Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Selected Areas in Communications, IEEE Journal on

Issue 4 • Date April 2013

Filter Results

Displaying Results 1 - 25 of 25
  • Table of contents

    Publication Year: 2013 , Page(s): c1 - c4
    Save to Project icon | Request Permissions | PDF file iconPDF (65 KB)  
    Freely Available from IEEE
  • Staff List

    Publication Year: 2013 , Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE
  • Guest Editorial: In-Network Computation: Exploring the Fundamental Limits

    Publication Year: 2013 , Page(s): 617 - 619
    Save to Project icon | Request Permissions | PDF file iconPDF (145 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Linear Function Computation in Networks: Duality and Constant Gap Results

    Publication Year: 2013 , Page(s): 620 - 638
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    In linear function computation, multiple source nodes communicate across a relay network to a single destination whose goal is to recover linear functions of the original source data. When the relay network is a linear deterministic network, a duality relation is established between function computation and broadcast with common messages. Using this relation, a compact sufficient condition is found describing those cases where the cut-set bound is tight. These insights are used to develop results for the case where the relay network contains Gaussian multiple-access channels. The proposed scheme decouples the physical and network layers. Using lattice codes for both source quantization and computation in the physical layer, the original Gaussian sources are converted into discrete sources and the Gaussian network into a linear deterministic network. Network codes for computing functions of discrete sources across the deterministic network are then found by applying the duality relation. The distortion for computing the sum of an arbitrary number of independent Gaussian sources over the Gaussian network is proven to be within a constant factor of the optimal performance. Furthermore, the constant factor results are extended to include asymmetric functions for the case of two sources. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal Computation of Symmetric Boolean Functions in Collocated Networks

    Publication Year: 2013 , Page(s): 639 - 654
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (557 KB) |  | HTML iconHTML  

    We consider collocated wireless sensor networks, where each node's transmissions can be heard by every other node. Each node has a Boolean measurement and the goal of the network is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We extend the results to the case of integer measurements and certain integer-valued functions. Next, we address the problem of minimizing the expected total number of bits that are transmitted when node measurements are random and drawn from independent Bernoulli distributions. In the case of computing a single instance of a Boolean threshold function, the problem reduces to one of determining the optimal order in which the nodes should transmit. We show that the optimal order of transmissions depends in an extremely simple way on the values of previously transmitted bits and the ordering of the marginal probabilities of the Boolean variables according to the k-th least likely rule: At any transmission, the node that transmits is the one that has the k-th least likely value of its Boolean variable, where k reduces by one whenever a node transmits a one. Initially the value of k is (n +1 - Threshold). Interestingly, the order of transmissions does not depend on the exact values of the probabilities of the Boolean variables. In the case of identically distributed measurements, we further show that the average-case complexity of block computation of a Boolean threshold function is O(θ), where θ is the threshold. We further show how to generalize to a pulse model of communication. One can also consider the related problem of approximate computati- n given a fixed number of bits. For the special case of the parity function, we show that the greedy strategy is optimal. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Communicating the Sum of Sources Over a Network

    Publication Year: 2013 , Page(s): 655 - 665
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (367 KB) |  | HTML iconHTML  

    We consider the network communication scenario, over directed acyclic networks with unit capacity edges in which a number of sources si each holding independent unit-entropy information Xi wish to communicate the sum ΣXi to a set of terminals tj. We show that in the case in which there are only two sources or only two terminals, communication is possible if and only if each source terminal pair si/tj is connected by at least a single path. For the more general communication problem in which there are three sources and three terminals, we prove that a single path connecting the source terminal pairs does not suffice to communicate ΣXi. We then present an efficient encoding scheme which enables the communication of ΣXi for the three sources, three terminals case, given that each source terminal pair is connected by two edge disjoint paths. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Computation over Mismatched Channels

    Publication Year: 2013 , Page(s): 666 - 677
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (289 KB) |  | HTML iconHTML  

    We consider the problem of distributed computation of a target function over a two-user deterministic multiple-access channel. If the target and channel functions are matched (i.e., compute the same function), significant performance gains can be obtained by jointly designing the communication and computation tasks. However, in most situations there is mismatch between these two functions. In this work, we analyze the impact of this mismatch on the performance gains achievable with joint communication and computation designs over separation-based designs. We show that for most pairs of target and channel functions there is no such gain, and separation of communication and computation is optimal. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Linear Coding Schemes for the Distributed Computation of Subspaces

