Driven by the explosive growth of information available online, the World-Wide-Web is currently witnessing a trend towards personalized information access. As part of this trend, numerous personalized news services are emerging. The goal of this project is to develop a prototype algorithm for using bookmarks to develop a personal profile. Ultimately, we imagine this might be used to construct a personalized RSS reader for reading news online. A reader returns a large number of news stories. To increase user satisfaction it is useful to rank them to bring the most interesting to the fore. This ranking is done by implementing a personalized profile. One way to create such a profile might be to extract it from users bookmarks. In this paper, we describe a process for learning user interest from bookmarks and present an evaluation of its effectiveness. The goal is to utilize a user profile based on bookmarks to personalize results by filtering and re-ranking the entries returned from a set of user defined feeds.

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications.

As the Internet moves towards a three-layer architecture consisting of overlay networks on top of the IP network layer on top of WDM-based physical networks, incorporating the interaction between and among network layers is crucial for efficient and effective implementation of survivability. <br/><br/> This dissertation has four major foci as follows: First, a first-of-its-kind analysis of the impact of overlay network dependency on the lower layer network unveils that backhaul, a link loop that occurs at any two or more lower layers below the layer where traffic is present, could happen. This prompts our proposal of a crosslayer survivable mapping to highlight such challenges and to offer survivability in an efficient backhaul-free way. The results demonstrate that the impact of layer dependency is more severe than initially anticipated making it clear that independent single layer network design is inadequate to assure service guarantees and efficient capacity allocation. Second, a forbidden link matrix is proposed masking part of the network for use in situations where some physical links are reserved exclusively for a designated service, mainly for the context of providing multiple levels of differentiation on the network use and service guarantee. The masking effect is evaluated on metrics using practical approaches in a sample real-world network, showing that both efficiency and practicality can be achieved. Third, matrix-based optimization problem formulations of several crosslayer survivable mappings are presented; examples on the link availability mapping are particularly illustrated. Fourth, survivability strategies for two-layer backbone networks where traffic originates at each layer are investigated. Optimization-based formulations of performing recovery mechanisms at each layer for both layers of traffic are also presented. Numerical results indicate that, in such a wavelength-based optical network, implementing survivability of all traffic at the bottom layer can be a viable solution with significant advantages. <br/><br/> This dissertation concludes by identifying a roadmap of potential future work for crosslayer survivability in layered network settings.

The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information. Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal. This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently. We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study. We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval. The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information.

Wireless ad hoc and sensor networks have received attention from research communities over the last several years. The ability to operate without a fixed infrastructure is suitable for a wide range of applications which in many cases require protection from security attacks. One of the first steps to provide security is to distribute cryptographic keys among nodes for bootstrapping security. The unique characteristics of ad hoc networks create a challenge in distributing keys among limited resource devices. In this dissertation we study the impact on secure connectivity achieved through key pre-distribution, of jamming attacks which form one of the easiest but efficient means for disruption of network connectivity. In response to jamming, networks can undertake different coping strategies (e.g., using power adaptation, spatial retreats, and directional antennas). Such coping techniques have impact in terms of the changing the initial secure connectivity created by secure links through key predistribution. The objective is to explore how whether predistribution techniques are robust enough for ad hoc/sensor networks that employ various techniques to cope with jamming attacks by taking into account challenges that arise with key predistribution when strategies for coping with jamming attacks are employed. In the first part of this dissertation we propose a hybrid key predistribution scheme that supports ad hoc/sensor networks that use mobility to cope with jamming attacks. In the presence of jamming attacks, this hybrid scheme provides high key connectivity while reducing the number of isolated nodes (after coping with jamming using spatial retreats). The hybrid scheme is a combination of random key predistribution and deployment-based key predistribution schemes that have complementary useful features for secure connectivity. In the second part we study performance of these key predistribution schemes under other jamming coping techniques namely power adaptation and directional antennas. We show that the combination of the hybrid key predistribution and coping techniques can help networks in maintaining secure connectivity even under jamming attacks.

