction mechanisms already built into modern processors. The system dynamically reduces voltage margins and uses correctable error reports raised by the hardware to identify the lowest, safe Operating voltage. The solution adapts to core-to-core variability by tailoring supply voltage to each core's safe Operating Level. In addition, it exploits variability in workload vulnerability to low voltage execution. The system was prototyped on an HP Integrity Server that uses Intel's Itanium 9560 processors. Evaluation using SPECjbb2005 and SPEC CPU2000 workloads shows core power savings ranging from 18% to 23%, with minimal performance impact.

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

  • dynamic resource optimization for decentralized estimation in energy harvesting iot networks
    arXiv: Signal Processing, 2020
    Co-Authors: Claudio Battiloro, Paolo Di Lorenzo, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed Operating Level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios.

  • dynamic resource optimization for decentralized signal estimation in energy harvesting wireless sensor networks
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Paolo Di Lorenzo, Claudio Battiloro, Paolo Banelli, S Barbarossa
    Abstract:

    We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed Operating Level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.