    Publication Year: 2013 , Page(s): 678 - 690
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (665 KB) |  | HTML iconHTML  

    Let X1, ..., Xm be a set of m statistically dependent sources over the common alphabet Fq, that are linearly independent when considered as functions over the sample space. We consider a distributed function computation setting in which the receiver is interested in the lossless computation of the elements of an s-dimensional subspace W spanned by the elements of the row vector [X1, ..., Xm]Γ in which the (m × s) matrix Γ has rank s. A sequence of three increasingly refined approaches is presented, all based on linear encoders. The first approach uses a common matrix to encode all the sources and a Korner-Marton like receiver to directly compute W. The second improves upon the first by showing that it is often more efficient to compute a carefully chosen superspace U of W. The superspace is identified by showing that the joint distribution of the {Xi} induces a unique decomposition of the set of all linear combinations of the {Xi}, into a chain of subspaces identified by a normalized measure of entropy. This subspace chain also suggests a third approach, one that employs nested codes. For any joint distribution of the {Xi} and any W, the sum-rate of the nested code approach is no larger than that under the Slepian-Wolf (SW) approach. Under the SW approach, W is computed by first recovering each of the {Xi}. For a large class of joint distributions and subspaces W, the nested code approach is shown to improve upon SW. Additionally, a class of source distributions and subspaces are identified, for which the nested-code approach is sum-rate optimal. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Distributed Function Computation with Confidentiality

    Publication Year: 2013 , Page(s): 691 - 701
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB) |  | HTML iconHTML  

    A set of terminals observe correlated data and seek to compute functions of the data using interactive public communication. At the same time, it is required that the value of a private function of the data remains concealed from an eavesdropper observing this communication. In general, the private function and the functions computed by the nodes can be all different. We show that a class of functions are securely computable if and only if the conditional entropy of data given the value of private function is greater than the least rate of interactive communication required for a related multiterminal source-coding task. A single-letter formula is provided for this rate in special cases. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-Session Function Computation and Multicasting in Undirected Graphs

    Publication Year: 2013 , Page(s): 702 - 713
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB) |  | HTML iconHTML  

    In the function computation problem, certain nodes of an undirected graph have access to independent data, while some other nodes of the graph require certain functions of the data; this model, motivated by sensor networks and cloud computing, is the focus of this paper. We study the maximum rates at which function computation is possible on a capacitated graph; the capacities on the edges of the graph impose constraints on the communication rate. We consider a simple class of computation strategies based on Steiner-tree packing (so-called computation trees), which does not involve block coding and has minimal delay. With a single terminal requiring function computation, computation trees are known to be optimal when the underlying graph is itself a directed tree, but have arbitrarily poor performance in general directed graphs. Our main result is that computation trees are near optimal for a wide class of function computation requirements even at multiple terminals in undirected graphs. The key technical contribution involves connecting approximation algorithms for Steiner cuts in undirected graphs to the function computation problem. Furthermore, we show that existing algorithms for Steiner tree packings allow us to compute approximately optimal packings of computation trees in polynomial time. We also show a close connection between the function computation problem and a communication problem involving multiple multicasts. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Network Flows for Function Computation

    Publication Year: 2013 , Page(s): 714 - 730
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1080 KB) |  | HTML iconHTML  

    We consider in-network computation of an arbitrary function over an arbitrary communication network. A network with capacity constraints on the links is given. Some nodes in the network generate data, e.g., like sensor nodes in a sensor network. An arbitrary function of this distributed data is to be obtained at a terminal node. The structure of the function is described by a given computation schema, which in turn is represented by a directed tree. We design computing and communicating schemes to obtain the function at the terminal at the maximum rate. For this, we formulate linear programs to determine network flows that maximize the computation rate. We then develop a fast combinatorial primal-dual algorithm to obtain near-optimal solutions to these linear programs. As a subroutine for this, we develop an algorithm for finding the minimum cost embedding of a tree in a network with any given set of link costs. We then briefly describe extensions of our techniques to the cases of multiple terminals wanting different functions, multiple computation schemas for a function, computation with a given desired precision, and to networks with energy constraints at nodes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Computing Statistical Functions in Wired Networks

    Publication Year: 2013 , Page(s): 731 - 742
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (408 KB) |  | HTML iconHTML  

    For applications in which a node is interested in a function of the data generated at different sources, in-network computation is a promising approach to improve the network performance. In this paper, we study the problem of computing the first M moments of the data using in-network computation in an arbitrary wired communication network. We are interested in finding a routing and queue management strategy that maximizes the data rate at which the sources could generate new data. We first propose a very simple tractable flow model that computes an upper bound on the maximum data generation rate that could be supported in a given network for a given M. To validate the tightness of this upper bound and to provide a practical feasible solution, we then propose a heuristic strategy involving the generation of multiple trees and effective queue management that achieves data generation rates close to this upper bound. This cross-validates the tightness of the upper bound and the goodness of our heuristic strategy. Finally, using the flow model, we provide engineering insights on what in-network computation can achieve. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Toward Efficient Distributed Algorithms for In-Network Binary Operator Tree Placement in Wireless Sensor Networks

    Publication Year: 2013 , Page(s): 743 - 755
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (699 KB) |  | HTML iconHTML  

    In-network processing is touted as a key technology to eliminate data redundancy and minimize data transmission, which are crucial to saving energy in wireless sensor networks (WSNs). Specifically, operators participating in in-network processing are mapped to nodes in a sensor network. They receive data from downstream operators, process them and route the output to either the upstream operator or the sink node. The objective of operator tree placement is to minimize the total energy consumed in performing in-network processing. Two types of placement algorithms, centralized and distributed, have been proposed. A problem with the centralized algorithm is that it does not scale to large WSN's, because each sensor node is required to know the complete topology of the network. A problem with the distributed algorithm is their high message complexity. In this paper, we propose a heuristic algorithm to place a treestructured operator graph, and present a distributed implementation to optimize in-network processing cost and reduce the communication overhead. We prove a tight upper bound on the minimum in-network processing cost, and show that the heuristic algorithm has better performance than a canonical greedy algorithm. Simulation-based evaluations demonstrate the superior performance of our heuristic algorithm. We also give an improved distributed implementation of our algorithm that has a message overhead of O(M) per node, which is much less than the O(√NM log2 M) and O(√NM) complexities for two previously proposed algorithms, Sync and MCFA, respectively. Here, N is the number of network nodes and M is the size of the operator tree. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Tractable Bayesian Social Learning on Trees

    Publication Year: 2013 , Page(s): 756 - 765
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (284 KB) |  | HTML iconHTML  

    We study agents in a social network who learn by observing the actions of their neighbors. The agents iteratively estimate an unknown "state of the world" s from initial private signals, and the past actions of their neighbors in the social network. First, we consider a set of Bayesian agents, and investigate the computational problem the agents face in implementing the (myopic) Bayesian decision rule. When private signals are independent conditioned on s, and when the social network graph is a tree, we provide a new `dynamic cavity algorithm' for the agents' calculations, with computational effort that is exponentially lower than what is currently known. We use our algorithm to perform the first numerical simulations of interacting Bayesian agents on networks with hundreds of nodes. Second, we investigate a different model of social learning, with naive agents who practice "majority dynamics", i.e., at each round adopt the majority opinion of their neighbors. Under mild conditions, we show that under majority dynamics, agents learn s with probability 1-ϵ in O(log log (1/ϵ)) rounds. We conjecture that on d-regular trees, myopic Bayesian agents learn s as quickly as agents who practice majority dynamics. Using our algorithm for Bayesian agents, the conjecture implies that the computational effort required of Bayesian agents to learn s is only polylogarithmic in 1/ϵ on d-regular trees. Thus, our results challenge the belief that iterative Bayesian learning is computationally intractable. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Resilient Asymptotic Consensus in Robust Networks

    Publication Year: 2013 , Page(s): 766 - 781
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1330 KB) |  | HTML iconHTML  

    This paper addresses the problem of resilient in-network consensus in the presence of misbehaving nodes. Secure and fault-tolerant consensus algorithms typically assume knowledge of nonlocal information; however, this assumption is not suitable for large-scale dynamic networks. To remedy this, we focus on local strategies that provide resilience to faults and compromised nodes. We design a consensus protocol based on local information that is resilient to worst-case security breaches, assuming the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach asymptotic consensus despite the influence of the misbehaving nodes under different threat assumptions. We show that traditional metrics such as connectivity are not adequate to characterize the behavior of such algorithms, and develop a novel graph-theoretic property referred to as network robustness. Network robustness formalizes the notion of redundancy of direct information exchange between subsets of nodes in the network, and is a fundamental property for analyzing the behavior of certain distributed algorithms that use only local information. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Continuous-Time Analysis of the Simple Averaging Scheme for Global Clock Synchronization in Sparsely Populated MANETs

