We present a low cost and easily deployed infrastructure for location aware computing that is built using standard Bluetooth® technologies and personal computers. Mobile devices are able to determine their location to room-level granularity with existing bluetooth technology, and to even greater resolution with the use of the recently adopted bluetooth 1.2 specification, all while maintaining complete anonymity. Various techniques for improving the speed and resolution of the system are described, along with their tradeoffs in privacy. The system is trivial to implement on a large scale – our network covering 5,000 square meters was deployed by a single student over the course of a few days at a cost of less than US$1,000. / Singapore-MIT Alliance (SMA)

Mobile phones are becoming a primary platform for information access. More and more people use their mobile devices as one of their major communication access tools. Commuters are increasingly carrying their mobile devices with them almost everywhere. Mobile devices fit perfectly into an ideal environment for realizing ubiquitous computing. A major aspect of ubiquitous computing is context-aware applications where the applications collect information about the environment that the user is in and use this information to achieve their goals or improve performance. The location of the device is one of the most important pieces of context information. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes, and greatly improves the performance of other applications, for example it can be associated with automobile navigation devices. A feature available to mobile applications in the Android platform makes it possible to determine a device's location without any additional hardware or sensor mechanisms, by simply using the native capability of the built-in wireless network card. Since the release of Android systems, there have been numerous applications developed to introduce new ways of tracking locations. Recently, there have been many papers on location estimation leveraging ubiquitous wireless networks. In this thesis, we develop an Android application to collect useful Wi-Fi information without registering a location listener with a network-based provider, such as Wi-Fi connections or data connections. Therefore it provides a passive, privacy-preserving, non-intrusive and power-saving way of achieving location awareness to Android mobile users. Accurate estimation of the location information can bring a more contextual experience to mobile users. We save the passively collected data of the IDs of Wi-Fi access points and the received signal strengths to a database in order to help us structure the data and analyse it. We employ some heuristics to infer the location information from the data. Our work presents a location tracking technique mainly based on Basic Service Set identification (BSSID) and/or Received Signal Strength Indicator (RSSI) using Wi-Fi information. It falls into one of the most active fields in mobile application development --location-based or location-aware applications.

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.

<p>Today, several tourist attractions use handheld devices that act as tour guides to give the customers an improved experience and better knowledge of the attraction. A graphical user interface on the devices provides the users with information through sound, pictures and video. In order to improve the information delivered to the users, some of these guides are location-aware. However, location-aware tour guides are expensive to develop. They have to be developed tailored for each attraction, which is a time consuming job.</p><p>By having a framework for the development of location-aware tour guides, the tour guides will be both easier and less expensive to develop. In addition, it will be easy to make changes to the guides if necessary.</p><p>This project resulted in the development of a prototype for a framework for location-aware tour guides. The framework consists of three main tools; a Creator Tool, a Statistical Tool and a Runtime System. The Creator Tool is used to create and configure new tour guides. The Statistical Tool is used by the staff of the attraction. It generates statistics based on information stored in a log. The Runtime System is the system that provides the mobile devices information, adjusted to their location during a guided tour. To demonstrate the use of the framework, we have developed two client applications, one for PDAs and the other for cellphones. Both applications are electronically location-aware tour guides made for the Nidaros Cathedral.</p><p>The cellphone application is an innovative application that may result in a tremendous evolvement in the development of cellphone applications. It shows great potentials in the area of location-aware cellphone applications with high accuracy, and can be used not only in tour attractions, but also in several other fields. Thus, the project has gained a great deal of publicity, both from newspapers and television.</p>

Today, several tourist attractions use handheld devices that act as tour guides to give the customers an improved experience and better knowledge of the attraction. A graphical user interface on the devices provides the users with information through sound, pictures and video. In order to improve the information delivered to the users, some of these guides are location-aware. However, location-aware tour guides are expensive to develop. They have to be developed tailored for each attraction, which is a time consuming job. By having a framework for the development of location-aware tour guides, the tour guides will be both easier and less expensive to develop. In addition, it will be easy to make changes to the guides if necessary. This project resulted in the development of a prototype for a framework for location-aware tour guides. The framework consists of three main tools; a Creator Tool, a Statistical Tool and a Runtime System. The Creator Tool is used to create and configure new tour guides. The Statistical Tool is used by the staff of the attraction. It generates statistics based on information stored in a log. The Runtime System is the system that provides the mobile devices information, adjusted to their location during a guided tour. To demonstrate the use of the framework, we have developed two client applications, one for PDAs and the other for cellphones. Both applications are electronically location-aware tour guides made for the Nidaros Cathedral. The cellphone application is an innovative application that may result in a tremendous evolvement in the development of cellphone applications. It shows great potentials in the area of location-aware cellphone applications with high accuracy, and can be used not only in tour attractions, but also in several other fields. Thus, the project has gained a great deal of publicity, both from newspapers and television.

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.

Understanding the context of a person’s interaction with a place is important to enabling ubiquitous computing applications. The ability for mobile computing to provide information and services that are relevant to a user’s current location—which is central to the vision of ubiquitous computing—requires that the technologies be able to characterize the activities that a person may potentially perform in place, whatever this place may be. To support the user as she goes about her day, this ability to characterize the potential activities for a place must support work on a city scale. In this dissertation, we present a method to process place-specific community-authored content (e.g., Yelp.com reviews) to identify a set of the potential activities (articulated as verb-noun pairs) that a person can perform at a specific place and apply this method for places on a city scale. We validate the method by processing the place-specific reviews authored by community members of Yelp.com and show that the majority of the 40 most common verb-noun pairs are true activities that can be performed at the respective place; achieving an average mean precision of up to 79.3% and recall of up to 55.9%. We applied this method by developing a Web-service (the Activity Service) that automatically processes all the places reviewed for a city and provides structured access to the activity data that can be identified for the respective places. To validate that the place and activity data is useful and useable, we developed and evaluated two applications that are supported by the Activity Service: Opportunities Exist and Vocabulary Wallpaper. In addition to these applications, we conducted a design contest to identify other types of applications that can be supported by the Activity Service. Finally, we discuss limitations of the activity data and the Activity Service, and highlight future considerations.

Understanding the context of a person’s interaction with a place is important to enabling ubiquitous computing applications. The ability for mobile computing to provide information and services that are relevant to a user’s current location—which is central to the vision of ubiquitous computing—requires that the technologies be able to characterize the activities that a person may potentially perform in place, whatever this place may be. To support the user as she goes about her day, this ability to characterize the potential activities for a place must support work on a city scale. In this dissertation, we present a method to process place-specific community-authored content (e.g., Yelp.com reviews) to identify a set of the potential activities (articulated as verb-noun pairs) that a person can perform at a specific place and apply this method for places on a city scale. We validate the method by processing the place-specific reviews authored by community members of Yelp.com and show that the majority of the 40 most common verb-noun pairs are true activities that can be performed at the respective place; achieving an average mean precision of up to 79.3% and recall of up to 55.9%. We applied this method by developing a Web-service (the Activity Service) that automatically processes all the places reviewed for a city and provides structured access to the activity data that can be identified for the respective places. To validate that the place and activity data is useful and useable, we developed and evaluated two applications that are supported by the Activity Service: Opportunities Exist and Vocabulary Wallpaper. In addition to these applications, we conducted a design contest to identify other types of applications that can be supported by the Activity Service. Finally, we discuss limitations of the activity data and the Activity Service, and highlight future considerations.