We almost missed the chance to highlight that the cover story of the July, 2014 issue of the Communications of the ACM (CACM) is a paper by a Caltech group on the Community Seismic Network (CSN). This note is about CSN as an example of system in a growing, important nexus: citizen science, inexpensive sensors, and cloud computing.
CSN uses inexpensive MEMS accelerometers or accelerometers in phones to detect shaking from earthquakes. The CSN project builds accelerometer “boxes” that contain an accelerometer, a Sheevaplug, and cables. A citizen scientist merely affixes the small box to the floor with double-sided sticky tape, and connects cables from the box to power and to a router. Installation takes minutes.
Analytics in the Sheevaplug or some other computer connected to the accelerometer analyzes the raw data streaming in from the sensor. This analytics engine detects local anomalous acceleration. Local anomalies could be due to somebody banging on a door, or a big dog jumping off the couch (frequent occurrence in my house), or due to an earthquake. The plug computer or phone sends messages to the cloud when it detects a local anomaly. An accelerometer may measure at 200 samples per second, but messages get sent to the cloud at rates that range from one per minute, to one every 20 minutes. The local anomaly message includes the sensor id, location (because phones move), and magnitude.
There are four critical differences between community networks and traditional seismic networks:
- Community sensor fidelity is much poorer than that of expensive instruments.
- The quality of deployment of community sensors by ordinary citizens is much more varied than that of sensors deployed by professional organizations.
- Community sensors can be deployed more densely than expensive sensors. Think about the relative density of phones versus seismometers in earthquake-prone regions of the world such as Peru, India, China, Pakistan, Iran and Indonesia.
- Community sensors are deployed where communities are located, and these locations may not be the most valuable for scientists.
Research questions investigated by the Caltech CSN team include: Are community sensor networks useful? Does the lower-fidelity, varied installation practices, and relatively random deployment result in networks that don’t provide value to the community and don’t provide value to science? Can community networks add value to other networks operated by government agencies and companies? Can inexpensive cloud computing services be used to fuse data from hundreds of sensors to detect earthquakes within seconds?
A thought experiment
Carry out the following thought experiment to set the context for these questions. A community sensor given to a member of the community may never be turned on, may be turned off accidentally after a while, may be placed on a kitchen table, or (ideally) affixed firmly to a concrete floor. By contrast, USGS deploys seismometers using a discipline. Some seismometers are placed deep in the ground, covered with sand, and have their own communication mechanism. How could community networks possibly work? What’s the point of trying to harness accelerometers in phones?
These are interesting questions. To get the answers, read the paper!
P.S. An important point about Caltech that helped produce this research: Caltech is truly interdisciplinary and collaborative. The CSN team includes geophysicists, earthquake engineers, computer scientists working on distributed systems and machine learning. The team has professors, research staff, graduate students, undergraduates, and summer undergraduate research fellows from around the world.
Rigor + Relevance 