Multicast capacity for large scale wireless ad hoc networks

Abstract

In this paper, we study the capacity of a large-scale random wireless network for multicast. Assume that n wireless nodes are randomly deployed in a square region with side-length 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 v i . 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(). Our bounds unify the previous capacity bounds on unicast (when k = 2) by Gupta and Kumar [7] and the capacity bounds on broadcast (when k = n) in [11,20]. We also study the capacity of group-multicast for wireless networks where for each source node, we randomly select k − 1 groups of nodes as receivers and the nodes in each group are within a constant hops from the group leader. The same asymptotic upper bounds and lower bounds still hold. For arbitrary networks, we provide a constructive lower bound Ω( √ n √ k · W ) for aggregated multicast capacity when we can carefully place nodes and schedule node transmissions.

Data gathering capacity of large scale multihop wireless networks

“…Recently, the network capacity of several other communication paradigms have been investigated. These include the one-to-one relay capacity, where there is only one source-destination pair while other nodes serve as relays [4]; the many-to-one capacity, where all of the nodes send data to one sink node [13,5,3]; the broadcast capacity, where one node broadcasts data to all of the other nodes in the network [19,9,14]; and the multicast capacity, where some nodes send data to other nodes in their respective multicast groups [8,11,18].…”

Multicast Throughput of Hybrid Wireless Networks Under Gaussian Channel Model

“…VapnikChervonenkis Theorem [16] is usually exploited to prove such issue, as in [2], [4], [8]. When the deployment region A is partitioned into a lattice consisting of subsquares that act as Voronoi cells, the exponent tails of probability bound can be equally used to prove the uniform convergence of some probability [10].…”

“…All our strategies are devised based on the cell-partitioned method [4], [8], [10]. For clarify the description of the strategies, we first introduce a notion called scheme lattice.…”

Efficient data aggregation in multi-hop wireless sensor networks under physical interference model

“…Lemma 12: [18] Assume there is a graph with n nodes randoml y deployed in a square region with side-length O( lo~n) ' We partition the deployment square into cells, each of sidelength a constant a. Then there is a sequence of 8(n) ---+ 0 such that…”