We present the design, implementation, evaluation, and user experiences of the CenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.

Abstract. We present the design, prototype implementation, and evaluation of CenceMe, a personal sensing system that enables members of social networks to share their sensing presence with their buddies in a secure manner. Sensing presence captures a user’s status in terms of his activity (e.g., sitting, walking, meeting friends), disposition (e.g., happy, sad, doing OK), habits (e.g., at the gym, coffee shop today, at work) and surroundings (e.g., noisy, hot, bright, high ozone). CenceMe injects sensing presence into popular social networking applications such as Facebook, MySpace, and IM (Skype, Pidgin) allowing for new levels of “connection ” and implicit communication (albeit non-verbal) between friends in social networks. The CenceMe system is implemented, in part, as a thin-client on a number of standard and sensor-enabled cell phones and offers a number of services, which can be activated on a per-buddy basis to expose different degrees of a user’s sensing presence; these services include, life patterns, my presence, friend feeds, social interaction, significant places, buddy search, buddy beacon, and “above average?” 1

In this article we report our experience on building a contextual application with two different middlewares: the Context Toolkit from Georgia Tech and the Coordination Language Facility (CLF) from XRCE. In our email notifier sample application people receive audible or visual notifications about selected incoming emails at their current locations, e.g. in a colleague's room. Although rather straightforward, this application provides a good starting point for discussing requirements on software infrastructures for contextual computing.

Communicating with someone via the right communication media seems to be difficult nowadays as people use various mode of communication media and senders do not have any knowledge about receivers ’ current communication media, especially in ubiquitous environment. Thus, we propose the method how the most appropriate media to reach the receiver can be selected based on the effective measures. In the paper, a preliminary evaluation of the proposed method is also described to show its effectiveness. We are developing a research prototype system to support the sender with the information of the best reachable media to the receiver. 1.