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

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Presentation on theme: "Research Challenges in the CarTel Mobile Sensor System Samuel Madden Associate Professor, MIT."— Presentation transcript:

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: 1.Collect / process 2.Deliver 3.Visualize / analyze data from mobile sensors (cars, phones, etc)

5 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

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 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 ABC 1 3 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 dont work well Most users dont 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 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 temp

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 isnt 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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