The Experts below are selected from a list of 21036 Experts worldwide ranked by ideXlab platform

Hoi-kwong Lo - One of the best experts on this subject based on the ideXlab platform.

  • classical Communication Cost in distributed quantum information processing a generalization of quantum Communication complexity
    Physical Review A, 2000
    Co-Authors: Hoi-kwong Lo
    Abstract:

    We study the amount of classical Communication needed for distributed quantum information processing. In particular, we introduce the concept of "remote preparation" of a quantum state. Given an ensemble of states, Alice's task is to help Bob in a distant laboratory to prepare a state of her choice. We find several examples of an ensemble with an entropy S where the remote preparation can be done with a Communication Cost lower than the amount (2S) required by standard teleportation. We conjecture that, for an arbitrary N-dimensional pure state, its remote preparation requires 2log_2 N bits of classical Communication, as in standard teleportation.

  • The classical Communication Cost of entanglement manipulation: Is entanglement an inter-convertible resource?
    Physical Review Letters, 1999
    Co-Authors: Hoi-kwong Lo, Sandu Popescu
    Abstract:

    Entanglement bits or ``ebits'' have been proposed as a quantitative measure of a fundamental resource in quantum information processing. For such an interpretation to be valid, it is important to show that the same number of ebits in different forms or concentrations are inter-convertible in the asymptotic limit. Here we draw attention to a very important but hitherto unnoticed aspect of entanglement manipulation --- the classical Communication Cost. We construct an explicit procedure which demonstrates that for bi-partite pure states, in the asymptotic limit, entanglement can be concentrated or diluted with vanishing classical Communication Cost. Entanglement of bi-partite pure states is thus established as a truly inter-convertible resource.

Samir Khuller - One of the best experts on this subject based on the ideXlab platform.

  • minimizing Communication Cost in distributed multi query processing
    International Conference on Data Engineering, 2009
    Co-Authors: Jian Li, Amol Deshpande, Samir Khuller
    Abstract:

    Increasing prevalence of large-scale distributed monitoring and computing environments such as sensor networks, scientific federations, Grids etc., has led to a renewed interest in the area of distributed query processing and optimization. In this paper we address a general, distributed multi-query processing problem motivated by the need to minimize the Communication Cost in these environments. Specifically we address the problem of optimally sharing data movement across the Communication edges in a distributed Communication network given a set of overlapping queries and query plans for them (specifying the operations to be executed). Most of the problem variations of our general problem can be shown to be NP-Hard by a reduction from the Steiner tree problem. However, we show that the problem can be solved optimally if the Communication network is a tree, and present a novel algorithm for finding an optimal data movement plan. For general Communication networks, we present efficient approximation algorithms for several variations of the problem. Finally, we present an experimental study over synthetic datasets showing both the need for exploiting the sharing of data movement and the effectiveness of our algorithms at finding such plans.

  • ICDE - Minimizing Communication Cost in Distributed Multi-query Processing
    2009 IEEE 25th International Conference on Data Engineering, 2009
    Co-Authors: Jian Li, Amol Deshpande, Samir Khuller
    Abstract:

    Increasing prevalence of large-scale distributed monitoring and computing environments such as sensor networks, scientific federations, Grids etc., has led to a renewed interest in the area of distributed query processing and optimization. In this paper we address a general, distributed multi-query processing problem motivated by the need to minimize the Communication Cost in these environments. Specifically we address the problem of optimally sharing data movement across the Communication edges in a distributed Communication network given a set of overlapping queries and query plans for them (specifying the operations to be executed). Most of the problem variations of our general problem can be shown to be NP-Hard by a reduction from the Steiner tree problem. However, we show that the problem can be solved optimally if the Communication network is a tree, and present a novel algorithm for finding an optimal data movement plan. For general Communication networks, we present efficient approximation algorithms for several variations of the problem. Finally, we present an experimental study over synthetic datasets showing both the need for exploiting the sharing of data movement and the effectiveness of our algorithms at finding such plans.

