The Community Seismic Network: Citizen Science and the Cloud

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?

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Leslie Lamport: The Ideal RSRG Scientist

Remember that this blog is coming from the Rigorous Systems Research Group (RSRG, aka “resurge”) at Caltech.  The group has a fairly unique perspective on systems research, so you might wonder:

If you were to pick the RSRG “ideal person,” whom would you pick?

My ideal is Leslie Lamport, the Turing Award winner this year. Here’s why.

Leslie is among the most logically-rigorous computer scientists in the history of computer science, and he has done as much for developing the discipline of scientific, theory-based, rigorous computer systems implementation as any person. The combination of logical rigor and practical systems makes Leslie stand head-and-shoulders above everybody as the RSRG ideal.

Everything we do at RSRG deals with concurrency: communication networks, power systems, economics and information technology, cloud computing, distributed systems, control systems, and real-time analytic systems. The theoretical foundations of concurrent systems have two parts: (1) a logic that enables systems to be designed and analyzed rigorously, and (2) a collection of fundamental algorithms that lie at the heart of almost all concurrent systems. Several great computer scientists have built the foundations for the first part, and several have developed the foundations for the second. But only two or three in the history of computer science have done both, and Leslie is one of them.

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Death spirals

One of the challenges of renewable integration that often goes undiscussed are the “death spirals” that are associated with adoption. We’ve been thinking a lot about these issues at Caltech over the past few years…

Two motivating stories

To highlight what we mean by a “death spiral”, let us first consider an example of  consumers in Southern California who use a lot of power from the power grid.  They clearly have an incentive to install rooftop solar since the price you pay for each incremental kilowatt-hour you consume increases with the total amount that you consume. That means that if you consume less you fall into a lower tier in which the price of the next kilowatt hour you consume is low; whereas if you consume a lot, the corresponding price you pay is high. This convex price structure is an incentive for high consumers to reduce consumption; it is also, however, an incentive for installing rooftop solar so that the consumer’s net consumption falls into a low tier.

But what are the consequences of the fact that incentives for adoption are much stronger for high consumers?

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