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50
Broadcast capacity in multihop wireless networks
 In MobiCom
, 2006
"... Abstract — In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully within a given time. To asses the impact of topology and interfer ..."
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Cited by 106 (5 self)
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Abstract — In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully within a given time. To asses the impact of topology and interference on the broadcast capacity we employ the Physical Model and Generalized Physical Model for the channel. Prior work was limited either by density constraints or by using the less realistic but manageable Protocol model [1], [2]. Under the Physical Model, we find that the broadcast capacity is within a constant factor of the channel capacity for a wide class of network topologies. Under the Generalized Physical Model, on the other hand, the network configuration is divided into three regimes depending on how the power is tuned in relation to network density and size and in which the broadcast capacity is asymptotically either zero, constant or unbounded. As we show, the broadcast capacity is limited by distant nodes in the first regime and by interference in the second regime. In the second regime, which covers a wide class of networks, the broadcast capacity is within a constant factor of the bandwidth. I.
Multicast capacity of wireless ad hoc networks
 IEEE/ACM Trans. Netw
, 2009
"... Abstract—We study the multicast capacity of largescale random extended multihop wireless networks, where a number of wireless nodes are randomly located in a square region with side length a = p n, by use of Poisson distribution with density 1. All nodes transmit at a constant power P, and the powe ..."
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Cited by 70 (23 self)
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Abstract—We study the multicast capacity of largescale random extended multihop wireless networks, where a number of wireless nodes are randomly located in a square region with side length a = p n, by use of Poisson distribution with density 1. All nodes transmit at a constant power P, and the power decays with attenuation exponent> 2. The data rate of a transmission is determined by the SINR as B log(1 + SINR), where B is the bandwidth. There are ns randomly and independently chosen multicast sessions. Each multicast session has k randomly chosen terminals. n We show that when k 1 and ns (log n) 2n 1=2+, the capacity that each multicast p session can achieve, with high proban bility, is at least c8 p, where 1, 2, and c8 are some special conn k stants and> 0 is any positive real number. We also show that for k = O( n), the perflow multicast capacity under Gaussian log n p n channel is at most O ( p) when we have at least ns = (log n) n k random multicast flows. Our result generalizes the unicast capacity for random networks using percolation theory.
Multicast capacity for large scale wireless ad hoc networks
 In ACM Mobicom
, 2007
"... In this paper, we study the capacity of a largescale random wireless network for multicast. Assume that n wireless nodes are randomly deployed in a square region with sidelength a and all nodes have the uniform transmission range r and uniform interference range R> r. We further assume that eac ..."
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Cited by 69 (22 self)
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In this paper, we study the capacity of a largescale random wireless network for multicast. Assume that n wireless nodes are randomly deployed in a square region with sidelength a and all nodes have the uniform transmission range r and uniform interference range R> r. We further assume that each wireless node can transmit/receive at W bits/second over a common wireless channel. For each node vi, we randomly pick k − 1 nodes from the other n − 1 nodes as the receivers of the multicast session rooted at node vi. The aggregated multicast capacity is defined as the total data rate of all multicast sessions in the network. In this paper we derive matching asymptotic upper bounds and lower bounds on multicast capacity of random wireless networks. We show that the total multicast capacity is Θ( � n log n · W √ k) when k = O ( n log n
Information Propagation Speed in Mobile and Delay Tolerant Networks
, 2009
"... The goal of this paper is to increase our understanding of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where endtoend multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive gene ..."
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Cited by 51 (14 self)
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The goal of this paper is to increase our understanding of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where endtoend multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently connected networks. In other words, we upperbound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility and graph models to obtain specific analytical estimates. In particular, when nodes move at speed v and their density ν is small (the network is sparse and surely disconnected), we prove that the information propagation speed is upper bounded by (1 + O(ν 2))v in the random waypoint model, while it is upper bounded by O ( √ νvv) for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios.
Capacity of large scale wireless networks under gaussian channel model
 in Mobicom08
, 2008
"... In this paper, we study the multicast capacity of a large scale random wireless network. We simply consider the extended multihop network, where a number of wireless nodes vi(1 ≤ i ≤ n) are randomly located in a square region with sidelength a = √ n, by use of Poisson distribution with density 1. ..."
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Cited by 48 (20 self)
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In this paper, we study the multicast capacity of a large scale random wireless network. We simply consider the extended multihop network, where a number of wireless nodes vi(1 ≤ i ≤ n) are randomly located in a square region with sidelength a = √ n, by use of Poisson distribution with density 1. All nodes transmit at constant power P, and the power decays along path, with attenuation exponent α> 2. The data rate of a transmission is determined by the SINR as B log(1 + SINR). There are ns randomly and independently chosen multicast sessions. Each multicast has k rann domly chosen terminals. We show that, when k ≤ θ1 (log n) 2α+6, and ns ≥ θ2n 1/2+β, the capacity that each multicast session can n achieve, with high probability, is at least c8 √ , where θ1, θ2, ns k and c8 are some special constants and β> 0 is any positive real number. Our result generalizes the unicast capacity [3] for random networks using percolation theory.
A Unifying Perspective on the Capacity of Wireless Ad Hoc
"... Abstract—We present the first unified modeling framework for the computation of the throughput capacity of random wireless ad hoc networks in which information is disseminated by means of unicast routing, multicast routing, broadcasting, or different forms of anycasting. We introduce (n, m, k)casti ..."
