The simultaneous optimization of multiple responses in a dynamic system is challenging. When a response has a known gradient, it is often easily improved along the path of steepest ascent. On the contrary, a stochastic approximation technique may be used when the gradient is unknown or costly to obtain. We consider the problem of optimizing multiple responses in which the gradient is known for only one response. We propose a hybrid approach for this problem called simultaneous perturbation stochastic approximation steepest ascent, SPSA-SA or SP(SA)2 for short. SP(SA)2 is an SPSA technique that leverages information about the known gradient to constrain the perturbations used to approximate the others. We apply SP(SA)2 to the cross-layer optimization of throughput, packet loss, and end-to-end delay in a mobile ad hoc network (MANET), a self- organizing wireless network. The results show that SP(SA)2 achieves higher throughput and lower packet loss and end-to-end delay than the steepest ascent, SPSA, and the Nelder-Mead stochastic approximation approaches. It also reduces the cost in the number of iterations to perform the optimization.

In this paper, we present an analytic model and a methodology to determine the optimal packet scheduling policy in a High Speed Downlink Packet Access (HSDPA) system. The optimal policy is the one that maximizes cell throughput while maintaining a level of fairness between the users in the cell. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for optimal scheduling policy. We use a FSMC (Finite State Markov Channel) to model the HSDPA downlink channel. A near optimal heuristic scheduling policy is developed. Simulation is used to study the performance of the resulted heuristic policy and compare it to the computed optimal policy. The results show that the devised heuristic policy performs very close to the optimal policy. It has much less computational complexity which makes it easy to deploy and with only slight reduction in performance compared to the optimal policy.

In this paper, we propose a constrained queueing model to investigate the performance of multi-hop wireless sensor networks. Speci¯cally, the cross layer interactions of rate admission control, tra±c engineering, dynamic routing and adaptive link scheduling are studied jointly with the proposed queueing model. In addition, the stochastic network utility maximization problem in wireless sensor networks is addressed within this framework. We propose an adaptive network resource allocation scheme, a.k.a., ANRA algorithm, which provides a joint solution to the multiple layer components of the stochastic network utility maximization problem. We show that the proposed ANRA algorithm achieves a near-optimal solution, i.e., (1 ¡ ²) of the global optimum network utility where ² can be arbitrarily small, with a tradeo® with the average delay experienced in the network. The proposed ANRA algorithm enjoys the merit of self-adaptability through its online nature and thus is of particular interest for time varying scenarios such as multi-hop wireless sensor networks.

We consider the problem of throughput-optimal cross-layer design of wireless networks. We propose a joint congestion control and scheduling algorithm that achieves a fraction 1=dI (G) of the capacity region, where dI (G) depends on certain structural properties of the underlying connectivity graph G of the wireless network, and also on the type of interference constraints. For a wide range of wireless networks, dI (G) can be upper bounded by a constant, independent of the number of nodes in the network. The scheduling element of our algorithm is the maximal scheduling policy. Although this scheduling policy has been considered in several previous works, the challenges underlying its practical implementation in a fully distributed manner while accounting for necessary message exchanges have not been addressed in the literature. In this paper, we propose two algorithms for the distributed implementation of the maximal scheduling policy accounting for message exchanges, and analytically show that they still can achieve the performance guarantee under the 1-hop and 2-hop interference models. We also evaluate the performance of our cross-layer solutions in more realistic network settings with imperfect synchronization under the signal-to-interference-plus-noise ratio (SINR) interference model, and compare with the standard layered approaches such as TCP over IEEE 802.11b DCF Networks.

Cross-layer design has become one of the most e®ective and e±cient methods to provide quality of service (QoS) over various communication networks, especially over wireless multimedia net- works. However, current research on cross-layer design has been carried out in various piecemeal approaches, and lacks a methodological foundation to gain in-depth understanding of complex cross-layer behaviors such as multiscale temporal-spatial behavior, leading to a design paradox and/or unmanageable design problems. In this paper, we propose a theoretical framework for quantitative interaction measures, which is further extended to sensitivity analysis by quantifying the contribution made by each design variable and by the interactions among them on the design objective. Thus, the proposed framework can signi¯cantly enhance our capability for cross-layer behavior characterization and provide design insights for future design. Furthermore, a case study on cross-layer optimized wireless multimedia communications has been adopted to illustrate major cross-layer design tradeo®s and validate the proposed framework. Both analytical and experimen- tal results show the correctness and e®ectiveness of the proposed framework.

