<p>The cellular phone network has been increasing rapidly during the last years. For many people the mobile phone has become an every day gadget with a wide performance and functional range. The usage of technologies like GPRS, HSCSD, EDGE and UMTS as well as the bandwidth of networks and consequently the connectivity of the phones has also increased persistently. Coming along with that, three technologies, which are ubiquitous or pervasive computing, mobile and wireless networks and location-based technologies, are making rapid progress.</p><p>The aim of this thesis is to offer an architecture for a location-based user interface in the intersection of the three technologies mentioned above. The system should work with a minimum of special hardware requirement. Not to overload the user with information, the user interface should be adaptable, context-aware and location-based. The context-data should remain extendible and adaptable.</p>

In this thesis, we tackle the problem of designing and implementing a wireless video sensor network for a surveillance application. The goal was to design a low power content aware system that is able to take an image from an image sensor, determine blocks in the image that contain important information and encode those block for transmission thus reducing the overall transmission effort. At the same time, the encoder and the preprocessor must not consume so much computation power that the utility of this system is lost. We have implemented such a system which uses a combination of Edge Detection and Frame Differencing to determine useful information within an image. A JPEG encoder then encodes the important blocks for transmission. An implementation on a FPGA is presented in this work. This work demonstrates that preprocessing gives us a 48.6 % reduction in power for a single frame while maintaining a delivery ratio of above 85 % for the given set of test frames.

After a century of sporadic advances in equipment, tools, materials, and methods, the US construction industry still faces a low rate of productivity growth. To improve the productivity of any site activity, it is important to rapidly record relevant data about utilized resources and processes, as well as about the output quantities produced by these activities. There is sufficient evidence to suggest that activity-level productivity measurement is the premise for making any productivity improvement decision. To date, certain aspects of productivity measurement, such as input/output quantities, are partially automated through advanced project control systems. However, measuring the process of construction activities for productivity improvement remains an elusive goal for most construction companies. This is mostly due to the massive manual effort embedded in these data collection methods. Digital cameras are inexpensive devices that are widely used in the construction industry as an effective site observation method. This opens the door for conducting scientific method studies on complex operations through examining recorded videos. However, in the absence of an efficient video interpretation method, tedious manual reviewing is currently still required to extract productivity information from the recorded videos. This research aims to develop a computational methodology to rapidly and intelligently interpret construction videos into productivity information. It determines what elements can represent the steps and information flows in construction video interpretation. It identifies, develops, and evaluates computer vision algorithms to enable reliable visual recognition and tracking of construction resources in typical construction environments. It develops methods to enable context aware video computing. A software prototype, the Construction Video Analyzer, was developed and implemented based on this conceptual methodology. The proposed methodology was validated through using the developed prototype system to analyze five construction video sequences that record various types of construction operations. The Construction Video Analyzer was able to interpret these videos into productivity information with an accuracy that was close to manual analysis, without the limitations of onsite human observation. The developed methodology provides site management with a tool that can rapidly collect productivity data with greatly reduced manual efforts. / text

The current prospects in wired and wireless access networks, it is becoming increasingly important to address potential convergence in order to offer integrated broadband services. These systems will need to offer higher data transmission capacities and long battery life, which is the catalyst for an everincreasing variety of air interface technologies targeting local area to wide area connectivity. Current integrated industrial networks do not offer application aware context delivery and enhanced services for optimised networks. Application aware services provide value-added functionality to business applications by capturing, integrating, and consolidating intelligence about users and their endpoint devices from various points in the network. This thesis mainly intends to resolve the issues related to ubiquitous application aware service, fair allocation of radio access, reduced energy consumption and improved capacity. A technique that measures and evaluates the data rate demand to reduce application response time and queuing delay for multi radio interfaces is proposed. The technique overcomes the challenges of network integration, requiring no user intervention, saving battery life and selecting the radio access connection for the application requested by the end user. This study is split in two parts. The first contribution identifies some constraints of the services towards the application layer in terms of e.g. data rate and signal strength. The objectives are achieved by application controlled handover (ACH) mechanism in order to maintain acceptable data rate for real-time application services. It also looks into the impact of the radio link on the application and identifies elements and parameters like wireless link quality and handover that will influence the application type. It also identifies some enhanced traditional mechanisms such as distance controlled multihop and mesh topology required in order to support energy efficient multimedia applications. The second contribution unfolds an intelligent application priority assignment mechanism (IAPAM) for medical applications using wireless sensor networks. IAPAM proposes and evaluates a technique based on prioritising multiple virtual queues for the critical nature of medical data to improve instant transmission. Various mobility patterns (directed, controlled and random waypoint) has been investigated and compared by simulating IAPAM enabled mobile BWSN. The following topics have been studied, modelled, simulated and discussed in this thesis: 1. Application Controlled Handover (ACH) for multi radios over fibre 2. Power Controlled Scheme for mesh multi radios over fibre using ACH 3. IAPAM for Biomedical Wireless Sensor Networks (BWSN) and impact of mobility over IAPAM enabled BWSN. Extensive simulation studies are performed to analyze and to evaluate the proposed techniques. Simulation results demonstrate significant improvements in multi radios over fibre performance in terms of application response delay and power consumption by upto 75% and 15 % respectively, reduction in traffic loss by upto 53% and reduction in delay for real time application by more than 25% in some cases.

