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MetroSense People-Centric Urban Sensing Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/

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Presentation on theme: "MetroSense People-Centric Urban Sensing Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/"— Presentation transcript:

1 MetroSense People-Centric Urban Sensing Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/ Columbia University

2 Focus of Sensor Network Research Industrial, structural, environmental monitoring systems, military systems, etc.

3 Characteristics of Existing Systems Small-scale, short-lived, mostly-static Application-specific Multi-hop wireless Very energy-constrained Mobility not an issues of driving factor People out of the loop

4 Sensor networks at cross roads?

5 They don’t impact our everyday lives, why?

6 Follow the people. Follow the money.

7 New Frontier for Sensing: Urban Timescape Ron Fricke, timescape is a day in the life of a city (edited version)

8 People-centric, mobility counts, scale matters.

9 Urban Sensing Apps. Noise mapping Emotion mapping Congestion charging Congestion Map London London Noise Map Emotion Maps of London Downing Street Noise Map

10 People-Centric Sensing Apps. Heath care applications Emergency care (Codeblue), assited living (AlarmNet) Recreational applications Running (Nikeplus), dancing (interactive dance ensembles) Urban gaming php

11 Emerging Urban Sensing Classes “Personal Sensing” for individuals e.g., nikeplus, sensor-enabled cellphone apps., health care Great potent for commercial success (catch the ipod generation) - youthful early adopters “Peer Sensing” for groups e.g., urban gaming, peers apps. Could be an explosive growth because of existing gaming users “Utility Sensing” (system-wide) provides utility to a large population of potential users e.g., noisemapping, others Providers (e.g., towns, organizations, enterprises) will have to invest in build out - costly

12 Need to exploit existing computing, sensing, wireless breakthroughs and infrastructure to support these emerging urban sensing classes

13 What is MetroSense?

14 Architecture for large-scale sensing based on mobile sensors.

15 Captures interaction between people, and, between people and their surroundings.

16 Enables general purpose programming of the infrastructure.

17 Based on three design principles that promote low cost, scalability, and performance.

18 Importantly, mobile people-centric sensors run their own apps. (i.e., personal sensing, peer sensing), and, in parallel support “symbiotic sensing” (i.e., utility sensing, peer sensing) of other users in a transparent manner.

19 Gains scalability and sensing coverage via people-centric mobility, and its adaptive “sphere of interaction” design.

20 Goal is to study and evaluate an “opportunistic sensor network” paradigm

21 Characteristics of Existing Systems Small-scale, short-lived, mostly-static Application-specific Multi-hop wireless Very energy-constrained Mobility not an issues of driving factor People out of the loop

22 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-specific Multi-hop wireless Very energy-constrained Mobility not an issues of driving factor People out of the loop

23 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Multi-hop wireless Very energy-constrained Mobility not an issues of driving factor People out of the loop

24 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Very limited multi-hop wireless Very energy-constrained Mobility not an issues of driving factor People out of the loop

25 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Very limited multi-hop wireless Not energy-constrained Mobility not an issues of driving factor People out of the loop

26 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Very limited multi-hop wireless Not energy-constrained Mobility is a driving factor People out of the loop

27 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Very limited multi-hop wireless Not energy-constrained Mobility is a driving factor People in the loop

28 Characteristics of MetroSense Large-scale, long-lived, mostly-mobile Application-agnostic Very limited multi-hop wireless Not energy-constrained Mobility is a driving factor People in the loop Security, trust, and privacy important

29 My sense of “urban” has changed … from here.. to here

30 Sensing across a Large Area is Challenging

31 Imagine the Green Is Time Square ;-)

32 Sensing across a Large Area is Challenging Some simple questions one might ask How many people are sitting, running, walking on the Green? Where is Andrew on the Green? Noise, temperature, allergies distribution across the Green [now, 10AM- 10PM, etc.] Others

33 Sensing across a Large Area is Challenging - what scales? Ubisense Tiered Mesh MetroSense BuildingCampusTownCity Scale Cost ($) Ubisense Tiered Mesh MetroSense BuildingCampusTownCity Scale Fidelity ( Samples/Area )

34 What is MetroSense? Relies on the random or not so random mobility of people Task sensors to “collect” sensor data and deliver it opportunistically Offers in delay-tolerant sensing Infrastructure Sensor Access Points (SAPs) Mobile Sensors (MSs) Static Sensors (SSs) Operations Opportunistic Tasking, Sensing, Collection Opportunistic Delegation Model (ODM)

35 Sensing across a Large Area is Challenging - a proposed Campus-wide Sensor Network SAP locations - Aruba APs

36 Sensing Coverage using MetroSense

37 MetroSense Infrastructure Sensor Access Point (SAP) People-centric sensing apps Dartmouth Pulse BikeNet Sensor devices Interacts with static sensor clouds

38 MetroSense Operations opportunistic tasking opportunistic collection opportunistic sensing limited peering comms & ground-truth sensing

39 Opportunistic Delegation Model (ODM) sensing “space” of interest Goal is to extend sensing coverage Application requires sensed modality ß from “space” during [t1, t2] Delegate “limited” responsibility for “limited” time Direct and indirect delegation of roles Sensing, tasking, collection and “data muling” Enables new services (ODM Primitives) Virtual sensing range, virtual collection range, virtual static sensor, virtual mobile network Design challenges Sensing range is dependent on modality Limited comms. “rendezvous” time Candidate sensor selection is challenging Likelihood of a mobile reaching a targeted sensing space is probabilistic in nature Delay tolerant characteristics of sensing and collection processes TX range sensing range direct delegation indirect delegation Data mule

40 Virtual Sensing Range - an ODM Primitive/Service Area of Interest - “sensing space” Virtual Sensing Range TX range sensing range

41 Virtual Sensing Range - Experimental Result Virtual Sensing Range

42 New Transports for Opportunistic Tasking and Collection Reliable, secure needs Limited “rendezvous” time, mobility, probabilistic sensing/collection, delay tolerant collection New transport needs Lazy uploading Lazy tasking Direction-based muling Adaptive multihop

43 Mote broadcasting sync msgs from GPS unit GPS Unit Measuring terrain slope Measuring pedal speed Measuring how fast i kick the ass of inconsiderate motorists Bikenet Road Warrior

44 Skiscape Dartmouth Skiway

45 Existing Urban Sensing Initiatives Nokia’s SensorPlanet CENS Urban Sensing Summit (May 2006) ban_Sensing_Summit CitySense (BBN/Harvard) MetroSense (Dartmouth/ Columbia) Others?

46 Conclusion Next wave in sensor networks is “people in the loop and not out of loop” sensor networks Scale and mobility matters and are challenging in terms of architectural design Didn’t talk about security, privacy, and trust that are central to this effort

47 What is MetroSense?

48 Ultimately, its about a new wireless sensor edge for Internet

49 Thanks for listening!


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