Master of Science / Computing and Information Sciences / Daniel A. Andresen / The growing needs of different products is only being increased from day to day and among these numerous products that each person plan to purchase, it has become a tedious task to keep track of all the products that should be purchased. One such important thing is that, everyone wish to keep track of an item when the location associated with the item is nearby. We have many To-Do applications where we can just note down our day to day needs and things to get. But we might face situations like forgetting to keep track of what we have in our To Do list related to buying an item and when the location associated to the item is nearby, there are high possibilities that we overlook and just forget about purchasing these items. These situations occurred most of the times when I did a small survey among my friends. This difficulty has lead me to rethink and find a solution. The usage of smart phones has become very common these days. Android market which is an open source has helped many people to develop their own applications and these could easily be run on Android smart phones. I thought of developing an android application that helps in tracking not only the To-Do list of the items that a person tracks down to purchase but also stores the location where that item can be purchased. This To-Do app then provide notifications when a person is nearby the location associated with the item. It also triggers an alarm so that the user can easily remember what item he has planned to get in that particular location. The proposed app tries to solve most of the problems by providing an intuitive interface to the user where the user can note down all their planned purchases with location of the products and get reminders about it when passing through that location. The Near Me application is about tracking the items to be purchased or the tasks that are to be done specific to the location. Each to-do item is associated with a date, location and notes. Storing the locations in the application helps the user with timely notifications and alarms according to the location the user is in and the tasks that should be done in that location. Also these To-Do items can also be synced with online storage application like Dropbox.

The transmitting of video content over Vehicular Ad Hoc Networks (VANETs) faces a great number of challenges caused by strict QoS (Quality of Service) requirements and highly dynamic network topology. In order to tackle these challenges, multipath forwarding schemes can be regarded as potential solutions. However, route coupling effect and the path length growth severely impair the performance of multipath schemes. In this thesis, the current research status about video streaming over VANETs as well as multipath transmissions are reviewed. With the demand to discover a more suitable solution, we propose the Location-Aware Multipath Video Streaming (LIAITHON+) protocol to address video streaming over urban VANETs. LIAITHON+ uses location information to discover relatively short paths with minimal route coupling effect. The performance results have shown it outperforms the underlying single path solution as well as the node-disjoint multipath solution. In addition, the impact of added redundancy on the multipath solution is investigated through LIAITHON+. According to the results, added redundancy has a different impact depending on the data rate.

One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

Master of Philosophy (Architecture) / Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences.

Master of Philosophy (Architecture) / Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences.

We present the high level (VHDL) modeling and high level synthesis of an ASIC (TOUR NAVIGATOR) for a portable hand held device - a tour guide. The tour guide is based on location-aware mobile computing, which gives the information of the current location to the user. The TOUR NAVIGATOR designed in this work is interfaced with off-the-shelf components to realise the tour guide system. The current location is given by an on-board GPS receiver chip. The TOUR NAVIGATOR is a search and play module which interfaces with the flash memory, GPS receiver and the audio codec. The functionality of the TOUR NAVIGATOR is to search the flash memory for audio data corresponding to the current GPS co-ordinate, which is an input to the TOUR NAVIGATOR. The look-up table containing the GPS coordinates and the corresponding audio files are loaded into the flash memory, where in each GPS entry in the table is indexed by the co-ordinates, and an audio file that contains information about the locations is associated with it. When there is a match, the audio file is streamed to the codec. The functionality of the interface of the TOUR NAVIGATOR with the memory module is verified at the RTL using Cadence-NCLaunch. The layout implementation of the TOUR NAVIGATOR is done using an automatic place and route tool (Silicon Ensemble), which uses standard cells for the entire design. Leakage power reduction is done by introducing sleep transistors in the standard cells. The TOUR NAVIGATOR is put into a "sleep" mode when there is no operation of the tour guide, thus giving significant power savings.

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.

We have simulated the effect of different parameters in location-aware information sharing policies for crowd-based information exchange systems. The purpose of this simulation was to find out which parameters improved the upload time, battery life and success rate for nodes trying to upload a large file under bad conditions. To test the effect of these parameters on a larger scale, we simulated an area where a large number of nodes were moving around. Our test results showed that nodes greatly improved their battery life and the upload time by limiting the number of nodes they send data to, rather than sharing data with all nodes within reach. However, sending the oldest collected data performed very bad in regards of battery life time and had a relatively high amount of nodes that did not manage to upload their file. We concluded that nodes should not share their data with all available nodes at all times, and be restrictive in the amount of data they share with other nodes to conserve battery.

