17144927
Improving scalability of wireless network simulation with bounded inaccuracies
Discrete event network simulators have emerged as popular tools for verification and performance evaluation of wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, limiting most researchers the ability to simulate networks with thousands of nodes. Previous attempts to optimize simulation of large-scale wireless networks have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies that may be further magnified in the evaluation of upper-layer protocols. This article presents a comprehensive analysis on the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance. Based on the analysis, it formulates distance limit derivation and mobility update reduction that introduce bounded inaccuracy to the radio propagation simulation. It further proposes a novel technique, Lazy Event Scheduling with Corrective Retrospection, that reduces simulation events twenty-five fold without introducing any inaccuracy at all. The experimental results show that these optimizations can substantially improve the runtime performance of an already efficient wireless network simulator, by a factor of up to 55 for wireless networks with 3200 nodes without compromising the simulation's accuracy.
15854837
An alternative time management mechanism for distributed simulations
Over the past few years, there has been a keen interest in the management of time in distributed simulation environments. Previous emphasis in time management (TM) services has been based on time stamp ordering, which is both computation and bandwidth intensive. This article discusses an alternative approach to time management based on causal ordering. Traditional causal ordering protocols incur a large amount of communication overhead, which is generally of the order of N2 for a distributed system of N processes. A new causal ordering protocol proposed by the authors, the Modified Schiper-Eggli-Sandoz (MSES) protocol, is presented in this article. This new protocol minimizes the control information overhead of causal ordering by using the direct dependency tracking technique. The MSES protocol works well in both unicast and multicast environments, without relying on information about the underlying network topology and communication pattern among the processes of the distributed system. The MSES protocol has been successfully implemented as a middleware on top of DMSO RTI. Experiments have been conducted to benchmark the performance of the new time management mechanism with respect to the existing TM mechanisms available in DMSO RTI. The simulation scenarios of the experiments vary with different degrees of inter-federate dependency and federate event granularities. The ordering limitations of the causality based TM mechanism are addressed in this article and the trade-off of the degree of event ordering and execution speed of simulations is discussed.
11738299
Optimizing parallel execution of detailed wireless network simulation
With parallel and discrete event simulation (PDES) techniques, the runtime performance of detailed wireless network simulation can be improved significantly without compromising fidelity of the simulation results. However, modelling characteristics of wireless communications such as signal propagation and interference may severely hinder the potential speedup yielded by PDES. This paper proposes various optimization techniques to address three major concerns in achieving efficient parallel execution of wireless network simulation: i.e., (1) reducing communication and computation overhead of simulating signal propagation across multiple logical processes; (2) reducing synchronization overhead among logical processes; (3) minimizing event scheduling overhead within individual logical processes. These techniques have been implemented in a parallel version of GloMoSim and QualNet. The experimental results with mobile ad hoc networking scenarios demonstrate that the proposed optimization techniques can improve the performance of parallel wireless network simulation by up to an order of magnitude.
15147736
Exploiting medium access diversity in rate adaptive wireless LANs
Recent years have seen the growing popularity of multi-rate wireless network devices (e.g., 802.11a cards) that can exploit variations in channel conditions and improve overall network throughput. Concurrently, rate adaptation schemes have been developed that selectively increase data transmissions on a link when it offers good channel quality. In this paper, we propose a Medium Access Diversity (MAD) scheme that leverages the benefits of rate adaptation schemes by aggressively exploiting multiuser diversity. The basic mechanism of MAD is to obtain instantaneous channel condition information from multiple receivers and selectively transmit data to a receiver that improves the overall throughput of the network, while maintaining temporal fairness among multiple data flows. We identify and address the challenges in the design and implementation of MAD's three phases: channel probing, data transmission, and receiver scheduling. We also use analytical models to examine the tradeoff between network performance improvement and overhead of channel probing, and derive an asymptotic performance bound for the receiver scheduling algorithms used by MAD. Results from the analysis and the extensive simulations demonstrate that, on average, MAD can improve the overall throughput of IEEE 802.11 wireless LANs by 50% as compared with the best existing rate adaptation scheme.
