Results 1  10
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80
The price of being nearsighted
 In SODA ’06: Proceedings of the seventeenth annual ACMSIAM symposium on Discrete algorithm
, 2006
"... Achieving a global goal based on local information is challenging, especially in complex and largescale networks such as the Internet or even the human brain. In this paper, we provide an almost tight classification of the possible tradeoff between the amount of local information and the quality o ..."
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Cited by 57 (11 self)
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Achieving a global goal based on local information is challenging, especially in complex and largescale networks such as the Internet or even the human brain. In this paper, we provide an almost tight classification of the possible tradeoff between the amount of local information and the quality of the global solution for general covering and packing problems. Specifically, we give a distributed algorithm using only small messages which obtains an (ρ∆) 1/kapproximation for general covering and packing problems in time O(k 2), where ρ depends on the LP’s coefficients. If message size is unbounded, we present a second algorithm that achieves an O(n 1/k) approximation in O(k) rounds. Finally, we prove that these algorithms are close to optimal by giving a lower bound on the approximability of packing problems given that each node has to base its decision on information from its kneighborhood. 1
Initializing Newly Deployed Ad Hoc and Sensor Networks
 in Proceedings of 10 th Annual International Conference on Mobile Computing and Networking (MOBICOM
, 2004
"... A newly deployed multihop radio network is unstructured and lacks a reliable and e#cient communication scheme. In this paper, we take a step towards analyzing the problems existing during the initialization phase of ad hoc and sensor networks. Particularly, we model the network as a multihop quasi ..."
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Cited by 49 (14 self)
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A newly deployed multihop radio network is unstructured and lacks a reliable and e#cient communication scheme. In this paper, we take a step towards analyzing the problems existing during the initialization phase of ad hoc and sensor networks. Particularly, we model the network as a multihop quasi unit disk graph and allow nodes to wake up asynchronously at any time. Further, nodes do not feature a reliable collision detection mechanism, and they have only limited knowledge about the network topology. We show that even for this restricted model, a good clustering can be computed e#ciently. Our algorithm e#ciently computes an asymptotically optimal clustering. Based on this algorithm, we describe a protocol for quickly establishing synchronized sleep and listen schedule between nodes within a cluster. Additionally, we provide simulation results in a variety of settings.
A LogStar Distributed Maximal Independent Set Algorithm . . .
 PODC'08
, 2008
"... We present a novel distributed algorithm for the maximal independent set (MIS) problem. On growthbounded graphs (GBG) our deterministic algorithm finishes in O(log ∗ n) time, n being the number of nodes. In light of Linial’s Ω(log ∗ n) lower bound our algorithm is asymptotically optimal. Our algori ..."
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Cited by 46 (15 self)
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We present a novel distributed algorithm for the maximal independent set (MIS) problem. On growthbounded graphs (GBG) our deterministic algorithm finishes in O(log ∗ n) time, n being the number of nodes. In light of Linial’s Ω(log ∗ n) lower bound our algorithm is asymptotically optimal. Our algorithm answers prominent open problems in the ad hoc/sensor network domain. For instance, it solves the connected dominating set problem for unit disk graphs in O(log ∗ n) time, exponentially faster than the stateoftheart algorithm. With a new extension our algorithm also computes a δ + 1 coloring in O(log ∗ n) time, where δ is the maximum degree of the graph.
Fast Deterministic Distributed Maximal Independent Set Computation on GrowthBounded Graphs
 IN PROC. 19TH CONFERENCE ON DISTRIBUTED COMPUTING (DISC
, 2005
"... The distributed complexity of computing a maximal independent set in a graph is of both practical and theoretical importance. While there exists an elegant O(log n) time randomized algorithm for general graphs [20], no deterministic polylogarithmic algorithm is known. In this paper, we study the p ..."
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Cited by 37 (10 self)
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The distributed complexity of computing a maximal independent set in a graph is of both practical and theoretical importance. While there exists an elegant O(log n) time randomized algorithm for general graphs [20], no deterministic polylogarithmic algorithm is known. In this paper, we study the problem in graphs with bounded growth, an important family of graphs which includes the wellknown unit disk graph and many variants thereof. Particularly, we propose a deterministic algorithm that computes a maximal independent set in time O(log \Delta * log*n) in graphs with bounded growth, where n and \Delta denote the number of nodes and the maximal degree in G, respectively.
Maximal Independent Sets in Radio Networks
"... We study the distributed complexity of computing a maximal independent set (MIS) in radio networks with completely unknown topology, asynchronous wakeup, and no collision detection mechanism available. Specifically, we propose a novel randomized algorithm that computes a MIS in time O(log 2 n) with ..."
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Cited by 33 (7 self)
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We study the distributed complexity of computing a maximal independent set (MIS) in radio networks with completely unknown topology, asynchronous wakeup, and no collision detection mechanism available. Specifically, we propose a novel randomized algorithm that computes a MIS in time O(log 2 n) with high probability, where n is the number of nodes in the network. This significantly improving on the best previously known solutions. A lower bound of Ω(log 2 n / log log n) given in [11] implies that our algorithm’s running time is close to optimal. Our result shows that the harsh radio network model imposes merely an additional O(log n) factor compared to Luby’s MIS algorithm in the message passing model. This has important implications in the context of ad hoc and sensor networks whose characteristics are closely captured by the radio network model.
Modeling sensor networks
, 2008
"... In order to develop algorithms for sensor networks and in order to give mathematical correctness and performance proofs, models for various aspects of sensor networks are needed. This chapter presents and discusses currently used models for sensor networks. Generally, finding good models is a challe ..."
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Cited by 27 (5 self)
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In order to develop algorithms for sensor networks and in order to give mathematical correctness and performance proofs, models for various aspects of sensor networks are needed. This chapter presents and discusses currently used models for sensor networks. Generally, finding good models is a challenging task. On the one hand, a
Facility location: distributed approximation
 In Proceedings of the twentyfourth annual ACM symposium on Principles of distributed computing
, 2005
"... In this paper, we initiate the study of the approximability of the facility location problem in a distributed setting. In particular, we explore a tradeoff between the amount of communication and the resulting approximation ratio. We give a distributed algorithm that, for every constant k, achieves ..."
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Cited by 27 (1 self)
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In this paper, we initiate the study of the approximability of the facility location problem in a distributed setting. In particular, we explore a tradeoff between the amount of communication and the resulting approximation ratio. We give a distributed algorithm that, for every constant k, achieves an O ( √ k(mρ) 1/ √ k log (m + n)) approximation in O(k) communication rounds where message size is bounded to O(log n) bits. The number of facilities and clients are m and n, respectively, and ρ is a coefficient that depends on the cost values of the instance. Our technique is based on a distributed primaldual approach for approximating a linear program, that does not form a covering or packing program.
Veracity radius  capturing the locality of distributed computations
 ACM PODC
, 2006
"... This paper focuses on local computations of distributed aggregation problems on fixed graphs. We define a new metric on problem instances, Veracity Radius (VR), which captures the inherent possibility to compute them locally. We prove that VR yields a tight lower bound on outputstabilization time, ..."
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Cited by 19 (8 self)
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This paper focuses on local computations of distributed aggregation problems on fixed graphs. We define a new metric on problem instances, Veracity Radius (VR), which captures the inherent possibility to compute them locally. We prove that VR yields a tight lower bound on outputstabilization time, i.e., the time until all nodes fix their outputs, as well as a lower bound on quiescence time. We present an efficient aggregation algorithm, ILEAG, which reaches both output stabilization and quiescence within a time that is proportional to the VR of the problem instance, and is also efficient in terms of pernode communication and memory. We empirically show that the VR metric also effectively captures the performance of previously suggested efficient aggregation protocols, and that ILEAG significantly outperforms these protocols in several respects.
Distributed Verification of Minimum Spanning Trees
 Proc. 25th Annual Symposium on Principles of Distributed Computing
, 2006
"... The problem of verifying a Minimum Spanning Tree (MST) was introduced by Tarjan in a sequential setting. Given a graph and a tree that spans it, the algorithm is required to check whether this tree is an MST. This paper investigates the problem in the distributed setting, where the input is given in ..."
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Cited by 19 (17 self)
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The problem of verifying a Minimum Spanning Tree (MST) was introduced by Tarjan in a sequential setting. Given a graph and a tree that spans it, the algorithm is required to check whether this tree is an MST. This paper investigates the problem in the distributed setting, where the input is given in a distributed manner, i.e., every node “knows ” which of its own emanating edges belong to the tree. Informally, the distributed MST verification problem is the following. Label the vertices of the graph in such a way that for every node, given (its own label and) the labels of its neighbors only, the node can detect whether these edges are indeed its MST edges. In this paper we present such a verification scheme with a maximum label size of O(log n log W), where n is the number of nodes and W is the largest weight of an edge. We also give a matching lower bound of Ω(log n log W) (except when W ≤ log n). Both our bounds improve previously known bounds for the problem. Our techniques (both for the lower bound and for the upper bound) may indicate a strong relation between the fields of proof labeling schemes and implicit labeling schemes. For the related problem of tree sensitivity also presented by Tarjan, our method yields rather efficient schemes for both the distributed and the sequential settings.
A New Technique For Distributed Symmetry Breaking
 In Symp. on Principles of Distributed Computing
, 2010
"... We introduce MultiTrials, a new technique for symmetry breaking for distributed algorithms and apply it to various problems in general graphs. For instance, we present three randomized algorithms for distributed (vertex or edge) coloring improving on previous algorithms and showing a time/color tra ..."
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Cited by 19 (5 self)
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We introduce MultiTrials, a new technique for symmetry breaking for distributed algorithms and apply it to various problems in general graphs. For instance, we present three randomized algorithms for distributed (vertex or edge) coloring improving on previous algorithms and showing a time/color tradeoff. To get a ∆ + 1 coloring takes time O(log ∆ + √ log n). To obtain an O( ∆ + log 1+1 / log ∗ n n) coloring takes time O(log ∗ n). This is more than an exponential improvement in time for graphs of polylogarithmic degree. Our fastest algorithm works in constant time using O( ∆ log (c) n + log 1+1/c n) colors, where c denotes an arbitrary constant and log (c) n denotes the c times (recursively) applied logarithm to n. We also use the MultiTrials technique to compute network decompositions and to compute maximal independent set (MIS), obtaining new results for several graph classes.