Sensor and Actuator Networks and the Internet of …gr/pdf/iot_wuxi_web.pdfSensor and Actuator...
Transcript of Sensor and Actuator Networks and the Internet of …gr/pdf/iot_wuxi_web.pdfSensor and Actuator...
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Sensor and Actuator Networks and the Internet of Things"
George Roussos!Birkbeck College, University of London!
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Overview"
• WSANs and the Future Internet!• Smart Dust and the first generation of WSANs!• Shift to next generation WSANs and the IoT!• The Internet of People!• Analytics for IoT WSAN systems!• Capturing and employing collective behaviours!
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The Internet today"
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The Internet of Things"
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Device evolution"
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Problems and evolution"
• Smart Dust faces significant problems!– Energy harvesting!– Maintenance!– Programmability at the system level!
• Mobility seen as significant for robustness/performance!• Popularity and proliferation of mobile networks!
– 400M sensors in mobile phones in 2014!• Shift of emphasis to smart-phone centric networks!
– e.g. sensor clouds around smart-phone core !• Shift of focus on data and human dynamics!
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The Internet of People"
Business Week March 2009
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Core ingredients"
• Humans carry sensors and actuators on personal devices!
• These devices interact with embedded systems such as building networks, Smart Dust, Personal Area Networks and RFID!
• The IoT captures and processes the data!• Maintain, infer, characterize and provide intelligence!
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New problems emerge"
• IoT sensor network systems generate automatically massive data sets!
• How to tell what is important and what is not!• How to find significant information!• One solution we currently investigate in our group!
– To combining behaviours, preferences, or ideas of a group of people to create novel insights!
– aka collective intelligence!
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Significant locations"
• Identify significant places!• Use mobile phone location records!• Identify hot-spots of activity!• Time specific!• Commercially available through Sense
Networks!• Track real-world consumer behaviour !
sensenetworks.com
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RFID Analytics "
• RFID-tagged products and locations!• Scan traffic at specific chock points!• Analyse traffic and identify hot-spots
or problem areas!• Visual tools!• Different spatial resolution!
Illic et al, Auto-ID Lab Zurich
TrakSens
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Social networks"
• Observe social networks in the real world!
• Tag and rank location of individual!
• Identify meetings through collocation or device-to-device interaction!
• Create social network graph !• Conduct analysis!• Reality mining data set!
Shen et al, UC Davies
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Patterns of behaviour"
• Identify typical behaviours !• Possibly context and task specific!• Applications in navigational assistance,
personalisation, recommendations!• Best-trails i.e. most popular pathways
followed!– GPS data from London Zoo!
• Daily activity patterns!– Reality Mining data!
Shen et al, UC Davies
Experience Recorder
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Prediction"
• Predict driver destination!• Use dense grid to identify
locations!• Metric representations of space
extremely costly!• Machine learning to identify
common behaviours!• Used for navigational assistance!
Krumm et al Microsoft Research
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Navigational assistance"
• Find best route between two places!• Use data from an expert data set!• Taxi drivers are considered experts in
this task!• Navigate like a cabbie!• Similarities of geographic navigation
and web navigation!
Navigationzone.net
Ziebart et al, CMU
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Summarization"
• Reduce a complex data set to typical behaviours!
• GSM tracks over metropolitan area!• Cluster typical behaviours in profiles!• Use road graph to identify sequences!• Topological descriptions of space are
more efficient !
Adrienko et al Fraunhofer IAIS
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Our groupʼs point-of-view"
• Spatiality/physicality sets most constraints, thus the starting point !
• Reality is a semantic-spatiotemporal environment!– pervasive computing technology to capture user behavior!– identify significant landmarks and pathways!– trail-based processing!
• Core ingredients!– trails!– metrics of significance!– suffix-tree based algorithms!
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A landmark is"
• A location!– A scanning station!– A popular place!– A nodal point according to Space Syntax!
• A person!– A mobile phone-carrying individual!– A mote-tagged conference attendee!
• A (physical or data) object!– A URI!– An RFID-tagged artefact!
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Identifying landmarks"
• A-priori!– Defined by system-specific characteristics!– Bluetooth, WLAN, GSM etc access point!– RFID, mote or other tag!– Construction of space graph e.g. Space Syntax!
• A-posteriori!– Identify significance through use!– e.g. Minimum Volume Embedding Algorithm !
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Experiments on 3 main data sets"
• Dartmouth University!– campus-wide wifi network!
• Reality Mining!– User movement over a mobile phone network!
• Cityware!– Bluetooth scanning at Bath!
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Landmark analytics"
• Statistics per landmark !
• Total number of visits!• Visit frequency!• Average and total dwell time!• Per hour, per day, per week etc!
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Trail analytics"
Best trails using different metrics!– frequency, time, orientation, hybrid!
and constraints!– start and end at specific landmark!– passes through specific landmark!– minimum, maximum, exact trail length!– time of day, week, month etc!– nodes tagged with specific meta-data!– user-specific !
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Examples (1/4)"
Top-10 trails by frequency
Dartmouth data set
Wi-Fi associations
3-year period
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Examples (2/4)"
Top-3 trails by time
Exact length 3
Top-3 trails weighted
Exact length 3
Cityware data set, 3-month period
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Examples (3/4)"
Hard to interpret visually
Nodes are individuals
Trails show patterns of contact
Top-10 trails by frequency
At least 7 different landmarks
Intel imote data set
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Examples (4/4)"
Concept drift: best-trail evolution over time
Reality-mining data set
Popular trails algorithm
Mobile phone (cellular and Bluetooth) over 9 months
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Hit and Miss results"
Using all trails in the data set. Using best trails only.
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Identify individual without ID"
Reality-mining data set
Identify user 39 using 2 months for training and test on next month
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Summary"
• New model for WSANs!• Data capture and connectivity to the IoT!• Significant developments in recent years!• Analytics, prediction, classification!