Combining Mobile Technologies For Accurate, Open Source, Privacy Sensitive, Zero Cost, Location Determination

Determining the location of an object or individual using a mobile device (e.g. cell phone) is an important aspect of modern information gathering. Various solutions have been proposed which all have their strengths and weaknesses. To date, no solution has been devised for a mobile device that will work effectively in multiple environments and without assistance from network-provider connections1. To address this, it is argued that the current state of the art can be advanced using a hybrid approach that combines a number of sensor technologies to provide a more reliable, and accurate mobile location determination that functions in multiple environments (indoors and outdoors). This thesis examines in detail current relevant available technology, calculation techniques for location determination, the Global Navigation Satellite System (GNSS) and other noteworthy location determination research. It then introduces our solution of a hybrid positioning system that is an open-source, provider-network independent, privacy sensitive, zero-cost and accurate software component. First the overall system design is described and then individual modules are described in detail. It describes in full an algorithm that intelligently combines signals from various technologies, applies weights to these signals and also leverages past signal readings to enhance current calculations. Next, the evaluation section is introduced which discusses how and why the test bed was chosen and deployed. It then discusses individual test results and finally the overall tests are analysed, discussed and summarised. Finally, the conclusions are prepared in detail, the three initial questions raised in the introduction are answered and discussed and the contributions to the body of knowledge are reaffirmed. Future work finishes the thesis and looks at several research paths that can be pursued from this research

LOCATION-BASED MARKETING: CONCEPTS, TECHNOLOGIES AND SERVICES

siirretty Doriast

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described