We study the network capacity of large scale wireless sensor networks under both Gaussian Channel model and Protocol Interference Model. To... Show moreWe study the network capacity of large scale wireless sensor networks under both Gaussian Channel model and Protocol Interference Model. To study network capacity under gaussian channel model, we assume n wireless nodes {v1, v2, · · · , vn} are randomly or arbitrarily distributed in a square region Ba with side-length a. We randomly choose ns multicast sessions. For each source node vi, we randomly select k points pi,j (1 ≤ j ≤ k) in Ba and the node which is closest to pi,j will serve as a destination node of vi. The per-flow unicast(multicast) capacity is defined as the minimum data rate of all unicast(multicast) sessions in this network. We derive the achievable upper bounds on unicast capacity and a upper bound(partial achievable) on multicast capacity of the wireless networks under and Gaussian Channel model. We found that the unicast(multicast) capacity for wireless networks under both two models has three regimes. Under protocol interference model, we 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 vi. The aggregated multicast capacity is defined as the total data rate of all multicast sessions in the network. In this work we derive matching asymptotic upper bounds and lower bounds on multicast capacity of large scale random wireless networks under protocol interference model. PH.D in Computer Science, December 2012 Show less
