30 December 2009
Coming Face-to-Face with Mobile Communications: Improving the connection between digital networks and the human networks that they serve 
 

Nadav AharonMIT
 


The talk will review some of the work at the MIT Media Lab in the area of close proximity interaction and communications. This includes two connected research tracks: The first deals with systems and software that allow users to easily discover and communicate with other people and services in their physical surrounding .The second track deals with the use of mobile phones and other platforms as social sensors that enable us to learn human interaction and social patterns. Most existing communication network architectures are agnostic to the structures and behaviors of the human networks that they serve. By incorporating social patterns into the network protocols we might be able to significantly improve the network security, privacy, and quality of service levels by producing protocols that are auto-adaptive to the social aspects of their users’ networking behaviors. Utilization of social information at the networking layers could facilitate the building of systems and applications with high reliability, efficiency, and usability. In the other direction, network activity and information that is available throughout the network stack, all the way down to the radio layer, could be used as sensors for detecting and learning human social network information. Coupled with additional sensors that exist on many of today’s smart-phones, they allow us to turn a mobile phone into a social sensor that is able to learn the behavior patterns of its owner, and, when aggregated - of an entire society. This approach, called “Reality Mining”, is a key component in the transformation of traditional social science into the emerging area of computational social science. The speaker will review some of their real-world experiments, involving tens of users for weeks or months at a time, and note some of the many interesting challenges that are part of this emerging area of research. TFhese include challenges in areas of communications, pattern recognition, machine learning, security and privacy, user interfaces, sensor technology, and more.

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