Reda A. Ammar

Surface-Level Gateway Deployment for Underwater Sensor Networks

The performance of underwater sensor networks (UWSNs) is greatly limited by the low bandwidth and high propagation delay of acoustic communications. Deploying multiple surface-level radio-capable gateways can enhance UWSN performance from many aspects. In this paper, we mainly focus on the surface gateway deployment, which is modelled as an optimization problem. Integer Linear Programming (ILP) is used for solving variations of the deployment optimization problem. The tradeoff between the number of surface gateways and the expected delay and energy consumption is analyzed. We conduct simulations to evaluate the benefits of surface gateway optimization and investigate the effect of acoustic channel capacity and the underwater sensor node deployment pattern. Our results show the significant advantages of surface gateway optimization. The results also provide useful guidelines for real network deployment.

Micro time cost analysis of parallel computations

The authors investigate the modeling and analysis of time cost behavior of parallel computations. It is assumed parallel computations reside in a computer system in which there is a limited number of processors, all the processors have the same speed, and they communicate with each other through a shared memory. It has been found that the time costs of parallel computations depend on the input, the algorithm, the data structure, the processor speed, the number of processors, the processing power allocation, the communication, the execution overhead, and the execution environment. The authors define time costs of parallel computations as a function of the first seven factors as listed. The computation structure model is modified to describe the impact of these seven factors on time cost. Techniques based on the modified computation structure model are developed to analyze time cost. A software tool, TCAS (time cost analysis system), that uses both the analytic and the simulation approaches is designed and implemented to aid users in determining the time cost behavior of their parallel computations. >

An evaluation of the change in electrocardiographic P-wave variables after acute caffeine ingestion in normal volunteers

Background: Caffeine’s effect on supraventricular dysrhythmias is poorly understood, and establishing a marker to predict atrial fibrillation may help to explain supraventricular dysrhythmias caused by caffeine.

Efficient surface gateway deployment for underwater sensor networks

Deploying multiple surface-level radio-capable gateways enhances the performance of underwater acoustic sensor network. The locations of gateways have to be carefully selected to maximize the benefit in a cost-effective way. In this paper, we show how to efficiently solve the surface gateway deployment optimization problem, using heuristic approaches. The results of applying these proposed algorithms to a variety of practical deployment scenarios suggest that these heuristics are nearly optimal for practical cases.

Scheduling real time parallel structures on cluster computing with possible processor failures

Efficient task scheduling is essential for achieving high performance computing applications for distributed systems. Most of existing real-time systems consider schedulability as a main goal and ignores other effects such as machines failures. In This work we develop an algorithm to efficiently schedule parallel task graphs (fork-join structures). Our scheduling algorithm considers more than one factor at the same time. These factors are scheduability, reliability of the participating processors and achieved degree of parallelism. To achieve most of these goals, we composed an objective function that combines these different factors simultaneously. The proposed objective function is adjustable to provide the user with a way to prefer one factor to the others. The simulation results indicate that our algorithm produces schedules where the applications deadlines are met, reliability is maximized and the application parallelism is exploited.

Scheduling real time parallel structure on cluster computing

Scheduling a large number of high performance computing applications on a cluster-computing environment is a complex task. This becomes more critical in real time systems. Efficient scheduling strategies are critically important to achieving good performance. A cluster scheduler without enough knowledge of the state of the cluster and the scheduled tasks cannot adequately manage the cluster resources. Accordingly, the available processing power of the participating nodes may experience uncontrolled fragmentation. Thus, some of the submitted applications may be rejected due to tasks missing their deadlines. The literature on scheduling real-time task graphs is much less extensive, especially for providing timing guarantees while maximizing the processing power utilization. In this paper, we present a framework for allocating and scheduling real-time applications represented as parallel task graphs on a cluster. We utilize the available processing power on each processor to accommodate as many tasks as possible while satisfying the required deadline of each task. The algorithm also reduces the communication cost among tasks and the possibility of processing power fragmentation.

Hierarchical performance modeling for distributed system architectures

Performance modeling and evaluation techniques are essential when designing and implementing distributed software systems. Constructing performance models for such systems can require significant effort. This paper presents Hierarchical Performance Modeling as a technique to model performance for different layers of abstraction. Once the system architecture and software functionality have been specified, this model supports performance model generation for the evaluation and analysis of computation delays of software processes, communication delays of distributed software architectures, and hardware platform alternatives. A simplified example is presented to illustrate the concepts of the Hierarchical Performance Model.

Towards efficient dynamic surface gateway deployment for underwater network

In underwater acoustic sensor network, deploying multiple surface-level radio capable gateways is an efficient way to alleviate the burdens of high propagation delay and high error probability during transmission. However, the locations of gateways need to be carefully selected to maximize the benefit in a cost-effective way. In this paper, we present our formulation of the surface gateway deployment problem as an integer linear programming (ILP) and we solve the problem with heuristic approaches to provide a realtime solution for large scale deployment problems. By applying the proposed heuristic algorithms to a variety of deployment scenarios, we show that they are nearly optimal for practical cases, which opens the door for dynamic deployment. Therefore, we extend our solution to a dynamic case and propose a modified framework that integrates Aqua-sim, a NS2-based underwater wireless sensor network simulator. Our simulation result shows the benefits of dynamic gateway redeployment over static deployment.

An adaptive surface sink redeployment strategy for Underwater Sensor Networks

The performance of Underwater Sensor Networks (UWSNs) can be severely affected by the dynamics of underwater environment. A surface sink is usually deployed at a pre-specified location to maximize one or more performance metrics. However, when the network is dynamic, a redeployment of surface sink should be considered to reduce the effect of mobility on the network performance. Redeployment can be done periodically, at times based on a mobility prediction models, or adaptively based on performance degradation. Unnecessary redeployment can result from using the periodic or prediction based redeployment. In this paper we present an adaptive dynamic sink redeployment strategy that enforces redeployment only if a reduction in energy consumption is guaranteed. The redeployment decision is based on routing information collected at the surface sink throughout network operation. We use a location unaware routing protocol “adaptive power controlled routing protocol” as the underlying routing strategy. When the mobility of the network is not severe, nodes tend to use a fixed power level to communicate with neighboring nodes or surface sink. However, if more nodes are switching to use higher power levels for communication and the energy consumption is increased a sink redeployment procedure is started. Surface sink then triggers localization and finds the optimal new location of surface sink to minimize total energy consumption. Simulation results show that adaptive sink redeployment achieves a considerable reduction in energy consumption.

Many-to-Many Game-Theoretic Approach for the Measurement of Transportation Network Vulnerability

The vulnerability of a transportation network is strongly correlated with the ability of the network to withstand shocks and disruptions. A robust network with strategic redundancy allows traffic to be redistributed or reassigned without unduly compromising system performance. High-volume edges with limited alternative paths represent system vulnerabilities—a feature of transportation networks that has been exploited to identify critical components. A mixed-strategy, stochastic game-theoretic approach is presented for the measurement of network vulnerability. This method is designed to incorporate all origins and destinations in a network in a computationally efficient manner. The presented method differs from previous efforts in that it provides a many-to-many measure of vulnerability and edge-based disruptions that may not reside on a common path. A game that considers all possible origin–destination pairs is constructed between a router, which seeks minimum cost paths for travelers, and a network tester, which maximizes travel cost by disabling edges within the network. The method of successive averages is used for routing probabilities, and a weighted entropy function is employed to compute edge-disruption probabilities. The method is demonstrated on a small example network and then applied to the Sioux Falls, South Dakota, network. Results indicate good correspondence with a previous method that used equilibrium assignment and rapid solution convergence.