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MetroSense People-Centric Urban Sensing
Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/ Columbia University
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Focus of Sensor Network Research
Industrial, structural, environmental monitoring systems, military systems, etc.
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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
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Sensor networks at cross roads?
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They don’t impact our everyday lives, why?
(Mark Weiser’s vision - 20 years on).
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Follow the people. Follow the money.
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New Frontier for Sensing: Urban Timescape
Ron Fricke, timescape is a day in the life of a city (edited version)
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People-centric, mobility counts, scale matters.
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Urban Sensing Apps. Noise mapping Emotion mapping Congestion charging
Emotion mapping Congestion charging London Noise Map The Bio Mapping tool allows the wearer to record their Galvanic Skin Response (GSR), which is a simple indicator of emotional arousal in conjunction with their geographical location. This can be used to plot a map that highlights point of high and low arousal. By sharing this data we can construct maps that visualise where we as a community feel stressed and excited Noise - unwanted sound - is a universal problem and most of us have been affected by it at some point in our lives.The Noise Mapping England project is the first stage in the development of a National Ambient Noise Strategy for England. The project aims to establish the environmental noise climate across England.Defra (Department for Environment, Food and Rural Affairs) has now launched the London Road Traffic Noise Map, which is fundamental to the Noise Mapping England project.To view the maps, click on the London area Downing Street Noise Map Emotion Maps of London Congestion Map London
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People-Centric Sensing Apps.
Heath care applications Emergency care (Codeblue), assited living (AlarmNet) Recreational applications Running (Nikeplus), dancing (interactive dance ensembles) Urban gaming
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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
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Need to exploit existing computing, sensing, wireless breakthroughs and infrastructure to support these emerging urban sensing classes
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What is MetroSense?
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Architecture for large-scale sensing based on mobile sensors.
Architecture for large-scale sensing based on mobile sensors (most people, but also vehicle mobility - cars, buses, bikes) + also interaction with static sensor webs
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Captures interaction between people, and, between people and their surroundings.
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Enables general purpose programming of the infrastructure.
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Based on three design principles that promote low cost, scalability, and performance.
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Importantly, mobile people-centric sensors run their own apps. (i. e
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.
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Gains scalability and sensing coverage via people-centric mobility, and its adaptive “sphere of interaction” design.
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Goal is to study and evaluate an “opportunistic sensor network” paradigm
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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
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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
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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
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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
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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
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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
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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
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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
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My sense of “urban” has changed … from here
.. to here
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Sensing across a Large Area is Challenging
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Sensing across a Large Area is Challenging
Imagine the Green Is Time Square ;-)
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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
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Sensing across a Large Area is Challenging - what scales?
Fidelity (Samples/Area) Ubisense Cost ($) Ubisense Tiered Mesh Tiered Mesh MetroSense MetroSense Building Campus Town City Scale Building Campus Town City Scale
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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)
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Sensing across a Large Area is Challenging - a proposed Campus-wide Sensor Network
SAP locations - Aruba APs
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Sensing Coverage using MetroSense
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MetroSense Infrastructure
Sensor Access Point (SAP) People-centric sensing apps Sensor devices Dartmouth Pulse BikeNet Interacts with static sensor clouds
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MetroSense Operations
comms & ground-truth sensing opportunistic tasking opportunistic sensing opportunistic collection limited peering
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Opportunistic Delegation Model (ODM)
TX range sensing range direct delegation indirect delegation Data mule 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 sensing “space” of interest
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Virtual Sensing Range - an ODM Primitive/Service
TX range sensing range Area of Interest - “sensing space”
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Virtual Sensing Range - Experimental Result
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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
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Bikenet Road Warrior GPS Unit Measuring terrain slope
Mote broadcasting sync msgs from GPS unit Measuring how fast i kick the ass of inconsiderate motorists Measuring pedal speed
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Skiscape - @ Dartmouth Skiway
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Existing Urban Sensing Initiatives
Nokia’s SensorPlanet CENS Urban Sensing Summit (May 2006) CitySense (BBN/Harvard) MetroSense (Dartmouth/ Columbia) Others?
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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
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What is MetroSense?
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Ultimately, its about a new wireless sensor edge for Internet
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Thanks for listening!
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