This thesis explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by a genetic algorithm (GA). The GA is used to represent adaptive behavior of the enemy when the friendly force has deployed our border security at a maximum level. By using a near optimal Latin hypercube design, our simulation runs are implemented efficiently and the border security system is analyzed using four different kinds of measures of effectiveness. / text

The increasing ubiquity of chip multiprocessor machines has made the need for accessible approaches to parallel programming all the more urgent. The current state of the art, based on threads and locks, requires the programmer to use mutual exclusion to protect shared resources, enforce invariants, and maintain consistency constraints. Despite decades of research effort, this approach remains fraught with difficulty. Lock-based programming is complex and error-prone, largely due to well-known problems such as deadlock, priority inversion, and poor composability. Tradeoffs between performance and complexity for locks remain unattractive. Coarse-grain locking is simple but introduces artificial sharing, needless serialization, and yields poor performance. Fine-grain locking can address these issues, but at a significant cost in complexity and maintainability. Transactional memory has emerged as a technology with the potential to address this need for better parallel programming tools. Transactions provide the abstraction of isolated, atomic execution of critical sections. The programmer specifies regions of code which access shared data, and the system is responsible for executing that code in a way that is isolated and atomic. The programmer need not reason about locks and threads. Transactional memory removes many of the pitfalls of locking: transactions are livelock- and deadlock-free and may be composed freely. Hardware transactional memory, which is the focus of this thesis, provides an efficient implementation of the TM abstraction. This thesis explores several key aspects of supporting hardware transactional memory (HTM): operating systems support and integration, architectural, design, and implementation considerations, and programmer-transparent techniques to improve HTM performance in the presence of contention. Using and supporting HTM in an OS requires innovation in both the OS and the architecture, but enables practical approaches and solutions to some long-standing OS problems. Innovations in transactional cache coherence protocols enable HTM support in the presence of multi-level cache hierarchies, rich HTM semantics such as suspend/resume and multiple transactions per thread context, and can provide the building blocks for support of flexible contention management policies without the need to trap to software handlers. We demonstrate a programmer-transparent hardware technique for using dependences between transactions to commit conflicting transactions, and suggest techniques to allow conflicting transactions to avoid performance-sapping restarts without using heuristics such as backoff. Both mechanisms yield better performance for workloads that have significant write-sharing. Finally, in the context of the MetaTM HTM model, this thesis contributes a high-fidelity cross-design comparison of representative proposals from the literature: the result is a comprehensive exploration of the HTM design space that compares the behavior of models of MetaTM (70, 75), LogTM (58, 94), and Sun's Rock (22). / text

RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.This dissertation research focuses on (a) advancing the representation and management of the RFID-collected shopping path data; (b) analyzing, modeling and predicting customer shopping activities with a spatial pattern discovery approach and a dynamic probabilistic modeling based methodology to enable advanced spatial business intelligence. The spatial pattern discovery approach identifies similar consumers based on a similarity metric between consumer shopping paths. The direct applications of this approach include a novel consumer segmentation methodology and an in-store real-time product recommendation algorithm. A hierarchical decision-theoretic model based on dynamic Bayesian networks (DBN) is developed to model consumer in-store shopping activities. This model can be used to predict a shopper's purchase goal in real time, infer her shopping actions, and estimate the exact product she is viewing at a time. We develop an approximate inference algorithm based on particle filters and a learning procedure based on the Expectation-Maximization (EM) algorithm to perform filtering and prediction for the network model. The developed models are tested on a real RFID-collected shopping trip dataset with promising results in terms of prediction accuracies of consumer purchase interests.This dissertation contributes to the marketing and information systems literature in several areas. First, it provides empirical insights about the correlation between spatial movement patterns and consumer purchase interests. Such correlation is demonstrated with in-store shopping data, but can be generalized to other marketing contexts such as store visit decisions by consumers and location and category management decisions by a retailer. Second, our study shows the possibility of utilizing consumer in-store movement to predict consumer purchase. The predictive models we developed have the potential to become the base of an intelligent shopping environment where store managers customize marketing efforts to provide location-aware recommendations to consumers as they travel through the store.

