research challenges in the cartel mobile sensor system samuel madden associate professor, mit
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Research Challenges in the CarTel Mobile Sensor System
Samuel MaddenAssociate Professor, MIT
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Wide Area Sensing• Real-world problems:
– Civil infrastructure monitoring– Road-surface conditions– Visual mapping– Commute time optimization
• Wide-area, static sensing– Costly deployment & maintenance
• Observation: some apps do not need high temporal fidelity
• Mobile Sensing– Costly platform?
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Our Approach: Opportunistic Mobility
• Take advantage of existing mobility• Example: cellphones w/ sensors
– 1.5 billion phones worldwide– High spatial coverage– High-performance processor
• Cars equipped with sensors– 650 million cars on the road– Abundance of power and space– Have >100 embedded sensors
What system architecture is best suited for mobile, wide-area sensing?
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CarTel: A Mobile Sensor Computing System
• Tool to answer questions about spatially diverse data sets– E.g., Collect traffic flow data from every road / issue queries for
route planning
• Core tasks:
1. Collect / process
2. Deliver
3. Visualize / analyze
data from mobile sensors (cars, phones, etc)
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Deployment
• Deployed on 9 users’ cars, 27 taxis• 2 boxes per cab
– Master; services for company, drivers, GPS– Slave; experimental box
• Taxi company gets fleet management software, in-car WiFi
• We get data!
• Demo
Coverage Map
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Applications & Research
• Route Planning– Under submission
• Pothole Finding– MobiSys 2008
• Managing lossy & noisy trajectories– SIGMOD 2008
• Others – wireless networking (MobiCom 06, 08), carbon footprint, visual mapping, ….
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Route Planning
• Match traces to map• Compute Gaussian delay for
each segment– Assume independence
• Minimize 3 metrics– Distance
• Google Maps– Expected delay– Pr(missing time goal)
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Max. Probability Planning• Travel time of each edge is a Gaussian
– If indepdendent, travel time of a path is also Gaussian
• Goal: find path with max. probability of reaching destination by deadline
• Unlike standard shortest paths, no suboptimality– If AxCyB is best path from A to B, AxC is not necessarily the best path
from A to C
• Implies cannot use A* or Dijkstra
2
A BC
13Lim et al. “Stochastic Motion Planning and Applications to
Traffic.” Under submission.
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Finding Potholes
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Classification-based Approach
• Classifier differentiates between several types of anomalies
• Window data, compute features per window
• Variety of features:– Range of X,Y,Z accel– Energy in certain frequency
bands– Car speed– …
See Erikkson et al, MobiSys 2008
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FunctionDB
• Challenge: how to store and query all of this data?
• Discrete points don’t work well• Most users don’t actually want raw data!
– Prefer trajectories, fields, fit functions– Idea: support these as first class objects inside the
DBMS
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FunctionDB• DBMS that can fit continuous functions to raw
data, query data represented by these functions using SQL
Raw data (temp readings)
Query: Report when temp crosses threshold
SELECT time WHERE temp = thresh
Regression Function temp(t)
Solve equation temp(t) = thresh
time
• Works for any polynomial function
• Supports aggregates (integrals) and joins
• Tricks to deal with intractable queries
• 5-6 x performance gains for common queries on CarTel data
See Thiagarajan and Madden, SIGMOD 2008
temp
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Open Problems
• CarTel is a lot of application specific code
• Many SIGMOD papers in building “a declarative framework for X”, where X in {– Signal processing & data management– Personalization– Data cleaning and de-noising– …}
• Focusing on a specific (real) application ensures relevance– Highlights limitations of a database-specific approach
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Conclusion
• Research is in capturing, processing, and synthesizing the data– This is what most of us are good at
• This kind of end-to-end deployment isn’t hard– Hardware is $50-$300 / car– 10 cars is sufficient to provide a very interesting data set
• Motes and TinyOS are an interesting novelty, not all there is to sensor networking
• Find an application that excites you and go for it!