<p> Smart communications devices are giving users instant access to applications that consume large amounts of data. These applications have different requirements on the network for delivery of data. In order to support these different applications, operators are required to support multiple service classes. </p><p> Given the regulatory and technology constraints and the relatively high cost associated with wireless spectrum licensing and utilization, demand will exceed supply leading to congestion and overload conditions. In addition to new broadband radio technologies offering higher data rates, operators are looking at deploying alternate heterogeneous technologies, such as WLAN, to provide additional bandwidth for serving customers. It is expected that this will still fall short of providing enough network resources to meet the ITU requirement for 1% new call blocking probability. An economic mechanism that offers incentives to individuals for rational behavior is required in order in order to reduce the demand for network resources and resolve the congestion problem. </p><p> The research in this dissertation demonstrates that the integration of a dynamic pricing with connection admission control mechanism for an operator deploying cooperative heterogeneous networks (e.g., LTE and WLAN) offering multiple QoS service classes reduces the new call blocking probability to the required 1% level. </p><p> The experimental design consisted, first, of an analytical model of the CAC algorithm with dynamic pricing in a heterogeneous environment. The analytical model was subsequently validated through discrete-event simulation using Matlab. </p>

<p> The High-Definition Multimedia Interface (HDMI) is widely adopted by the modern media devices, and supporting HDMI source is required in many streaming and broadcasting solutions. This paper introduces an HDMI frame grabber design which will capture the image from an HDMI source, and then display the image on both a local HDMI display and a remote computer screen. As a result, the captured frame will be shared in the different locations across the network. The project design involves the embedded hardware/software development on a Xilinx FPGA board, and GUI/Network programming on the Microsoft .NET framework. The paper describes the embedded design to enable the HDMI and Ethernet peripherals on the FPGA board, analyzes the data format and protocol difference among the network nodes, and then introduces the method to convert and transport the data.</p><p> <i>Keywords:</i> Embedded Design, FPGA, HDMI, lwIP, Network Programming.</p>

<p> As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system.</p><p> Our first goal is to emulate natural interventions provided by a caregiver to individuals with memory impairments, by using a supervised machine learning approach to classify pre-segmented activity steps into prompt or no-prompt classes. However, the lack of training examples representing prompt situations causes imbalanced class distribution. We proposed two probabilistic oversampling techniques, RACOG and wRACOG, that help in better learning of the "prompt" class. Moreover, there are certain prompt situations where the sensor triggering signature is quite similar to the situations when the participant would probably need no prompt. The absence of sufficient data attributes to differentiate between prompt and no-prompt classes causes class overlap. We propose ClusBUS, a clustering-based under-sampling technique that identifies ambiguous data regions. ClusBUS preprocesses the data in order to give more importance to the minority class during classification.</p><p> Our second goal is to automatically detect activity errors in real time, while an individual performs an activity. We propose a collection of one-class classification-based algorithms, known as DERT, that learns only from the normal activity patterns and without using any training examples for the activity errors. When evaluated on unseen activity data, DERT is able to identify abnormalities or errors, which can be potential prompt situations. We validate the effectiveness of the proposed algorithms in predicting potential prompt situations on the sensor data of ten activities of daily living, collected from 580 participants, who were part of two smart home studies.</p>