Promised some decades ago by researchers in artificial intelligence and robotics as an imminent breakthrough in our everyday lives, a robotic assistant that could work with us in our home and our workplace is a dream still far from being fulfilled. The work presented in this thesis aims at bringing this future vision a little closer to realization. Here, we start from the assumption that an efficient robotic helper should not impose constraints on users' activities, but rather perform its tasks unobtrusively to fulfill its goals and to facilitate people in achieving their objectives.  Also, the helper should be able to consider the outcome of possible future actions by the human users, to assess how those would affect the environment with respect to the agent's objectives, and to predict when its support will be needed. In this thesis we address two highly interconnected problems that are essential for the cohabitation of people and service robots: robot task planning and human activity recognition. First, we present human-aware planning, that is, our approach to robot high-level symbolic reasoning for plan generation. Human-aware planning can be applied in situations where there is a controllable agent, the robot, whose actions we can plan, and one or more uncontrollable agents, the human users, whose future actions we can only try to predict. In our approach, therefore, the knowledge of the users' current and future activities is an important prerequisite. We define human-aware as a new type of planning problem, we formalize the extensions needed by a classical planner to solve such a problem, and we present the implementation of a planner that satisfies all identified requirements. In this thesis we explore also a second issue, which is a prerequisite to the first one: human activity monitoring in intelligent environments. We adopt a knowledge driven approach to activity recognition, whereby a constraint-based domain description is used to correlate sensor readings to human activities. We validate our solutions to both human-aware planning and activity recognition both theoretically and experimentally, describing a number of explanatory examples and test runs in a real environment.

Context-aware systems can dynamically adapt to user situations to provide smarter services. In general, context refers to the information that can be used to characterize these situations, and context models are deployed to specify contextual information described in context-aware systems. However, even though user context is highly dynamic, existing context models either focus on modeling static views of context or lack appropriate design abstractions to deal with dynamic aspects and interactions involving contextual elements such location, time, user roles, social relationships, and changing preferences. Moreover, virtual environments have not been modelled by most of the existing context models even though online interaction is very common and popular. This thesis presents a dynamic user-centric context model that can be used to model the aspects of context-aware systems that are subject to frequent change. Four case studies are proposed to illustrate the applicability of the approach taken by this thesis, and they are in the domains of mobile e-healthcare, mobile commerce, mobile tourism, and mobile augmented reality gaming. Benefits of the proposed model include avoiding the development of context-aware systems from scratch, enabling future use of model-driven approaches, and reducing implementation effort.

Service providers’ goals include providing reliable connections with the minimum allocated resources over a shared-mesh path restoration scheme in WDM networks. However, in some cases, the requested parameters in an SLA are beyond the capacity of the network, and the connection is typically blocked. To give the customer a chance to choose another provider, or in the case of having only one provider, to comply with the provider’s network capacity, new SLA-based architectures and mechanisms are required to be introduced to provide better service to prioity-aware shared mesh WDM networks. To achieve this goal, the dissertation’s contributions focus on three main characteristics of the network design: i) A dynamic SLA negotiation infrastructure to negotiate and propagate crucial SLA parameters, ii) Path attributes which can provide a better picture of network resources and status and are suitable to be propagated by the negotiating system, and iii) Algorithms benefiting from the path attributes to improve the blocking probability and resource utilization of the network. To fulfill the first goal of the contributions, a dynamic SLA negotiation mechanism for both intra and inter-domain communications using OSPF and BGP protocols is proposed. Link attributes via intra-domain, and new proposed TE path attributes through inter-domain mechanisms are advertised. Several novel path constraints and attributes are proposed which are dynamically updated and propagated through the network over the connections provisioning process period to satisfy the second objective of the contributions in this dissertation. The path availability, holding time, SLA violation risk, and path risk factor are the important characteristics of the proposed path attributes. As the third goal considered for the contributions, novel priority-aware algorithms and SLA-based mechanisms are proposed to improve the network performance for different traffic types of various priority classes. The algorithms and mechanisms proposed in this thesis take advantage of the new path attributes and SLA negotiation infrastructure to better serve high-priority connection requests at the lowest cost. The mechanisms and network architectures proposed in this work are a solution for the high-priority requests that normally cannot be accommodated as they violate the best availability offered by service providers.