My paper titled 'ProxiMate: Proximity-based Secure Pairing using Ambient Wireless Signals' has been accepted at the 9th Annual International Conference on Mobile Systems, Applications and Services (ACM Mobisys) to be held at Washington D.C.. The paper is about how existing wireless signals, such as FM radio and TV signals, can be used by radios in close proximity to generate a shared secret, and then use that secret to communicate securely. By building a shared key in this manner, it is hoped that the problem of identity spoofing and eavesdropping on wireless links can be addressed. The principles at work in ProxiMate are: (i) Small-scale fading: the wireless channel between a distance RF source (such as an FM radio tower) and a receiver, behaves as a random process, thereby serving as a source of randomness, and (ii) receivers that are within half a wavelength of each other can observe highly correlated (but not identical) versions of these random processes, allowing wireless devices in proximity access to a shared source of common randomness that is not available to any sufficiently distant adversary. ProxiMate is a proof of concept system built on software-defined radio and evaluated using ambient FM and TV signals. It is intended to be information-theoretically secure -- meaning, the key generation phase does not need to assume a computationally bounded adversary -- as opposed to say, Diffie Hellman, which must assume a computationally bounded adversary in order to guarantee that the observations available to the adversary will not allow it to compute the key. On a side note, D.C. is one of the more boring cities to have a conference once you have done the museums circuit. Anyhow, looking forward to it!
My work on mobile sensing for automotive parking applications has just been accepted to ACM Mobisys 2010 and I will br presenting this work at Mobisys in San Francisco in June 2010. The paper is about the design, implementation and evaluation of a mobile sensing system called ParkNet, for the purpose of harvesting in as close to real time as possible, information about the availability street-parking spaces in urban areas. ParkNet was recently featured in the MIT Technology review. It has just been covered by Rutgers Today, an organization within Rutgers University that produces news articles about promising research efforts within Rutgers. ParkNet proposes that sensors for sensing vacant street side parking spaces be made mobile (see this project trial that uses stationary sensors installed in the asphalt road, one sensor per spot, presumably at a huge cost) by exploiting vehicles that regularly comb city environment, such as taxicabs. ParkNet can be thought of as a compressive sensing system because it drastically reduces the number of sensors needed [with a corresponding dramatic decrease in overall cost], with a very small loss in spatio-temporal accuracy.
Naturally, one of the things I needed to figure out was: how well can a fleet of N taxicabs cover a given geographical area, in terms of mean time between successive cabs visiting a given street? How does this number vary with N. The San Francisco Taxicab dataset came in handy. The visualization on the right is made with good old Matlab©. It shows GPS coordinates of a single taxicab in the San Francisco area, measured about once per minute, over a period of 30 days. When I plotted this, I immediately realized that taxicabs would be great for ParkNet because most taxicabs tend to spend most of their time in the crowded downtown area of the city (dense, upper right corner in the plot), and this is where the parking problem is most serious.
Update: My ParkNet paper received the best paper award at ACM's annual Mobile Systems and Applications Conference [ACM Mobisys]!