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32
Consensus and collision detectors in wireless ad hoc networks
 In PODC
, 2005
"... Abstract In this study, we consider the faulttolerant consensus problem in wireless ad hoc networks with crashprone nodes. Specifically, we develop lower bounds and matching upper bounds for this problem in singlehop wireless networks, where all nodes are located within broadcast range of each oth ..."
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Cited by 46 (21 self)
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Abstract In this study, we consider the faulttolerant consensus problem in wireless ad hoc networks with crashprone nodes. Specifically, we develop lower bounds and matching upper bounds for this problem in singlehop wireless networks, where all nodes are located within broadcast range of each other. In a novel break from existing work, we introduce a highly unpredictable communication model in which each node may lose an arbitrary subset of the messages sent by its neighbors during each round. We argue that this model better matches behavior observed in empirical studies of these networks. To cope with this communication unreliability we augment nodes with receiverside collision detectors and present a new classification of these detectors in terms of accuracy and completeness. This classification is motivated by practical realities and allows us to determine, roughly speaking, how much collision detection capability is enough to solve the consensus problem efficiently in this setting. We consider ten different combinations of completeness and accuracy properties in total, determining for each whether consensus is solvable, and, if it is, a lower bound on the number of rounds required. Furthermore, we distinguish anonymous and nonanonymous protocolswhere &quot;anonymous &quot; implies that devices do not have unique identifiersdetermining what effect (if any) this extra information has on the complexity of the problem. In all relevant cases, we provide matching upper bounds. Our contention is that the introduction of (possibly weak) receiverside collision detection is an important approach to reliably solving problems in unreliable networks. Our results, derived in a realistic network model, provide important feedback to ad hoc network practitioners regarding what hardware (and lowlayer software) collision detection capability is sufficient to facilitate the construction of reliable and faulttolerant agreement protocols for use in realworld deployments.
Packets Distribution Algorithms for Sensor Networks
, 2003
"... In this paper, we study, via simple discrete mathematical models, the problems of data distribution and data collection in wireless sensor networks. The work that follows continues the work presented by the authors in [1] where the focus was on sensor networks equipped with unidirectional antenna el ..."
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Cited by 32 (0 self)
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In this paper, we study, via simple discrete mathematical models, the problems of data distribution and data collection in wireless sensor networks. The work that follows continues the work presented by the authors in [1] where the focus was on sensor networks equipped with unidirectional antenna elements. Here we shift our interest to networks equipped with omnidirectional antenna elements. In particular we show how the data distribution and collection tasks can be performed optimally (with respect to time) on tree networks and give the corresponding time performances of those strategies. We also present a strategy for general graph networks that performs within a factor of 3 of the optimal performance. Finally we compare the performance of a network equipped with omnidirectional antenna elements with one equipped with unidirectional antenna elements. We show the latter outperforms the former by 33% at most in tree networks. To that purpose we included relevant results on directional antenna sensor networks, partly obtained in [1].
Lower bounds on data collection time in sensory networks
 IEEE Journal on Selected Areas in Communications
, 2004
"... Abstract—Data collection, i.e., the aggregation at the user location of information gathered by sensor nodes, is a fundamental function of sensory networks. Indeed, most sensor network applications rely on data collection capabilities, and consequently, an inefficient data collection process may adv ..."
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Cited by 32 (0 self)
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Abstract—Data collection, i.e., the aggregation at the user location of information gathered by sensor nodes, is a fundamental function of sensory networks. Indeed, most sensor network applications rely on data collection capabilities, and consequently, an inefficient data collection process may adversely affect the performance of the network. In this paper, we study via simple discrete mathematical models, the time performance of the data collection and data distribution tasks in sensory networks. Specifically, we derive the minimum delay in collecting sensor data for networks of various topologies such as line, multiline, and tree and give corresponding optimal scheduling strategies. Furthermore, we bound the data collection time on general graph networks. Our analyses apply to networks equipped with directional or omnidirectional antennas and simple comparative results of the two systems are presented. Index Terms—Data collection, delay, sensory networks. I.
The abstract MAC layer
, 2009
"... Abstract. A diversity of possible communication assumptions complicates the study of algorithms and lower bounds for radio networks. We address this problem by defining an Abstract MAC Layer. This service provides reliable local broadcast communication, with timing guarantees stated in terms of a ..."
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Cited by 20 (15 self)
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Abstract. A diversity of possible communication assumptions complicates the study of algorithms and lower bounds for radio networks. We address this problem by defining an Abstract MAC Layer. This service provides reliable local broadcast communication, with timing guarantees stated in terms of a collection of abstract delay functions applied to the relevant contention. Algorithm designers can analyze their algorithms in terms of these functions, independently of specific channel behavior. Concrete implementations of the Abstract MAC Layer over basic radio network models generate concrete definitions for these delay functions, automatically adapting bounds proven for the abstract service to bounds for the specific radio network under consideration. To illustrate this approach, we use the Abstract MAC Layer to study the new problem of MultiMessage Broadcast, a generalization of standard singlemessage broadcast, in which any number of messages arrive at any processes at any times. We present and analyze two algorithms for MultiMessage Broadcast in static networks: a simple greedy algorithm and one that uses regional leaders. We then indicate how these results can be extended to mobile networks. 1
The Wireless Synchronization Problem
, 2009
"... In this paper, we study the wireless synchronization problem which requires devices activated at different times on a congested singlehop radio network to synchronize their round numbering. We assume a collection of n synchronous devices with access to a shared band of the radio spectrum, divided i ..."
