292448
Efficient Network Utilization for Multimedia Wireless Networks
In this paper, we present an access scheme to satisfy the QoS requirements for two classes of traffic during the contention phase of packet-switched wireless communications. In the proposed scheme, difierent classes of users contend with other users for resources based on controlled class-dependent permission probabilities. We prove that our algorithm is stable for a large class of arrival processes. Under certain QoS requirements, we derive an upper-bound on the throughput for a general class of random access algorithms. We show that the throughput of our algorithm asymptotically approaches this upper-bound. We also show, through numerical examples, that our algorithm achieves high network utilization.
5642431
Sensing-based opportunistic channel access
Enabled by regulatory initiatives and advanced radio technologies, more flexible opportunistic spectrum access has great potential to alleviate the spectrum scarcity. In this paper, we study the channel selection issue of secondary users in spectrum-agile communication systems. We focus on the sensing-based approach because it is simple and has low infrastructure requirements. We propose a two-step approach for channel selection. The first step is to determine whether or not a channel is idle and thus accessible to secondary users. We propose three algorithms to perform the accessibility check based on measurements of primary signals. Then we address the question whether an accessible channel is a good opportunity for a secondary user.
368408
A framework for opportunistic scheduling in wireless networks
Abstract We present a method, called opportunistic scheduling, for exploiting the time-varying nature of the radio environment to increase the overall performance of the system under certain quality of service/fairness requirements of users. We first introduce a general framework for opportunistic scheduling, and then identify three general categories of scheduling problems under this framework. We provide optimal solutions for each of these scheduling problems. All the proposed scheduling policies are implementable online; we provide parameter estimation algorithms and implementation procedures for them. We also show how previous work by us and others directly fits into or is related to this framework. We demonstrate via simulation that opportunistic scheduling schemes result in significant performance improvement compared with non-opportunistic alternatives.
12314143
Combs, needles, haystacks: balancing push and pull for discovery in large-scale sensor networks
In this paper we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale vwireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sands or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting discovery and query in large scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for discovery in large scale geometric networks are demonstrated. We also raise the issue of query coverage in unreliable networks and investigate how redundancy can improve the coverage via both theoretical analysis and simulation. Last, we study adaptive strategies for the case where the frequencies of query and events are unknown a priori and time-varying.
6643803
Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
BackgroundThe identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.ResultsWe validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.ConclusionThe temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.
9356286
A design of overlay anonymous multicast protocol
Multicast services are demanded by a variety of applications. Many applications require anonymity during their communication. However, there has been very little work on anonymous multicasting and such services are not available yet. Since there are fundamental differences between multicast and unicast, the solutions proposed for anonymity in unicast communications cannot be directly applied to multicast applications. In this paper, we define the anonymous multicast system, and propose a mutual anonymous multicast (MAM) protocol including the design of a unicast mutual anonymity protocol and construction and optimization of an anonymous multicast tree. MAM is self organizing and completely distributed. We define the attack model in an anonymous multicast system and analyze the anonymity degree. We also evaluate the performance of MAM by simulations
62878
Single-hop probing asymptotics in available bandwidth estimation: sample-path analysis
In this paper, we take the sample-path approach in analyzing the asymptotic behavior of single-hop bandwidth estimation under bursty cross-traffic and show that these results are provably different from those observed under fluid models of prior work. This difference, which we call the probing bias, is one of the previously unknown factors that can cause measurement inaccuracies in available bandwidth estimation. We present an analytical formulation of "packet probing," based on which we derive several major properties of the probing bias. We then experimentally observe the <i>probing bias</i> and investigate its quantitative relationship to several deciding factors such as probing packet size, probing train length, and cross-traffic burstiness. Both our analytical and experimental results show that the probing bias vanishes as the packet-train length or packet size increases. The vanishing rate is decided by the burstiness of cross-traffic.
14510898
Evolutionary learning of dynamic probabilistic models with large time lags
In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm