Simulation and analysis of network traffic for efficient and reliable information transfer

With the growing commercial importance of the Internet and the development of new real-time, connection-oriented services like IP-telephony and electronic commerce resilience is becoming a key issue in the design of TP-based networks. Two emerging technologies, which can accomplish the task of efficient information transfer, are Multiprotocol Label Switching (MPLS) and Differentiated Services. A main benefit of MPLS is the ability to introduce traffic-engineering concepts due to its connection-oriented characteristic. With MPLS it is possible to assign different paths for packets through the network. Differentiated services divides traffic into different classes and treat them differently, especially when there is a shortage of network resources. In this thesis, a framework was proposed to integrate the above two technologies and its performance in providing load balancing and improving QoS was evaluated. Simulation and analysis of this framework demonstrated that the combination of MPLS and Differentiated services is a powerful tool for QoS provisioning in IP networks.

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

Energy-efficient self -organization of wireless acoustic sensor networks for ground target tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Developing an enhanced model for combined heat and air infiltration energy simulation

The need for efficient, sustainable, and planned utilization of resources is ever more critical. In the U.S. alone, buildings consume 34.8 Quadrillion (1015) BTU of energy annually at a cost of $1.4 Trillion. Of this energy 58% is utilized for heating and air conditioning. ^ Several building energy analysis tools have been developed to assess energy demands and lifecycle energy costs in buildings. Such analyses are also essential for an efficient HVAC design that overcomes the pitfalls of an under/over-designed system. DOE-2 is among the most widely known full building energy analysis models. It also constitutes the simulation engine of other prominent software such as eQUEST, EnergyPro, PowerDOE. Therefore, it is essential that DOE-2 energy simulations be characterized by high accuracy. ^ Infiltration is an uncontrolled process through which outside air leaks into a building. Studies have estimated infiltration to account for up to 50% of a building's energy demand. This, considered alongside the annual cost of buildings energy consumption, reveals the costs of air infiltration. It also stresses the need that prominent building energy simulation engines accurately account for its impact. ^ In this research the relative accuracy of current air infiltration calculation methods is evaluated against an intricate Multiphysics Hygrothermal CFD building envelope analysis. The full-scale CFD analysis is based on a meticulous representation of cracking in building envelopes and on real-life conditions. The research found that even the most advanced current infiltration methods, including in DOE-2, are at up to 96.13% relative error versus CFD analysis. ^ An Enhanced Model for Combined Heat and Air Infiltration Simulation was developed. The model resulted in 91.6% improvement in relative accuracy over current models. It reduces error versus CFD analysis to less than 4.5% while requiring less than 1% of the time required for such a complex hygrothermal analysis. The algorithm used in our model was demonstrated to be easy to integrate into DOE-2 and other engines as a standalone method for evaluating infiltration heat loads. This will vastly increase the accuracy of such simulation engines while maintaining their speed and ease of use characteristics that make them very widely used in building design.^

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Docking and simulation study of the enantiospecificity of chiral epoxidation reactions catalyzed by chloroperoxidase

Chloroperoxidase (CPO), a 298-residue glycosylated protein from the fungus Caldariomyces fumago, is probably the most versatile heme enzyme yet discovered. Interest in CPO as a catalyst is based on its power to produce enantiomerically enriched products. Recent research has focused its attention on the ability of CPO to epoxidize alkenes in high regioselectivity and enantioselectivity as an efficient and environmentally benign alternative to traditional synthetic routes. There has been little work on the nature of ligand binding, which probably controls the regio- and enantiospecifity of CPO. Consequently it is here that we focus our work. We report docking calculations and computer simulations aimed at predicting the enantiospecificity of CPO-catalyzed epoxidation of three model substrates. On the basis of this work candidate mutations to improve the efficiency of CPO are predicted. In order to accomplish these aims, a simulated annealing and molecular dynamics protocol is developed to sample potentially reactive substrate/CPO complexes.

Evaluating the effectiveness of signal timing optimization based on microscopic simulation evaluation

The optimization of the timing parameters of traffic signals provides for efficient operation of traffic along a signalized transportation system. Optimization tools with macroscopic simulation models have been used to determine optimal timing plans. These plans have been, in some cases, evaluated and fine tuned using microscopic simulation tools. A number of studies show inconsistencies between optimization tool results based on macroscopic simulation and the results obtained from microscopic simulation. No attempts have been made to determine the reason behind these inconsistencies. This research investigates whether adjusting the parameters of macroscopic simulation models to correspond to the calibrated microscopic simulation model parameters can reduce said inconsistencies. The adjusted parameters include platoon dispersion model parameters, saturation flow rates, and cruise speeds. The results from this work show that adjusting cruise speeds and saturation flow rates can have significant impacts on improving the optimization/macroscopic simulation results as assessed by microscopic simulation models.

Developing an Enhanced Model for Combined Heat and Air Infiltration Energy Simulation

The need for efficient, sustainable, and planned utilization of resources is ever more critical. In the U.S. alone, buildings consume 34.8 Quadrillion (1015) BTU of energy annually at a cost of $1.4 Trillion. Of this energy 58% is utilized for heating and air conditioning. Several building energy analysis tools have been developed to assess energy demands and lifecycle energy costs in buildings. Such analyses are also essential for an efficient HVAC design that overcomes the pitfalls of an under/over-designed system. DOE-2 is among the most widely known full building energy analysis models. It also constitutes the simulation engine of other prominent software such as eQUEST, EnergyPro, PowerDOE. Therefore, it is essential that DOE-2 energy simulations be characterized by high accuracy. Infiltration is an uncontrolled process through which outside air leaks into a building. Studies have estimated infiltration to account for up to 50% of a building’s energy demand. This, considered alongside the annual cost of buildings energy consumption, reveals the costs of air infiltration. It also stresses the need that prominent building energy simulation engines accurately account for its impact. In this research the relative accuracy of current air infiltration calculation methods is evaluated against an intricate Multiphysics Hygrothermal CFD building envelope analysis. The full-scale CFD analysis is based on a meticulous representation of cracking in building envelopes and on real-life conditions. The research found that even the most advanced current infiltration methods, including in DOE-2, are at up to 96.13% relative error versus CFD analysis. An Enhanced Model for Combined Heat and Air Infiltration Simulation was developed. The model resulted in 91.6% improvement in relative accuracy over current models. It reduces error versus CFD analysis to less than 4.5% while requiring less than 1% of the time required for such a complex hygrothermal analysis. The algorithm used in our model was demonstrated to be easy to integrate into DOE-2 and other engines as a standalone method for evaluating infiltration heat loads. This will vastly increase the accuracy of such simulation engines while maintaining their speed and ease of use characteristics that make them very widely used in building design.