Existing social networking services recommend friends to the users based on their social graphs, which may not be the most appropriate to reflect a user's preferences on a friend selection in their real life. Friendbook is a novel semanticbased friend recommendation system, which recommends friends to the users based on their lifestyles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of lifestyles between users, and recommends friends to the users if their life styles have high similarity. User's daily life is recorded as life activities, from which his/her life styles are extracted and further get stored in the cloud. Similarity metric is used to measure the similarity of life styles between users, and calculate users' impact in terms of life styles. When entering the friend recommendation system, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. The idea is to implement Friendbook on the Android-based smart phones, and evaluate its performance on both small-scale simulations.