The Experts below are selected from a list of 23097 Experts worldwide ranked by ideXlab platform
Andreas Spanias - One of the best experts on this subject based on the ideXlab platform.
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max consensus in sensor networks non linear bounded transmission and additive noise
IEEE Sensors Journal, 2016Co-Authors: Sai Zhang, Cihan Tepedelenlioglu, Mahesh K Banavar, Andreas SpaniasAbstract:A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded Transmit Function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
Sai Zhang - One of the best experts on this subject based on the ideXlab platform.
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max consensus in sensor networks non linear bounded transmission and additive noise
IEEE Sensors Journal, 2016Co-Authors: Sai Zhang, Cihan Tepedelenlioglu, Mahesh K Banavar, Andreas SpaniasAbstract:A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded Transmit Function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
Cihan Tepedelenlioglu - One of the best experts on this subject based on the ideXlab platform.
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max consensus in sensor networks non linear bounded transmission and additive noise
IEEE Sensors Journal, 2016Co-Authors: Sai Zhang, Cihan Tepedelenlioglu, Mahesh K Banavar, Andreas SpaniasAbstract:A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded Transmit Function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
Mahesh K Banavar - One of the best experts on this subject based on the ideXlab platform.
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max consensus in sensor networks non linear bounded transmission and additive noise
IEEE Sensors Journal, 2016Co-Authors: Sai Zhang, Cihan Tepedelenlioglu, Mahesh K Banavar, Andreas SpaniasAbstract:A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded Transmit Function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
Phil Demar - One of the best experts on this subject based on the ideXlab platform.
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wirecap a novel packet capture engine for commodity nics in high speed networks
Internet Measurement Conference, 2014Co-Authors: Phil DemarAbstract:Packet capture is an essential Function for many network applications. However, packet drop is a major problem with packet capture in high-speed networks. This paper presents WireCAP, a novel packet capture engine for commodity network interface cards (NICs) in high-speed networks. WireCAP provides lossless zero-copy packet capture and delivery services by exploiting multi-queue NICs and multicore architectures. WireCAP introduces two new mechanisms-the ring-buffer-pool mechanism and the buddy-group-based offloading mechanism-to address the packet drop problem of packet capture in high-speed network. WireCAP is efficient. It also facilitates the design and operation of a user-space packet-processing application. Experiments have demonstrated that WireCAP achieves better packet capture performance when compared to existing packet capture engines. In addition, WireCAP implements a packet Transmit Function that allows captured packets to be forwarded, potentially after the packets are modified or inspected in flight. Therefore, WireCAP can be used to support middlebox-type applications. Thus, at a high level, WireCAP provides a new packet I/O framework for commodity NICs in high-speed networks.