We propose a new framework for worst case performance evaluation of MAC protocols for wireless ad hoc networks. Given a protocol, its performance metrics and a network topology, our framework first generates MAC scenarios which achieve poor performance at MAC-level. In order to evaluate the impact of these MAC scenarios on the end performance, we model the interactions between MAC interface and the MAC layer using a state transition graph and generate high level scenarios using enumeration techniques. These high level scenarios can be simulated and compared with heuristics developed by others to identify high level scenarios that are expected to lead to the worst case end performance.

In order to demonstrate its usefulness, we use our framework to evaluate the worst case performance of IEEE 802.11 DCF protocol by generating a library of MAC and high level scenarios. We simulate the high level scenarios to demonstrate that the scenarios we generate exhibit the worst performance among all the scenarios, including those generated by using heuristics recently proposed by other researchers.

We present a numerical inversion method for generating random variates from continuous distributions when only the density function is given. The algorithm is based on polynomial interpolation of the inverse CDF and Gauss-Lobatto integration. The user can select the required precision which may be close to machine precision for smooth, bounded densities; the necessary tables have moderate size. Our computational experiments with the classical standard distributions (normal, beta, gamma, t-distributions) and with the noncentral chi-square, hyperbolic, generalized hyperbolic and stable distributions showed that our algorithm always reaches the required precision. The setup time is moderate and the marginal execution time is very fast and nearly the same for all distributions. Thus for the case that large samples with xed parameters are required the proposed algorithm is the fastest inversion method known. Speed-up factors up to 1000 are obtained when compared to inversion algorithms developed for the specic distributions. This makes our algorithm especially attractive for the simulation of copulas and for quasi-Monte Carlo applications.

Scheduling and resource management play an important role in building complex distributed systems, such as grids. In this paper we study the impact on performance of job service demand variability in a two-level grid architecture, given that the grid and local schedulers are unaware of each job’s service demand. We examine two scheduling policies at grid level, which utilize site load information and three policies at local level. A simulation model is used to evaluate performance. Results show that service demand variability degrades performance, and thus a suitable local resource allocation policy is needed to reduce its impact.

As a collective and highly dynamic social group, human crowd is a fascinating phenomenon which has been constantly concerned by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This paper assesses the major existing technologies for crowd modeling and simulation. We ¯rst propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time- scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user.

We have discussed some in°uential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in these work. We have also discussed some open problems in the area. This paper will provide the researchers with use- ful information and insights on the state-of-the-art of the technologies in crowd modeling and simulation as well as future research directions.

Lindley’s recursion is an explicit recursive equation that describes the recursive relationship between consecutive waiting times in a single-stage single-server queue. In this paper, we develop explicit recursive representations for multi-server tandem queues with blocking. We demonstrate the application of these recursive representations with simulations of large-scale tandem queueing networks. We compare the computational efficiency of these representations with two other popular discrete-event simulation approaches, namely, event scheduling and process interaction. Experimental results show that these representations are seven (or more) times faster than their counterparts based on the event-scheduling and process-interaction approaches.

The design, visualization, manipulation, and implementation of models for computer simulation are key parts of the discipline. Models are constructed as a means to understand physical phenomena as state changes occur over time. One issue that arises is the need to correlate models and their components with the phenomena being modeled. For example, a part of an automotive engine needs to be placed into cognitive context with the diagrammatic icon that represents that part's function. A typical solution to this problem is to display a dynamic model of the engine in one window and the engine's CAD model in another. Users are expected to, on their own, mentally blend the dynamic model and the physical phenomenon into the same context. However, this contextualization is not trivial in many applications.

Our approach expands upon this form of user interaction by specifying two ways in which dynamic models and the corresponding physical phenomena may be viewed, and experimented with, within the same human interaction space. We present a methodology and implementation of contextualization for diagram-based dynamic models using an anesthesia machine, and then follow up with a human study of its effects on spatial cognition.

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning for evacuation scenarios, whose submodules are based on a Bayesian belief network (BBN), Decision- Field-Theory (DFT), and a probabilistic depth first search (PDFS) technique. A key novelty of the proposed model is its ability to represent both the human decision-making and decision-planning functions in a unified framework. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human’s evacuation behaviors in response to a terrorist bomb attack. The simulated environment and agents (models of humans) conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each agent calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualities).

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