<p> In the static analysis of functional programs, control-flow analysis (&kappa;-CFA) is a classic method of approximating program behavior as a finite state automata. CFA2 and abstract garbage collection are two recent, yet orthogonal improvements, on &kappa;-CFA. CFA2 approximates program behavior as a pushdown system, using summarization for the stack. CFA2 can accurately approximate arbitrarily-deep recursive function calls, whereas &kappa;-CFA cannot. Abstract garbage collection removes unreachable values from the store/heap. If unreachable values are not removed from a static analysis, they can become reachable again, which pollutes the final analysis and makes it less precise. Unfortunately, as these two techniques were originally formulated, they are incompatible. CFA2's summarization technique for managing the stack obscures the stack such that abstract garbage collection is unable to examine the stack for reachable values.</p><p> This dissertation presents introspective pushdown control-flow analysis, which manages the stack explicitly through stack changes (pushes and pops). Because this analysis is able to examine the stack by how it has changed, abstract garbage collection is able to examine the stack for reachable values. Thus, introspective pushdown control-flow analysis merges successfully the benefits of CFA2 and abstract garbage collection to create a more precise static analysis.</p><p> Additionally, the high-performance computing community has viewed functional programming techniques and tools as lacking the efficiency necessary for their applications. Nebo is a declarative domain-specific language embedded in C++ for discretizing partial differential equations for transport phenomena. For efficient execution, Nebo exploits a version of expression templates, based on the C++ template system, which is a type-less, completely-pure, Turing-complete functional language with burdensome syntax. Nebo's declarative syntax supports functional tools, such as point-wise lifting of complex expressions and functional composition of stencil operators. Nebo's primary abstraction is mathematical assignment, which separates what a calculation does from how that calculation is executed. Currently Nebo supports single-core execution, multicore (thread-based) parallel execution, and GPU execution. With single-core execution, Nebo performs on par with the loops and code that it replaces in Wasatch, a pre-existing high-performance simulation project. With multicore (thread-based) execution, Nebo can linearly scale (with roughly 90% efficiency) up to 6 processors, compared to its single-core execution. Moreover, Nebo's GPU execution can be up to 37x faster than its single-core execution. Finally, Wasatch (the pre-existing high-performance simulation project which uses Nebo) can scale up to 262K cores.</p>

<p> Wireless communication became popular in the last decades, giving the mobility to the users. However with increased number of users and contention, network efficiency can hardly keep up with user needs. This thesis focuses on a new frequency domain contention technique called FICA. In FICA, the channel is assumed to be using Orthogonal Frequency Division Multiplex (OFDM) with multiple sub-carriers. We investigated the use of multiple channels and multiple access points (APs) in the design. First we investigated having one channel that is divided into number of sub-carriers, it shows good result, but only for limited number of users. Therefore we worked on the second scenario of having several sub-channels and each sub-channel is divided into a number of sub-carriers to communicate through one AP. And for efficient result nodes contend on the contention band and winner nodes will have the chance to send their data through the transmission band. In real world, networks have more than one AP, for that reason we investigate the third scenario, which is having more than one AP. In this setup, the result showed significant outcome, that we can divide the channel into several sub-channels to serve more than one AP and hash an ID for each AP. We further investigated optimal number of ID bits that are used to represent the hashed receiver IDs. We summarize the results as following: 1) it is possible to divide the channel bandwidth into several sub-channels that is divided into several sub-carriers to serve large number of users. 2) node contention should be partitioned into contention band and transmission band to reduce the overhead that the contending node cause when contending on the whole channel. 3) AP ID is required when the network has more than one AP. 4) number of sub-carriers in the contention band has to increase at least to the double for higher efficiency, since more AP on the network would make the channel more loaded. 5) AP ID can be anything between 20-40 bits. Decreasing the ID to less than 40bits did not affect the throughput and efficiency of the channel.</p>

<p> Keeping track of requirements from eliciting data to making decision needs an effective path from data to decision [43]. Visualization science helps to create this path by extracting insights from flood of data. Model helps to shape the transformation of data to visualization. Defect Detection and Prevention model was created to assess quality assurance activities. We selected DDP and started enhancing user interactivity with requirements visualization over basic DDP with implementing a visual requirements analytics framework. By applying GQM table to our framework, we added six visualization features to the existing visual requirements visualization approaches. We applied this framework to technical and non-technical stakeholder scenarios to gain the operational insights of requirements-driven risk mitigation in practice. The combination of the first and second scenarios' result presented the multiple stakeholders scenario result which was a small number of strategies from kept tradespase with common mitigations that must deploy to the system.</p>

