Distributed Function Computation Over a Rooted Directed Tree

Sefidgaran, Milad; Tchamkerten, Aslan

doi:10.1109/tit.2016.2530398

Cited by 24 publications

(35 citation statements)

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“…In [6], the function computation rate is defined and lower bounds on such rate are obtained for various simple classes of functions. Recently, in [65], informationtheoretic bounds on the function computation rate were ob- tained for a special case, when network is a directed rooted tree, and the set of sinks contains only the root of the tree, and the source symbols satisfy a certain Markov criterion. A number of works study computation of sum in the network.…”

Section: A Theoretical Aspects Of Nfc Layermentioning

confidence: 99%

CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

et al. 2016

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In forthcoming years, the Internet of Things (IoT) will connect billions of smart devices generating and uploading a deluge of data to the cloud. If successfully extracted, the knowledge buried in the data can significantly improve the quality of life and foster economic growth. However, a critical bottleneck for realising the efficient IoT is the pressure it puts on the existing communication infrastructures, requiring transfer of enormous data volumes. Aiming at addressing this problem, we propose a novel architecture dubbed Condense (reconfigurable knowledge acquisition systems), which integrates the IoT-communication infrastructure into data analysis. This is achieved via the generic concept of network function computation: Instead of merely transferring data from the IoT sources to the cloud, the communication infrastructure should actively participate in the data analysis by carefully designed en-route processing. We define the Condense architecture, its basic layers, and the interactions among its constituent modules. Further, from the implementation side, we describe how Condense can be integrated into the 3rd Generation Partnership Project (3GPP) Machine Type Communications (MTC) architecture, as well as the prospects of making it a practically viable technology in a short time frame, relying on Network Function Virtualization (NFV) and Software Defined Networking (SDN). Finally, from the theoretical side, we survey the relevant literature on computing "atomic" functions in both analog and digital domains, as well as on function decomposition over networks, highlighting challenges, insights, and future directions for exploiting these techniques within practical 3GPP MTC architecture.

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Section: A Theoretical Aspects Of Nfc Layermentioning

confidence: 99%

CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

et al. 2016

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Section: A Non-interactive Communication: Fundamental Limitsmentioning

confidence: 99%

“…In the case of lossless (in the Shannon sense) computation, it was shown that the minimum average rate is the conditional graph entropy of the characteristic graph of the source [17], [18]. Building upon this line of research, the rate region for the lossless DFC problem was characterized for certain problem instances [14], [15]. Sefidgaran et al derived inner and outer bounds to the rate region for a class of tree-structured networks (which includes the CEO problem) and showed that the inner and outer bound coincide with each other if the sources obey certain Markov properties [14].…”

Section: A Non-interactive Communication: Fundamental Limitsmentioning

confidence: 99%

“…Building upon this line of research, the rate region for the lossless DFC problem was characterized for certain problem instances [14], [15]. Sefidgaran et al derived inner and outer bounds to the rate region for a class of tree-structured networks (which includes the CEO problem) and showed that the inner and outer bound coincide with each other if the sources obey certain Markov properties [14]. Doshi et al gave the rate region for the DFC problem under a different constraint that they referred to as the "zig-zag" condition [15].…”

Section: A Non-interactive Communication: Fundamental Limitsmentioning

confidence: 99%

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Interactive Scalar Quantization for Distributed Resource Allocation

Boyle

Ren

Walsh

et al. 2016

IEEE Trans. Signal Process.

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In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremum of the distributed users' utilities. The overhead rate necessary to enable the controller to reproduce the users' local state can be prohibitively high. An approach to reduce this overhead is interactive communication wherein rate savings are achieved by tolerating an increase in delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstrate that tolerating a small increase in delay can yield significant rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the rate-delay trade-offs. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.Comment: 31 pages, 9 figures. Submitted on 2015-05-15 to IEEE Transactions on Signal Processing. Revised 2015-09-0

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“…It also gave an inner bound to the rate region of a general tree network. The rate region for a multi-stage tree-network when every source message at a vertex in the network satisfies a local Markovian property was characterized in [44]. The function computation scenarios described above all allowed for a vanishing block-error probability with asymptotically large block lengths.…”

Section: Introductionmentioning

confidence: 99%

Network coding for function computation

Tripathy¹

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Distributed Function Computation Over a Rooted Directed Tree

Cited by 24 publications

References 35 publications

CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT

Interactive Scalar Quantization for Distributed Resource Allocation

Network coding for function computation

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