No abstract

Internet of Things is not only a platform for communication between people, but also it provides the real-time information exchange between things and things, people and things, things and people. With the popularity of the internet of things, the application services are increasing. However, those services are usually based on the user's location information, which contains user's privacy information directly or indirectly. This paper discussed on how to protect user's location privacy while providing personalized services. Proposed a strategy of pseudonym policy and user's location information acquisition, and built a model for protecting user policy of pervasive computing based on that policy.

In this paper, we propose a context-awareness based personalized recommender system in the pervasive application space. The recommender system comprises the personalized recommender engine and the distributed context management framework. With a hybrid approach, the personalized recommender engine combines those contexts into the decisions on recommendations to get more comprehensive recommendation effectiveness. In contrast with existing middleware of context-awareness, the recommender system has an ability of user-centric recommendation. At the end of this paper, an emphasis is put on the metrics of the effectiveness of the recommender system.