In this paper, we consider CSMA policies for scheduling of multihop wireless networks with one-hop traffic. The main contribution of this paper is to propose Unlocking CSMA (U-CSMA) policy that enables to obtain high throughput with low (average) packet delay for large wireless networks. In particular, the delay under U-CSMA policy becomes orderoptimal. For one-hop traffic, delay is defined to be order-optimal if it is O(1), i.e., it stays bounded, as the network-size increases to infinity. Using mean field theory techniques, we analytically show that for torus (grid-like) interference topologies with one-hop traffic, to achieve a network load of ρ, the delay under U-CSMA policy becomes O(1/(1 − ρ) 3 ) as the network-size increases, and hence, delay becomes order optimal. We conduct simulations for general random geometric interference topologies under U-CSMA policy combined with congestion control to maximize a network-wide utility. These simulations confirm that order optimality holds, and that we can use U-CSMA policy jointly with congestion control to operate close to the optimal utility with a low packet delay in arbitrarily large random geometric topologies. To the best of our knowledge, it is for the first time that a simple distributed scheduling policy is proposed that in addition to throughput/utility-optimality exhibits delay order-optimality.2 In the limit of large toruses, the maximum uniform throughput is 0.5, and load ρ in the limit becomes λ 0.5 . See Section III-C for the definition of ρ.

In wireless sensor networks, clustering allows the aggregation of sensor data. It is well known that leveraging the correlation between different samples of the observed data will lead to better utilization of energy reserve. However, no previous work has analyzed the effect of non-ideal data aggregation in multi-hop sensor networks. In this paper, we propose a novel analytical framework to study how partially correlated data affect the performance of clustering algorithms. We analyze the behavior of multi-hop routing and, by combining random geometry techniques and rate distortion theory, predict the total energy consumption and network lifetime. We show that when a moderate amount of correlation is available, the optimal probabilities that lead to minimum energy consumption are far from optimality in terms of network lifetime. In addition, we study the sensitivity of the total energy consumption and network lifetime to the amount of correlation and compression distortion constraint.

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Abstract-In this paper, we consider the problem of optimal control for throughput utility maximization in cognitive radio networks with dynamic user arrivals and departures. The cognitive radio network considered in this paper consists of a number of heterogeneous sub-networks. These sub-networks may be power-constrained and are required to operate in such a way that the average total interference received on primary channels are kept below given thresholds. We develop a control policy that performs joint admission control and resource scheduling. Through Lyapunov optimization techniques, we show that the proposed policy achieves a utility performance within O(δ) of optimality for any positive δ. We further show that this arbitrarily closeness to optimality comes at the price of having a delay that is O( 1 δ ) in admitting users. We also propose constant factor approximations of the policy for distributed implementation.

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