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Research Challenges in the CarTel Mobile Sensor System

Research Challenges in the CarTel Mobile Sensor System. Samuel Madden Associate Professor, MIT. Wide Area Sensing. Real-world problems: Civil infrastructure monitoring Road-surface conditions Visual mapping Commute time optimization Wide-area, static sensing

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Research Challenges in the CarTel Mobile Sensor System

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  1. Research Challenges in the CarTel Mobile Sensor System Samuel Madden Associate Professor, MIT

  2. 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?

  3. 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?

  4. 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: • Collect / process • Deliver • Visualize / analyze data from mobile sensors (cars, phones, etc)

  5. Coverage Map 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

  6. 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, ….

  7. 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)

  8. 1 3 A C B 2 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 Lim et al. “Stochastic Motion Planning and Applications to Traffic.” Under submission.

  9. Finding Potholes

  10. 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

  11. 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

  12. FunctionDB • DBMS that can fit continuous functions to raw data, query data representedby these functions using SQL Regression Function temp(t) • 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 Raw data (temp readings) Solveequation temp(t) = thresh Query: Report when temp crosses threshold SELECT time WHERE temp = thresh time

  13. 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

  14. 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!

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