H. T. Kung - One of the best experts on this subject based on the ideXlab platform.

  • Scaling network-based spectrum analyzer with constant Communication Cost
    Proceedings - IEEE INFOCOM, 2013
    Co-Authors: Youngjune Gwon, H. T. Kung
    Abstract:

    We propose a spectrum analyzer that leverages many networked commodity sensor nodes, each of which samples its portion in a wideband spectrum. The sensors operate in parallel and transmit their measurements over a wireless network without performing any significant computations such as FFT. The measurements are forwarded to the backend of the system where spectrum analysis takes place. In particular, we propose a solution that compresses the raw measurements in a simple random linear projection and combines the compressed measurements from multiple sensors in-network. As a result, we achieve a substantial reduction in the network bandwidth requirement to operate the proposed system. We discover that the overall Communication Cost can be independent of the number of sensors and is affected only by sparsity of discretized spectrum under analysis. This principle founds the basis for a claim that our network-based spectrum analyzer can scale up the number of sensor nodes to process a very wide spectrum block potentially having a GHz bandwidth. We devise a novel recovery algorithm that systematically undoes compressive encoding and in-network combining done to the raw measurements, incorporating the least squares and I1-minimization decoding used in compressive sensing, and demonstrate that the algorithm can effectively restore an accurate estimate of the original data suitable for finegrained spectrum analysis. We present mathematical analysis and empirical evaluation of the system with software-defined radios.

  • Reducing Reconciliation Communication Cost with Compressed Sensing
    arXiv: Information Theory, 2012
    Co-Authors: H. T. Kung, Chia-mu Yu
    Abstract:

    We consider a reconciliation problem, where two hosts wish to synchronize their respective sets. Efficient solutions for minimizing the Communication Cost between the two hosts have been previously proposed in the literature. However, they rely on prior knowledge about the size of the set differences between the two sets to be reconciled. In this paper, we propose a method which can achieve comparable efficiency without assuming this prior knowledge. Our method uses compressive sensing techniques which can leverage the expected sparsity in set differences. We study the performance of the method via theoretical analysis and numerical simulations.

Chun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • on optimal Communication Cost for gathering correlated data through wireless sensor networks
    ACM IEEE International Conference on Mobile Computing and Networking, 2006
    Co-Authors: Micah Adler, Don Towsley, Chun Zhang
    Abstract:

    In many energy-constrained wireless sensor networks, nodes cooperatively forward correlated sensed data to data sinks. In order to reduce the Communication Cost (e.g.overall energy) used for data collection, previous works have focused on specific coding schemes, such as Slepian-Wolf Code or Explicit Entropy Code. However, the minimum Communication Cost under arbitrary coding/routing schemes has not yet been characterized. In this paper, we consider the problem of minimizing the total Communication Cost of a wireless sensor network with a single sink. We prove that the minimum Communication Cost can be achieved using Slepian-Wolf Code and Commodity Flow Routing when the link Communication Cost is a convex function of link data rate. Furthermore, we find it useful to introduce a new metric distance entropy, a generalization of entropy, to characterize the data collection limit of networked sources. When the energy consumption is proportional to the link data rate (e.g.normally in 802.11), we show that distance entropy provides a lower bound of the Communication Cost and can be achieved by using a specific rate SWC and shortest path routing. Theoretically, achieving optimality may require global knowledge of the data correlation structure, which may not be available in practice. Therefore, we propose a simple, hierarchical scheme that primarily exploits data correlation between local neighboring nodes. We show that for several correlation structures and topologies, the Communication Cost achieved by this scheme is within a constant factor of the distance entropy, i.e., it is asymptotically optimal. Finally, we simulate our algorithm using radar reffectivity data as well as traces from Gaussian Markov Fields (GMF). As the network size goes large, for the radar data, we find our algorithm saves two thirds of the Communication Cost compared to a non-coding approach; as for the GMF data, our algorithm converges to a constant factor (1 .5_1.8) the distance entropy.

