Catálogo de publicaciones - libros

In this paper, we propose various novel detection algorithms based on chip-level processing for direct sequence code-division multiple access (DS/CDMA) pseudo noise (PN) code acquisition in fast fading channels, wherein the fading process changes rapidly within the accumulation interval of the correlation samples between the locally generated and received PN codes. By applying the maximum-likelihood (ML) and locally optimal (LO) detection criteria to the correlation samples obtained on a chip-by-chip basis, both optimal and LO detection algorithms are derived. Both of these algorithms are found to include the conventional algorithm as a special case. Simpler suboptimal algorithms are also derived. Finally, numerical results show that the proposed algorithms can offer a substantial improvement over the conventional algorithm in fast fading channels.

As a potential solution to provide quality of service (QoS) for next-generation IP networks, QoS routing (QoSR) seeks to find a multi-constrained path, where the scalability and routing performance are still open problems. We propose a novel Clustering-based Distributed Precomputation algorithm (CDP) for multi-constrained QoSR. After dominating path selection is analyzed to omitting numerous dominated paths, a clustering technique is further presented for dominating path aggregation in routing computation. These two techniques in turn achieve efficient aggregation of the QoS routing table. CDP greatly decreases the computational complexity on a single node by utilizing the distributed computation on each node in the network. Simulation results confirm that CDP not only has low computational complexity, but also achieves high routing performance with good scalability on both QoS parameters and the network scale.

In optical networks with huge transmission capability, Wavelength Division Multiplexing (WDM) has been actively studied in the research community. Traffic grooming technology based on divided bandwidth of a wavelength by WDM is very important for the network cost. This paper proposes a traffic grooming algorithm that employs the table for shortest Edge Disjoint Paths (EDPs) with clever selection on demands. Comprehensive simulations in various network environments show that the proposed algorithm outperforms well-known Maximizing Resource Utilization (MRU) up to 15% for the network throughput and up to 17% for the running time.

Publish/subscribe model is appropriate in many push based data dissemination applications such as data dissemination services, information sharing, service discovery, etc. Recently, theses types systems are rapidly adopted in mobile and ubiquitous environment. However, mobile and ubiquitous environments are error prone due to wireless link disconnection, power exhaustion on mobile devices, etc. We analyze performance and effectiveness of publish/subscribe systems on the failure of client and server nodes and disconnection of communication links, which is common in mobile environments. We also perform experiments on our test bed to verify correctness and usefulness of our analysis.

Mobile ad hoc networking offers convenient infrastructureless communication over the shared wireless channel. However, the nature of ad hoc networks makes them vulnerable to security attacks. Existing security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermeasures are only to protect the networks and there is no automated, network-wide counteraction against detected intrusions. While they all play an important role in counteracting intrusion, they do not, however, effectively address the root cause of the problem – intruders. In this paper, we propose the architecture of automated intrusion response. When an intruder is found in our architecture, the block agents will get to the neighbor nodes of the intruder and formed the mobile firewall to isolate the intruder.

The integration of WLANs and CDMA networks has recently evolved into a very hot issue. In order to support a vertical handoff, we propose a context based vertical handoff decision process and the corresponding algorithms from WLAN to CDMA system, and vice versa, based on wireless channel assignment. We focuses on the handoff decision which uses context information such as dropping probability, blocking probability, GoS (Grade of Service), the number of handoff attempts and velocity. As a decision criterion, velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign available channels to the mobile stations with various handoff strategies. The proposed scheme is validated using computer simulation. Also, the overflow traffic (a vertical handoff) is evaluated and compared with non-overflow traffics in terms of GoS.

We are presenting a progress overview associated with our work on a data-driven environment for multiphysics applications (DDEMA). In this paper, we emphasize the dynamic-data-driven adaptive modeling and simulation aspects. Adaptive simulation examples of sensor-originating data-driven precomputed solution synthesis are given for two applications. Finally, some of the computational implementation details are presented.

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems () that efficiently integrate the observational data and the models. In this paper we discuss fundamental aspects of nonlinear ensemble data assimilation applied to atmospheric chemical transport models. We formulate autoregressive models for the background errors and show how these models are capable of capturing flow dependent correlations. Total energy singular vectors describe the directions of maximum errors growth and are used to initialize the ensembles. We highlight the challenges encountered in the computation of singular vectors in the presence of stiff chemistry and propose solutions to overcome them. Results for a large scale simulation of air pollution in East Asia illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.

The adequate location of wells in oil and environmental applications has a significant economical impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic dynamic data-driven self-optimizing reservoir framework. In this paper, we present the use of distributed data to dynamically drive the optimization of well placement in an oil reservoir.