    Publication Year: 2013 , Page(s): 782 - 793
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1057 KB) |  | HTML iconHTML  

    In sparsely populated mobile ad hoc networks (MANETs), mobile nodes are chronically isolated each other and they meet very occasionally. Global clock synchronization among nodes in such networks is a challenging problem because reference clock information cannot be disseminated promptly over nodes due to the lack of stable connections among nodes. In recent years, averaging-based algorithms for distributed global clock synchronization have been studied. In this paper, we conduct the continuous-time analysis of the simplest one, called the simple averaging scheme, where two mobile nodes exchange their local clock times when they meet and adjust their own clocks to the average of them. Through the analysis and simulation experiments, we reveal how the clock accuracy of nodes and meeting rates among them affect the rate of convergence to the steady state and the accuracy of clock synchronization in steady state. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Topological Conditions for In-Network Stabilization of Dynamical Systems

    Publication Year: 2013 , Page(s): 794 - 807
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1704 KB) |  | HTML iconHTML  

    We study the problem of stabilizing a linear system over a wireless network using a simple in-network computation method. Specifically, we study an architecture called the "Wireless Control Network" (WCN), where each wireless node maintains a state, and periodically updates it as a linear combination of neighboring plant outputs and node states. This architecture has previously been shown to have low computational overhead and beneficial scheduling and compositionality properties. In this paper we characterize fundamental topological conditions to allow stabilization using such a scheme. To achieve this, we exploit the fact that the WCN scheme causes the network to act as a linear dynamical system, and analyze the coupling between the plant's dynamics and the dynamics of the network. We show that stabilizing control inputs can be computed in-network if the vertex connectivity of the network is larger than the geometric multiplicity of any unstable eigenvalue of the plant. This condition is analogous to the typical min-cut condition required in classical information dissemination problems. Furthermore, we specify equivalent topological conditions for stabilization over a wired (or point-to-point) network that employs network coding in a traditional way - as a communication mechanism between the plant's sensors and decentralized controllers at the actuators. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Impact of In-Network Aggregation on Target Tracking Quality Under Network Delays

    Publication Year: 2013 , Page(s): 808 - 818
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1949 KB) |  | HTML iconHTML  

    In this paper, we investigate how in-network aggregation approach impacts the target tracking quality in multi-hop wireless sensor networks under network delays. Specifically, we use the mean squared error (MSE) of the target location estimate to quantify the target tracking quality, and investigate how in-network aggregation affects the MSE. To obtain insights without being obscured by onerous mathematical details, we assume a Brownian motion mobility model for the target, Gaussian measurement noise for the sensors, and independent per-hop delays. Under the above assumptions, we first propose an aggregation scheme that preserves a sufficient statistic for optimal tracking under data aggregation at the intermediate nodes and arbitrary network delays. We then analytically study the impact of aggregation in three increasingly more complicated scenarios: single task tracking with only transmission delay, single task tracking with both transmission delay and queueing delay at intermediate nodes, and multi-task tracking. Our results demonstrate that in-network aggregation improves tracking quality in all three scenarios. Furthermore, our analysis provides guidelines on how to choose aggregation parameters in practice. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Call for Papers

    Publication Year: 2013 , Page(s): 819
    Save to Project icon | Request Permissions | PDF file iconPDF (27 KB)  
    Freely Available from IEEE
  • Call for Papers

    Publication Year: 2013 , Page(s): 820
    Save to Project icon | Request Permissions | PDF file iconPDF (27 KB)  
    Freely Available from IEEE
  • Information for Authors

    Publication Year: 2013 , Page(s): 821
    Save to Project icon | Request Permissions | PDF file iconPDF (44 KB)  
    Freely Available from IEEE
  • Open Access

    Publication Year: 2013 , Page(s): 822
    Save to Project icon | Request Permissions | PDF file iconPDF (790 KB)  
    Freely Available from IEEE
  • Call for Papers

    Publication Year: 2013 , Page(s): 823
    Save to Project icon | Request Permissions | PDF file iconPDF (32 KB)  
    Freely Available from IEEE
  • On-line tutorials: converged core networks and services [advertisement]

    Publication Year: 2013 , Page(s): 824
    Save to Project icon | Request Permissions | PDF file iconPDF (767 KB)  
    Freely Available from IEEE
  • Staff List

    Publication Year: 2013 , Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Journal on Selected Areas in Communications focuses on all telecommunications, including telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Muriel Médard
MIT