Abstract The development of mobile and ubiquitous computing has recently been rapid. One of the most promising research and development fields has been location-aware services. These services are challenging as they require a lot of resources for generating maps from spatial data and for visualizing temporary data gathered from the environment. Applying location-awareness and ubiquitous computing in the industry is currently under intensive research and development activities especially because of signs of promising new business opportunities. This thesis studies the development and evaluation of a location-aware system platform called Locawe designed for indoor and outdoor conditions. This decentralized middleware-based platform has been developed at CENTRIA Research and Development, Ylivieska. The main objectives of this research have been primarily to study and develop new ubiquitous features for the Locawe platform with various software and hardware combinations. In addition, during these studies gathered experiences have been used in order to design new versions of the Locawe architecture. These ubiquitous features, covering the use of GPS, RFID and WSAN technologies, have been tested in five field experiments, one laboratory experiment and several industrial pilots. In these evaluations, the author has considered what software and hardware alternatives are feasible and appropriate for improving the newly developed features in the Locawe platform. These features have been evaluated from usability, visualization and communication techniques perspective. As a result the author presents in this thesis main characteristics in location-aware system development from system layers, ubiquitous computing, mobility, and restrictions perspective. The author also suggests that the architeture presented in this thesis enables location-aware system development in indoor and outdoor conditions. With respect of state-of-the-art platforms, the Locawe platform is, at the moment, in some sense unique, although only as a research prototype. This platform combines two approaches, namely research on location platforms, and research on geosensor networks or smart environments. In addition, this platform gives one answer how RFID or WSAN technologies could be integrated under one framework together with mobile devices. Finally, the author also proposes that the research and development approach presented in this thesis can be applied successfully in research and development organizations specialized in applied research. / Tiivistelmä Mobiilin ja sulautetun tietotekniikan kehitys on ollut viime aikoina nopeaa. Yksi lupaavimmista tutkimus- ja kehityskohteista on ollut paikkatietoiset palvelut. Näiden palveluiden kehittäminen on varsin haasteellista, koska kehittämistyö vaatii runsaasti resursseja sekä karttojen generoimisessa paikkatietoaineistoista että ympäristöstä kerättävän väliaikaisen tiedon visualisoinnissa. Paikkatietoisten sovellusten ja sulautetun tietotekniikan soveltaminen teollisuudessa on aktiivisen tutkimus- ja kehittämistoiminnan keskiössä, koska näköpiirissä on selvästi uusia lupaavia liiketoimintamahdollisuuksia. Tässä väitöskirjassa keskitytään Locawe-nimisen paikkatietoisen ohjelmistoalustan kehittämisen ja arvioinnin raportointiin. Locawe-alusta on suunniteltu sekä sisä- että ulko-olosuhteisiin. Tämä hajautettu väliohjelmistopohjainen alusta on kehitetty CENTRIA Tutkimus ja kehityksen Ylivieskan yksikössä. Väitöskirjan tavoitteena on ollut ensisijaisesti tutkia ja kehittää uusia, sulautettua tietotekniikkaa hyödyntäviä ominaisuuksia Locawe-alustaan hyödyntäen erilaisia ohjelmisto- ja laitteistokombinaatioita. Väitöskirjaan liittyvän tutkimustyön aikana käyttäjiltä on useaan otteeseen kerätty käyttökokemuksia. Kokemuksia on hyödynnetty Locawe-arkkitehtuurin seuraavien versioiden suunnittelussa. Uusia, sulautettua tietotekniikkaa hyödyntäviä ominaisuuksia on testattu kaikkiaan viidessä kenttätestissä, yhdessä laboratoriotestissä sekä useissa teollisissa piloteissa. Ominaisuuksien toteuttamisessa käytettiin GPS-, RFID- ja WSAN-teknologioita. Testeissä kirjoittaja arvioi, minkälaiset ohjelmisto- ja laitteistovaihtoehdot ovat toteuttamiskelpoisimpia ja soveliaimpia uusien ominaisuuksien kehittämiseksi Locawe-alustaan. Ominaisuuksia on arvioitu käytettävyyden, visualisoinnin ja viestintätekniikoiden näkökulmista. Tutkimustyön tuloksena kirjoittaja esittelee väitöskirjassa paikkatietoisten järjestelmien kehittämiseen liittyviä ominaispiirteitä järjestelmätasojen, sulautetun tietotekniikan, mobiliteetin ja rajoitteiden näkökulmista. Lisäksi kirjoittaja esittää väitöskirjassa arkkitehtuurin, joka mahdollistaa paikkatietoisen järjestelmän kehittämisen soveltuen niin sisä- kuin ulko-olosuhteisiin. Huomioiden paikkatietoisten alustojen tason maailmanlaajuisesti väitöskirjassa kuvattu Locawe-alusta on tutkimusprototyyppinäkin tällä hetkellä tietyssä määrin ainutlaatuinen. Alustan tutkimisessa on nimittäin keskitytty sekä paikkatietoisten alustojen että geosensoriverkkojen ja älykkäiden ympäristöjen tutkimiseen. Lisäksi Locawe-alusta tarjoaa yhden ratkaisun RFID ja WSAN teknologioiden integrointiin yhdeksi kehykseksi yhdessä mobiililaitteiden kanssa. Lopuksi kirjoittaja esittää, että väitöskirjassa esitetty tutkimus- ja kehittämistoiminnan lähestymistapaa voidaan soveltaa vastaavissa tutkimusorganisaatioissa kuin CENTRIA.

Automotive telematic systems will be as common in future cars as ABS and airbags are in current cars. These systems will provide services such as navigation aid, automatic emergency alerts, traffic and road information, information about parking possibilities, tourist information, and personalized news. All these services need a communication link to the mobile Internet to be able to work properly. In this master’s thesis General Packet Radio Service, GPRS will be investigated and evaluated as a bearer for these kinds of services. A test application was built to test the location aware communication on the field. Upstream and downstream delays, possible bottlenecks in the network, connection set-up time, characteristics of different operators, and connection breakdowns where analyzed. The tests showed high network delays, and that many connection breakdowns occurred. The operators did not differ much when it comes to performance. One mayor drawback with GPRS is the low scalability with respect to operator’s lack of IP addresses, which will be discussed. Conclusions of the evaluation were that only certain location-based services are suitable over GPRS with the quality of today.