27166902
Toward automatic annotation of in situ mRNA expression patterns of drosophila embryos
The in situ mRNA hybridization gene expression pattern images of Drosophila melanogaster in the course of embryogenesis provide spatial-temporal information of the expression patterns of a target gene. This information is critical for understanding the roles of genes during the development of embryos. Currently, the annotation of these images is often done by manually assigning a subset of image ontology terms to the images. This approach is time consuming and depends heavily on the consistency of the experts. Alternatively, if the annotation process can be automated or semi-automated, efficiency and consistency are likely to be greatly enhanced. We formulate this task as a pattern classification problem, and present preliminary results. We consider both the template-based global matching and the multi-objective classification based on neural networks (e.g. multi-layer perceptron). We develop a method to combine them to optimize efficiency and accuracy. This method has been applied to the gene expression pattern image database generated by the Berkeley Drosophila Genome Project.
17813636
Model Generalization and Its Implications on Intrusion Detection
To make up for the incompleteness of the known behaviors of a computing resource, model generalization is utilized to infer more behaviors in the behavior model besides the known behaviors. In principle, model generalization can improve the detection rate but may also degrade the detection performance. Therefore, the relation between model generalization and detection performance is critical for intrusion detection. However, most of past research only evaluates the overall efficiency of an intrusion detection technique via detection rate and false alarm/positive rate, rather than the usefulness of model generalization for intrusion detection. In this paper, we try to do such evaluation, and then to find the implications of model generalization on intrusion detection. Within our proposed methodology, model generalization can be achieved in three levels. In this paper, we evaluate the first level model generalization. The experimental results show that the first level model generalization is useful mostly to enhance the detection performance of intrusion detection. However, its implications for intrusion detection are different with respect to different detection techniques. Our studies show that in general, though it is useful to generalize the normal behavior model so that more normal behaviors can be identified as such, the same is not advisable for the intrusive behavior model. Therefore, the intrusion signatures should be built compactly without first level generalization.
2288037
An Overlay Subscription Network for Live Internet TV Broadcast
We propose a framework, called overlay subscription network (OSN), for live Internet TV broadcast, where a subscriber can choose to watch at any time. This framework allows the source server to incrementally build a topology graph that contains the network connections not only from the server to each subscriber, but also among the subscribers themselves. With such a topology graph in place, we consider efficient overlay multicast for scalable OSN services. We first show that idling nodes, which do not receive video data for their own playback, can actually be used for data forwarding to significantly reduce the cost of overlay multicast. In light of this observation, we then propose a novel overlay multicast technique that distinguishes itself from existing schemes with these three aspects. First, the proposed technique is centered on the topology graph and can take advantage of the actual network connections among the subscribing nodes. Second, the new scheme is able to find and incorporate appropriate idling nodes in multicast to reduce network traffic. Third, with our approach, a node can be used in multiple multicast trees for data forwarding to improve the overall system performance. We evaluate the performance of the proposed technique through simulation. Our extensive studies show that the proposed framework has the potential to enable the Internet, a vehicle up to date mainly for transferring text and image data, for large-scale and cost-effective TV broadcast
11171096
Automatic detection of premature ventricular contraction using quantum neural networks
Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum neurons in the network, trained QNN can model the levels of uncertainty arising from complex classification problems. This fuzzy feature is expected to enhance the reliability of the algorithm, which is critical for the applications in the biomedical domain. Experiments were conducted on ECG records in the MIT-BIH Arrhythmia Database. Results showed consistently higher or same reliability of QNN on all the available records compared to the backpropagation network. QNN, however, has a relatively higher resource requirement for training.
26671042
Enforcing Trust in Pervasive Computing with Trusted Computing Technology
Pervasive computing as a concept holds the promise of simplifying daily life by integrating mobile devices and digital infrastructures into our physical world. These devices in a pervasive environment would establish dynamic ad-hoc networks to provide ubiquitous services. The open and dynamic characteristics of pervasive environments necessitate the requirement for some form of trust assumptions to be made. Trust in this context not only includes authentication, confidentiality and privacy but also includes the belief that the devices and smart environment behave as expected. In this paper, we propose a trust enforced pervasive computing environment using the primitives provided by a TPM (Trusted Platform Module). The application scenario shows how critical information infrastructure such as services and data can be protected. In this smart environment, a person carrying a device authenticates to the environment in order to utilize its services. In this context the device and the smart environment can also test and check each other's behaviors to better perform trust negotiation.