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Cited by 16 (6 self)
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In this paper, we study the wireless synchronization problem which requires devices activated at different times on a congested singlehop radio network to synchronize their round numbering. We assume a collection of n synchronous devices with access to a shared band of the radio spectrum, divided into F narrowband frequencies. We assume that the communication medium suffers from unpredictable, perhaps even malicious interference, which we model by an adversary that can disrupt up to t frequencies per round. Devices begin executing in different rounds and the exact number of participants is not known in advance. “ We first prove a lower bound, demonstrating that at least log Ω
Polylogarithmic additive inapproximability of the radio broadcast problem
 in 7th Int’l Workshop on Approximation Algorithms for Combinatorial Optimization Problems–APPROX’04
, 2004
"... Abstract The input for the radio broadcast problem is an undirected nvertex graph G and a source node s. The goal is to send a message from s to the rest of the vertices in minimum number of rounds. In a round, a vertex receives the message only if exactly one of its neighbors transmits. The radio ..."
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Cited by 14 (0 self)
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Abstract The input for the radio broadcast problem is an undirected nvertex graph G and a source node s. The goal is to send a message from s to the rest of the vertices in minimum number of rounds. In a round, a vertex receives the message only if exactly one of its neighbors transmits. The radio broadcast problem admits an O(log 2 n) approximation [10, 22]. In this paper we consider the additive approximation ratio of the problem. We prove that there exists a constant c so that the problem can not be approximated within an additive term of c log 2
Reliable Broadcast in Wireless Mobile Ad Hoc Networks
, 2006
"... We propose a single source reliable broadcasting algorithm for linear gridbased networks where a message is guaranteed to be delivered to all the nodes of the network. The nodes are mobile and can move from one grid point to another. The solution does not require the nodes to know the network size ..."
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Cited by 6 (0 self)
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We propose a single source reliable broadcasting algorithm for linear gridbased networks where a message is guaranteed to be delivered to all the nodes of the network. The nodes are mobile and can move from one grid point to another. The solution does not require the nodes to know the network size or its diameter. The only information a node has is its identity and its position. On average, only a subset of nodes transmit and they transmit only once to achieve reliable broadcast. The protocol is contentionfree and energyefficient. We show that reliable broadcast can be achieved in O(D log n) timeslots despite node mobility, where D is the diameter of the network and n the number of nodes.
Virtual Infrastructure for Wireless Ad Hoc Networks
, 2007
"... One of the most significant challenges introduced by ad hoc networks is coping with the unpredictable deployment, uncertain reliability, and erratic communication exhibited by emerging wireless networks and devices. The goal of this thesis is to develop a set of algorithms that address these challen ..."
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Cited by 5 (1 self)
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One of the most significant challenges introduced by ad hoc networks is coping with the unpredictable deployment, uncertain reliability, and erratic communication exhibited by emerging wireless networks and devices. The goal of this thesis is to develop a set of algorithms that address these challenges and simplify the design of algorithms for ad hoc networks. In the first part of this thesis, I introduce the idea of virtual infrastructure, an abstraction that provides reliable and predictable components in an unreliable and unpredictable environment. This part assumes reliable communication, focusing primarily on the problems created by unpredictable motion and faultprone devices. I introduce several types of virtual infrastructure, and present new algorithms based on the replicatedstatemachine paradigm to implement these infrastructural components. In the second part of this thesis, I focus on the problem of developing virtual infrastructure for more realistic networks, in particular coping with the problem of unreliable communication. I introduce a new framework for modeling wireless networks based on the ability to detect collisions. I then present a new algorithm for
G.: An improved algorithm for radio broadcast
 ACM Trans. Algorithms
, 2007
"... We show that for every radio network G = (V, E) and source s ∈ V, there exists a radio broadcast schedule for G of length Rad(G, s)+O ( √ Rad(G, s) ·log 2 n) = O(Rad(G, s)+log 4 n), where Rad(G, s) is the radius of the radio network G with respect to the source s. This result improves the previous ..."
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Cited by 5 (1 self)
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We show that for every radio network G = (V, E) and source s ∈ V, there exists a radio broadcast schedule for G of length Rad(G, s)+O ( √ Rad(G, s) ·log 2 n) = O(Rad(G, s)+log 4 n), where Rad(G, s) is the radius of the radio network G with respect to the source s. This result improves the previously bestknown upper bound of O(Rad(G, s) + log 5 n) due to Gaber and Mansour [11]. For graphs with constant genus, particularly for planar graphs, we provide an even better upper bound of Rad(G, S) + O ( √ Rad(G, s) · log n + log 3 n) = O(Rad(G, s) + log 3 n).