1. Microsoft Research Faculty Summit 2007

2. Aman Kansal Researcher Networked Embedded Computing, MSR

3. PARK with people …and phones Upload Pictures, Video, Audio 1. Is the court wet? 3. Which bird sounds reported? APPLICATION GROUP MEMBER Stitched view SMS: Click picture of court. Group Points: 400 SenseWeb (Data centric coverage model) 2. What play structures are there?

4. Community Fitness Runners: Where are sidewalks broken? Construction finished on 24 th St? Recreation Mountain Bikers: Average biker heart rate at Adams Pass on trail 320? Surfer: What do the waves look like now? Hikers: Did the storm block the trail? Public initiated instant news coverage: ground truthsShopping Which displays changed? What’s attracting most attention? Urban Moods Where are people hanging out tonight? Real time Virtual Earth street side imageryPollution updates to Scorecard.orgBusiness Intelligence What did customer add to our design at the last meeting?

5. 2.14 billion phones and growing Mobility reach where static sensor cannot increased spatial coverage Phone exists for voice/data apps: Piggybacking sensing is cost effective Human assistance Can sometimes help detect or aim at interesting phenomenon

6. Client on phone Allows users to take pictures Automatically uploads data to server Location stamps using inbuilt/Bluetooth GPS SenseWeb Server Indexes images by location and time (SQL Server database) Web service API for phones and apps. Supports several sensor types Example App: Portal Displays sensor data by location and sensor type Publicly accessible at http://atom.research. microsoft.com/sensor map Web service API’s allow building other apps.

7. Information value Which data to collect and share: battery and bandwidth constraints Coverage management Which phone sensed where app needs coverage Sensor tasking for application demands Incentive mechanisms Data verifiability, user privacy

8. Entropy of a single image: H(X) = -  (p.log(p)) [p: image histogram] Value among multiple images Consider common spatial coverage H(X|Y) = -E[log 2 p(X|Y)] H(X|Y 1,…,Y m ) = H(X|Z) (Z: common spatial coverage) Commonality: found using key feature based algorithm Relevance Value Cutoff (%) Data Size (MB) Buildings Kitchen Value based selection Details: ACM Sensys WSW 2006

9. Which sensors does app access Who sensed in required region during required time window? Mobile Sensor Swarm

10. Which sensors does app access Who sensed in required region during required time window? Solution: location Samples are geo- stamped Apps do not track device Trajectory Connectivity Sharing preferences Device ID anonymized Data Centric Abstraction Mobile Sensor Swarm Application 1 Application n

11. Several location technologies GPS: does not work everywhere Cell tower: coarse Wifi: coarse Human entered tags: approximate, high manual effort Leverage camera data to enhance location Refine location granularity Room within building, aisle within store Associate data when location not available Verify location i j M ij Algorithm Images within vicinity organized as a graph Edge weight by match Relation R(i,j) by highest weight Refined location zone: Transitive closure of R Details: ACM NOSSDAV 2007

12. Minimize sensing task overhead on phones Sense to be most accurate on most used regions Good model: determine where sensing needed Learn most used: where apps need data Task phones: battery, bandwidth, privacy, intrusion costs Phenomenon DemandSensing cost Details: Andreas Krause, Intern project report

13. Set V of possible observations For each subset A of V, define utility U(A) = Σ i E[D i (Var(S i ) – Var(S i | A)) ] Expectation over demand D i and observations A Theorem: U(A) is submodular Theorem [Nemhauser et al]: For submodular U: U(greedy solution) > (1-1/e) U(optimal)

14. Mobile phones enable many sensing apps Architecture to use a highly volatile swarm of mobile devices as a sensor network Information value based data selection Location based data centric abstraction Coverage management and data addressing Avoids burdening applications with managing device motion, connectivity, sharing Efficient sensor tasking Contact: kansal@microsoft.comkansal@microsoft.com

15. © 2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.