Saturday, April 18, 2009


Sensor Network-Based Countersniper System
Simon et al.

VoxNet: An Interactive, Rapidly-Deployable Acoustic Monitoring Platform
Allen et al.

Acoustic sensing was the topic for Tuesday’s class. The first paper used acoustics to locate a sniper (after taking a shot) and the second presented a generic acoustic sensing framework (to find marmots, among other things).

We clarified a few elements of the countersniper paper first. In particular, the consistency function was unclear to a few people. The function is used to make sense of the multiple shooter predictions coming from nodes. Some nodes may be victim to sound reflection and others may be hearing other sounds. The base station uses the consistency function to combine all of these potentially erroneous readings to make a confident final prediction of the shooter’s location. The function just breaks up a 3D location (X,Y,Z) and time (T) into a four dimensional grid. Each node’s prediction falls into one of the grid’s unit hypercubes (the 4D volume is determined by sensor accuracy characteristics) and the unit hypercube with the most predictions is considered the true shooter location. We had a calculator jam session and found that there are roughly 880 billion potential hypercubes in the shooting range, so an exhaustive search would be slow. Instead, the consistency function just starts with a bisected 4D space, chooses the hypercube and bisects it. This process repeats until a final unit hypercube is chosen.

Signal detection was another important problem the paper had to deal with. To predict a shooter’s location, each node must first know there was a shot. The paper’s approach used zero-crossing points in the acoustic signal, which would give you information on the frequency of the loudest sound. The paper also uses other statistics (max amplitude, etc.) to create a sound signature and can compare this with the expected gunshot signature. At least a few people in class though it was cool that the paper used custom logic (or an FPGA at least) to perform this work more efficiently than a mote.

There seem to be a few ways the system could be fooled. For example, if more motes heard echos than heard a direct event, the consistency function would not work properly. Alternatively, someone could maliciously blast gunshot noises from other locations to distract or jam the countersniper system. Finally, the system wouldn’t do too well if there were multiple rapid-fire shots.

The consensus on this paper was that it brought a lot of techniques together and motivated the use of sensor networks well. This felt like the paper wanted to solve a real problem rather than making up a contrived one.

We moved on to VoxNet, a more generic platform for monitoring acoustics over some area. The system is a bit beefier than our typical sensor networks because it is handling multiple sources with high data rates (4 channels of 44.1KHz audio each). This raised a hearty debate about what a “sensor network” was, exactly. These nodes are using StrongARMs, 802.11, have a large battery, and only have a lifetime of eight hours. These nodes are a far cry from the MSP430 powered motes that might last for months in most of the other papers we read. But, the system forms a network and uses sensors, so it’s technically a sensor network, no?

The paper made accessibility to domain scientists a priority using WaveScope, their dataflow language. The language is pretty simple and can be retargeted for other platforms (such as desktop computers). It also makes use of hand-tuned functions for computationally intensive operations, such as FFTs. This allows WaveScope to get good performance/energy results despite the higher-level language.  However, this also lead us to question the performance/energy claims of WaveScope – if most of the processor cycles are spent in these hand-tuned (non-WaveScope) functions, the power/performance evaluation isn’t really measuring WaveScope. Rather, it’s showing that WaveScope doesn’t glue these functions together really poorly. It would be interesting to see how WaveScope performs by itself without these hand-tuned functions.

Finally, we really liked the idea of letting multiple applications coexist. The introduction had us sold on testing code revisions while letting the old programs run, but sadly the paper never came back to it. We would really have loved to see how that functionality fit into the system.