  • MobiCom - On optimal Communication Cost for gathering correlated data through wireless sensor networks
    Proceedings of the 12th annual international conference on Mobile computing and networking - MobiCom '06, 2006
    Co-Authors: Micah Adler, Don Towsley, Chun Zhang
    Abstract:

    In many energy-constrained wireless sensor networks, nodes cooperatively forward correlated sensed data to data sinks. In order to reduce the Communication Cost (e.g.overall energy) used for data collection, previous works have focused on specific coding schemes, such as Slepian-Wolf Code or Explicit Entropy Code. However, the minimum Communication Cost under arbitrary coding/routing schemes has not yet been characterized. In this paper, we consider the problem of minimizing the total Communication Cost of a wireless sensor network with a single sink. We prove that the minimum Communication Cost can be achieved using Slepian-Wolf Code and Commodity Flow Routing when the link Communication Cost is a convex function of link data rate. Furthermore, we find it useful to introduce a new metric distance entropy, a generalization of entropy, to characterize the data collection limit of networked sources. When the energy consumption is proportional to the link data rate (e.g.normally in 802.11), we show that distance entropy provides a lower bound of the Communication Cost and can be achieved by using a specific rate SWC and shortest path routing. Theoretically, achieving optimality may require global knowledge of the data correlation structure, which may not be available in practice. Therefore, we propose a simple, hierarchical scheme that primarily exploits data correlation between local neighboring nodes. We show that for several correlation structures and topologies, the Communication Cost achieved by this scheme is within a constant factor of the distance entropy, i.e., it is asymptotically optimal. Finally, we simulate our algorithm using radar reffectivity data as well as traces from Gaussian Markov Fields (GMF). As the network size goes large, for the radar data, we find our algorithm saves two thirds of the Communication Cost compared to a non-coding approach; as for the GMF data, our algorithm converges to a constant factor (1 .5_1.8) the distance entropy.

Jian Li - One of the best experts on this subject based on the ideXlab platform.

  • minimizing Communication Cost in distributed multi query processing
    International Conference on Data Engineering, 2009
    Co-Authors: Jian Li, Amol Deshpande, Samir Khuller
    Abstract:

    Increasing prevalence of large-scale distributed monitoring and computing environments such as sensor networks, scientific federations, Grids etc., has led to a renewed interest in the area of distributed query processing and optimization. In this paper we address a general, distributed multi-query processing problem motivated by the need to minimize the Communication Cost in these environments. Specifically we address the problem of optimally sharing data movement across the Communication edges in a distributed Communication network given a set of overlapping queries and query plans for them (specifying the operations to be executed). Most of the problem variations of our general problem can be shown to be NP-Hard by a reduction from the Steiner tree problem. However, we show that the problem can be solved optimally if the Communication network is a tree, and present a novel algorithm for finding an optimal data movement plan. For general Communication networks, we present efficient approximation algorithms for several variations of the problem. Finally, we present an experimental study over synthetic datasets showing both the need for exploiting the sharing of data movement and the effectiveness of our algorithms at finding such plans.

  • ICDE - Minimizing Communication Cost in Distributed Multi-query Processing
    2009 IEEE 25th International Conference on Data Engineering, 2009
    Co-Authors: Jian Li, Amol Deshpande, Samir Khuller
    Abstract:

    Increasing prevalence of large-scale distributed monitoring and computing environments such as sensor networks, scientific federations, Grids etc., has led to a renewed interest in the area of distributed query processing and optimization. In this paper we address a general, distributed multi-query processing problem motivated by the need to minimize the Communication Cost in these environments. Specifically we address the problem of optimally sharing data movement across the Communication edges in a distributed Communication network given a set of overlapping queries and query plans for them (specifying the operations to be executed). Most of the problem variations of our general problem can be shown to be NP-Hard by a reduction from the Steiner tree problem. However, we show that the problem can be solved optimally if the Communication network is a tree, and present a novel algorithm for finding an optimal data movement plan. For general Communication networks, we present efficient approximation algorithms for several variations of the problem. Finally, we present an experimental study over synthetic datasets showing both the need for exploiting the sharing of data movement and the effectiveness of our algorithms at finding such plans.