Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Understanding the Reaction Mechanism of Nanocomposite Thermites

Nanocomposite energetics are a relatively new class of materials that combine nanoscale fuels and oxidizers to allow for the rapid release of large amounts of energy. In thermite systems (metal fuel with metal oxide oxidizer), the use of nanomaterials has been illustrated to increase reactivity by multiple orders of magnitude as a result of the higher specific surface area and smaller diffusion length scales. However, the highly dynamic and nanoscale processes intrinsic to these materials, as well as heating rate dependencies, have limited our understanding of the underlying processes that control reaction and propagation. For my dissertation, I have employed a variety of experimental approaches that have allowed me to probe these processes at heating rates representative of free combustion with the goal of understanding the fundamental mechanisms. Dynamic transmission electron microscopy (DTEM) was used to study the in situ morphological change that occurs in nanocomposite thermite materials subjected to rapid (10^11 K/s) heating. Aluminum nanoparticle (Al-NP) aggregates were found to lose their nanostructure through coalescence in as little as 10 ns, which is much faster than any other timescale of combustion. Further study of nanoscale reaction with CuO determined that a condensed phase interfacial reaction could occur within 0.5-5 µs in a manner consistent with bulk reaction, which supports that this mechanism plays a dominant role in the overall reaction process. Ta nanocomposites were also studied to determine if a high melting point (3280 K) affects the loss of nanostructure and rate of reaction. The condensed phase reaction pathway was further explored using reactive multilayers sputter deposited onto thin Pt wires to allow for temperature jump (T-Jump) heating at rates of ~5x10^5 K/s. High speed video and a time of flight mass spectrometry (TOFMS) were used to observe ignition temperature and speciation as a function of bilayer thickness. The ignition process was modeled and a low activation energy for effective diffusivity was determined. T-Jump TOFMS along with constant volume combustion cell studies were also used to determine the effect of gas release in nanoparticle systems by comparing the reaction properties of CuO and Cu2O.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.

Understanding Urban Stormwater Denitrification in Bioretention Internal Water Storage Zones

Free-draining bioretention systems commonly demonstrate poor nitrate removal. In this study, column tests verified the necessity of a permanently saturated zone to target nitrate removal via denitrification. Experiments determined a first-order denitrification rate constant of 0.0011 min-1 specific to Willow Oak woodchip media. A 2.6-day retention time reduced 3.0 mgN/L to below 0.05 mg-N/L. During simulated storm events, hydraulic retention time may be used as a predictive measurement of nitrate fate and removal. A minimum 4.0 hour retention time was necessary for in-storm denitrification defined by a minimum 20% nitrate removal. Additional environmental parameters, e.g., pH, temperature, oxidation-reduction potential, and dissolved oxygen, affect denitrification rate and response, but macroscale measurements may not be an accurate depiction of denitrifying biofilm conditions. A simple model was developed to predict annual bioretention nitrate performance. Novel bioretention design should incorporate bowl storage and large subsurface denitrifying zones to maximize treatment volume and contact time.

Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)

As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system