<p> In recent years there has been a proliferation of research on a number of wireless multi-hop networks that include mobile ad-hoc networks, wireless mesh networks, and wireless sensor networks (WSNs). Routing protocols in such networks are often required to meet design objectives that include a combination of factors such as throughput, delay, energy consumption, network lifetime etc. In addition, many modern wireless networks are equipped with multi-channel radios, where channel selection plays an important role in achieving the same design objectives. Consequently, addressing the routing problem together with <i>cross-layer</i> adaptations such as channel selection is an important issue in such networks. In this work, we study the joint routing and channel selection problem that spans two domains of wireless networks. The first is a cost-effective and scalable wireless-optical access networks which is a combination of high-capacity optical access and unethered wireless access. The joint routing and channel selection problem in this case is addressed under an anycasting paradigm. In addition, we address two other problems in the context of wireless-optical access networks. The first is on optimal gateway placement and network planning for serving a given set of users. And the second is the development of an analytical model to evaluate the performance of the IEEE 802.11 DCF in radio-over- fiber wireless LANs. The second domain involves resource constrained WSNs where we focus on route and channel selection for network lifetime maximization. Here, the problem is further exacerbated by distributed power control, that introduces additional design considerations. Both problems involve cross-layer adaptations that must be solved together with routing. Finally, we present an analytical model for lifetime calculation in multi-channel, asynchronous WSNs under optimal power control.</p>

<p> Provisioning of rich routing building blocks to mobile ad-hoc networking applications has been of high interest. Several multi-hop wireless network applications need flexibility in describing paths their traffic will follow. To accommodate this need, previous work has proposed several viable routing schemes such as Dynamic Source Routing (DSR) and Trajectory-Based Routing (TBR). However, tradeoffs involved in the interaction of these routing schemes and the application-specific requirements or constraints have not been explored. Particularly, techniques to help the application to do the right routing choices based on a desired metric are much needed. Depending on the application's goals, routing choices should be steered for different metrics rather than the traditional notion of shortest-path in terms of distance. For instance, obstacle or hostility avoidance would require "accurate" paths, end-to-end traffic engineering/balancing would require "minimum utilization" paths, low delay routing for multimedia traffic would require "short distance" paths, and, finally, low loss routing for reliable end-to-end transfers would require "minimum congestion" paths. Our focus in this dissertation is the "accuracy" of paths. </p><p> First, we consider techniques that minimize routing protocol state costs under application-based constraints. We study the constraint of "accuracy" of the application's desired route, as this constraint provides a range of choices to the applications. As a crucial part of this optimization framework, we investigate the tradeoff between the packet header size and the network state. We, then, apply our framework to the case of TBR with application-based accuracy constraints in obeying a given trajectory and show that approximating trajectories under such accuracy constraints is NP-hard. We develop heuristics solving this problem and illustrate their performance. </p><p> Second, we take our TBR framework to a more general solution by adding automated trajectory generator and end-to-end traffic engineering support. We focus on the context of multi-hop wireless protocols for which application-specific needs are emphasized along with a highly dynamic underlying network environment. We propose a framework supporting a standardized way of interfacing between the network routing and the wireless applications. We use this framework to develop a roadmap-based trajectory planning scheme to engineer the end-to-end traffic over multi-hop wireless networks. We illustrate how our roadmap-based approach can automate the process of planning/selecting the trajectories so that better balancing of the traffic is achieved. We compare our roadmap-based trajectory planning approach to its shortest-path routing counterpart, Greedy Parameter Stateless Routing (GPSR), and show that beneficial tradeoffs can be attained.</p>

Thesis (PH.D.) -- Syracuse University, 2007. / "Publication number AAT 3281725"

Thesis (Ph.D. in Software Engineering)--Naval Postgraduate School, June 2008. / Thesis Advisor(s): Michael, James B. "June 2008." Description based on title screen as viewed on August 25, 2008. Includes bibliographical references (p. 155-158). Also available in print.