Adaptive hypermedia is one of the most popular approaches of personalized information access. When the field started to emerge, the expectation was that soon nearly all published hypermedia content could be adapted to the needs, preferences, and abilities of its users. However, after a decade and a half, the gap between the amount of total hypermedia content available and the amount of content available in a personalized way is still quite large. In this work we are proposing a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content. To empirically prove the viability of our approach, we developed PERSEUS server of adaptation functionalities. First, we confirmed that the conceptual design of PERSEUS supports realization of a several of the widely used adaptive hypermedia techniques. Second, to demonstrate that the extracted adaptation does not create a significant computational bottleneck, we conducted a series of performance tests. The results show that PERSEUS is capable of providing a basis for implementing computationally challenging adaptation procedures and compares well with alternative, not-encapsulated adaptation solutions. As a result, even on modest hardware, large user populations can be served content adapted by PERSEUS.

Wireless Sensor Networks (WSNs) have unique characteristics which constrain them; including small energy stores, limited computation, and short range communication capability. Most traditional security algorithms use cryptographic primitives such as Public-key cryptography and are not optimized for energy usage. Employing these algorithms for the security of WSNs is often not practical. At the same time, the need for security in WSNs is unavoidable. Applications such as military, medical care, structural monitoring, and surveillance systems require information security in the network. As current security mechanisms for WSNs are not sufficient, development of new security schemes for WSNs is necessary. New security schemes may be able to take advantage of the unique properties of WSNs, such as the large numbers of nodes typical in these networks to mitigate the need for cryptographic algorithms and key distribution and management. However, taking advantage of these properties must be done in an energy efficient manner. The research examines how the redundancy in WSNs can provide some security elements. The research shows how multiple random delivery paths (MRDPs) can provide data integrity for WSNs. Second, the research employs multiple sinks to increase the total number of duplicate packets received by sinks, allowing sink voting to mitigate the packet discard rate issue of a WSN with a single sink. Third, the research examines the effectiveness of using multiple random paths in maintaining data confidentiality in WSNs. Last, the research examines the use of a rate limit to cope with packet flooding attacks in WSNs.

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.). Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications. In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g., high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance.

A fundamental problem in mobile ad hoc and unstructured sensor networks is maintaining connectivity. A network is connected if all nodes have a communication route (typically multi-hop) to each other. Maintaining connectivity is a challenge due to the unstructured nature of the network topology and the frequent occurrence of link and node failures due to interference, mobility, radio channel effects and battery limitations. In order to effectively deploy techniques to improve the resilience of sensor and mobile ad hoc networks against failures or attacks one must be able to identify all the weak points of a network topology. Here we define the weak or critical points of the topology as those links and nodes whose failure results in partitioning of the network. In this dissertation, we propose a set of algorithms to identify the critical points of a network topology. Utilizing these algorithms we study the behavior of critical points and the effect of using only local information in identifying global critical points. Then, we propose both local and global based resilient techniques that can improve the wireless network connectivity around critical points to lessen their importance and improve the network resilience. Next we extend the work to examine the network connectivity for heterogeneous wireless networks that can be result due to factors such as variations in transmission power and signal propagation environments and propose an algorithm to identify the connectivity of the network. We also propose two schemes for constructing additional links to enhance the connectivity of the network and evaluate the network performance of when a random interference factor occurs. Lastly, we implement our resilience techniques to improve the performance.

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata generation process, especially for semantic metadata. This research aims to find a method to categorize web resources using social tags as metadata. In this research, social tagging systems are a mechanism to allow non-professional catalogers to participate in metadata generation. Because social tags are not from a controlled vocabulary, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research examines ways to deal with those issues to obtain a set of tags representing the resource from the tags provided by users. Two measurements that measure the importance of a tag are introduced. Annotation Dominance (AD) is a measurement of how much a tag term is agreed to by users. Another is Cross Resources Annotation Discrimination (CRAD), a measurement to discriminate tags in the collection. It is designed to remove tags that are used broadly or narrowly in the collection. Further, the study suggests a process to identify and to manage compound tags. The research aims to select important annotations (meta-terms) and remove meaningless ones (noise) from the tag set. This study, therefore, suggests two main measurements for getting a subset of tags with classification potential. To evaluate the proposed approach to find classificatory metadata candidates, we rely on users relevance judgments comparing suggested tag terms and expert metadata terms. Human judges rate how relevant each term is on an n-point scale based on the relevance of each of the terms for the given resource.