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Cited by 46 (14 self)
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Abstract—We present the first unified modeling framework for the computation of the throughput capacity of random wireless ad hoc networks in which information is disseminated by means of unicast routing, multicast routing, broadcasting, or different forms of anycasting. We introduce (n, m, k)casting as a generalization of all forms of onetoone, onetomany and manytomany information dissemination in wireless networks. In this context, n, m, and k denote the total number of nodes in the network, the number of destinations for each communication group, and the actual number of communicationgroup members that receive information (i.e., k ≤ m), respectively. We compute upper and lower bounds for the (n, m, k)cast throughput capacity in random wireless networks. When m = k = Θ(1), the resulting capacity equals the wellknown capacity result for multipair unicasting by Gupta and Kumar. We demonstrate that Θ(1 / √ mn log n) bits per second constitutes a tight bound for the capacity of multicasting (i.e., m = k < n) when m ≤ Θ (n/(log n)). We show that the multicast capacity of a wireless network equals its capacity for multipair unicasting when the number of destinations per multicast source is not a function of n. We also show that the multicast capacity of a random wireless ad hoc network is Θ (1/n), which is the broadcast capacity of the network, when m ≥ Θ(n / log n). Furthermore, we show that Θ ( √ m/(k √ n log n)), Θ(1/(k log n)) and Θ(1/n) bits per second constitutes a tight bound for the throughput capacity of multicasting (i.e., k < m < n) when Θ(1) ≤ m ≤ Θ (n / log n), k ≤ Θ (n / log n) ≤ m ≤ n and Θ (n / log n) ≤ k ≤ m ≤ n respectively.
Bounds for the capacity of wireless multihop networks imposed by topology and demand
 in Proc. ACM MobiHoc
, 2007
"... Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overes ..."
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Cited by 38 (0 self)
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Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overestimate the achievable capacity in real world situations where network topology or traffic patterns often deviate from these simplistic assumptions. To provide analytically tractable yet asymptotically tight approximations of network capacity we propose a novel spacebased approach. At the heart of our methodology lie simple functions which indicate the presence of active transmissions near any given location in the network and which constitute a tool well suited to untangle the interactions of simultaneous transmissions. We are able to provide capacity bounds which are tighter than the traditional ones and which involve topology and traffic patterns explicitly, e.g., through the length of Euclidean Minimum Spanning Tree, or through traffic demands between clusters of nodes. As an additional novelty our results cover unicast, multicast and broadcast and are asymptotically tight. Notably, our capacity bounds are simple enough to require only knowledge of node location, and there is no need for solving or optimizing multivariable equations in our approach.
Scaling laws on multicast capacity of large scale wireless networks
 in Proc. IEEE INFOCOM
, 2009
"... Abstract—In this paper, we focus on the networkingtheoretic multicast capacity for both random extended networks (REN) and random dense networks (RDN) under Gaussian Channel model, when all nodes are individually powerconstrained. During the transmission, the power decays along path with the atten ..."
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Cited by 18 (11 self)
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Abstract—In this paper, we focus on the networkingtheoretic multicast capacity for both random extended networks (REN) and random dense networks (RDN) under Gaussian Channel model, when all nodes are individually powerconstrained. During the transmission, the power decays along path with the attenuation exponent α> 2. In REN and RDN, n nodes are randomly distributed in the square region with sidelength √ n and 1, respectively. We randomly choose ns nodes as the sources of multicast sessions, and for each source v, we pick uniformly at random nd nodes as the destination nodes. Based on percolation theory, we propose multicast schemes and analyze the achievable throughput by considering all possible values of ns and nd. As a special case of our results, we show that for ns = Θ(n), the 1 persession multicast capacity of RDN is Θ ( √ ndn) when nd = n O( (log n) 3) and is Θ ( 1) when nd = Ω( n n); the persession log n 1 multicast capacity of REN is Θ ( √ ndn) when nd n = O( (log n) α+1) and is Θ ( 1 · (log n) nd − α 2) when nd = Ω ( n log n).
On the Connectivity and Multihop Delay of Ad Hoc Cognitive Radio Networks
"... We analyze the multihop delay of ad hoc cognitive radio networks, where the transmission delay of each hop consists of the propagation delay and the waiting time for the availability of the communication channel (i.e., the occurrence of a spectrum opportunity at this hop). Using theories and techniq ..."
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Cited by 16 (2 self)
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We analyze the multihop delay of ad hoc cognitive radio networks, where the transmission delay of each hop consists of the propagation delay and the waiting time for the availability of the communication channel (i.e., the occurrence of a spectrum opportunity at this hop). Using theories and techniques from continuum percolation and ergodicity, we establish the scaling law of the minimum multihop delay with respect to the sourcedestination distance in cognitive radio networks. When the propagation delay is negligible, we show the starkly different scaling behavior of the minimum multihop delay in instantaneously connected networks as compared to networks that are only intermittently connected due to scarcity of spectrum opportunities. Specifically, if the network is instantaneously connected, the minimum multihop delay is asymptotically independent of the distance; if the network is only intermittently connected, the minimum multihop delay scales linearly with the distance. When the propagation delay is nonnegligible but small, we show that although the scaling order is always linear, the scaling rate for an instantaneously connected network can be orders of magnitude smaller than the one for an intermittently connected network. Index Terms Cognitive radio network, multihop delay, connectivity, intermittent connectivity, continuum percolation, ergodic theory. I.
Toward Optimal Data Aggregation in Random Wireless Sensor Networks
 IEEE Computer Society: Anchorage , AK, USA
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