Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Social Networking <s> 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?". <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Social Networking <s> Participatory sensing technologies could improve our lives and our communities, but at what cost to our privacy? <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Social Networking <s> In this paper, we identify mobile social networks as an important new direction of research in mobile computing, and show how an expanded definition of mobile social networks that includes sensor networks can enable exciting new context-aware applications, such as context-aware video screens, music jukeboxes, and mobile health applications. We offer SocialFusion as a system capable of systematically integrating such diverse mobile, social, and sensing input streams and effectuating the appropriate context-aware output action. We explain some of the major challenges that SocialFusion must overcome. We describe some preliminary results that we have obtained in implementing the SocialFusion vision. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> D. Social Networking <s> Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale. <s> BIB004
The ubiquity of Internet usage have enable people to exchange innumerable different form of information at a global scale. This situation have resulted in explosive growth in the creation of social network platforms (e.g., Facebook, Twitter etc.) where people can describe and share their personal interests, preferences and information. With the emergence of sophisticated sensors equipped smartphones, the integration of smartphones and social networks have leveraged data collection capability, and led the born of exciting contextaware applications as well as the evolution of the Internet of Things. However, the question of how the inference of a human relevant context can incorporate with social network platforms in an autonomous way is still the most exciting research topic. In this sense, researchers have been trying to create context-aware systems where diverse and large data streams (e.g., image, video, user location, user transportation mode) are automatically sensed and logically fused together for social interaction of individuals or groups of people, which is sometimes called crowdsensing or crowdsourcing. "CenceMe" BIB001 is the foremost study which enables to infer user relevant activities, dispositions, habits and surroundings, and then to inject these information into social networks platforms. The fusing of sensor and social data for context-aware computing is also studied in BIB003 . A detailed study for the current state and future challenges of the crowdsensing is given in  , BIB004 . In addition, some exciting futuristic project ideas can be obtained through www.funf.org. On the other hand, privacy, security and resource considerations unfortunately limit the expansion of community-based sensing applications since cyber-stalking BIB002 by tracing the revealed user information could harm mobile users by economically, physically, and legally. In the absence of relevant concerns, some websites/applications such as  can be used for reducing risks of community sensing.