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On Rendezvous in Mobile Sensing Networksolgasaukh.com/paper/saukh13rendezvous_slides.pdf · 2017....
Transcript of On Rendezvous in Mobile Sensing Networksolgasaukh.com/paper/saukh13rendezvous_slides.pdf · 2017....
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Olga Saukh, David Hasenfratz, Christoph Walser and Lothar Thiele Computer Engineering and Networks Laboratory, ETH Zurich [email protected] RealWSN 2013, Como Lake, Italy
On Rendezvous in Mobile Sensing Networks
19.09.2013 Olga Saukh 1
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§ WSNs are successfully being used in a number of long-term installations
§ Static deployments § Sensors are installed at carefully chosen
locations § High temporal but low spatial resolution
§ Mobile deployments § Sensors moving randomly or along predefined
routes § Increased spatial coverage at a price of temporal
coverage § Enable aperiodic rendezvous between mobile
sensors
Sensor Network Deployments
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PermaSense, Beutel et al., IPSN’09
Torre Aquila, Ceriotti et al., IPSN’09
Road Tunnel, Ceriotti et al., IPSN’11
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§ How to ensure … § … data quality in mobile networks? § … fault tolerant sensor operation? § … periodic sensor calibration?
§ Possible solutions: § Model-based data validation schemes § Careful design and exploitation of rendezvous
between mobile sensors
Mobility in Sensing Networks
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Static air quality monitoring station
Luftibus with OpenSense node
10 streetcars in Zurich equipped with OpenSense nodes
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§ Communication range based rendezvous are binary § Sensing range based rendezvous
§ Phenomenon-based § Continuous
§ Given temporal and spatial distances between two sensors, what can be concluded about the similarity of their respective measurements?
§ How close should two sensors come together so that one can expect their measurements to be similar?
Sensing Range Based Rendezvous
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§ Dataset: 1st March – 31st May § 10 streetcars § 2 reference stations:
§ 4m and 16m from streetcar tracks
§ Sampling intervals: § Temperature, humidity, ozone: 30 sec; § CO, internal temperature: 1 minute;
§ Data filtering § HDOP filter – low GPS signal quality § Indoor filter – inside depots and factory
OpenSense: Air Quality Monitoring in Zurich
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OpenSense node
Deployment on top of a streetcar
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§ 9 out of 10 streetcars are online every day on average § One exception is ID=1, stayed in
depot for several weeks after an accident
§ A streetcar is online 20 hours a day § Operation time: 5am-1am
§ 4.3 million data over 3 month period
Deployment Evaluation
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§ Streetcars change routes almost every day
§ High probability of meeting other streetcars
Deployment Evaluation
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§ A rendezvous is a temporal and spatial vicinity of two sensors § Temporal and spatial locality of a physical process impacts the number of
rendezvous between sensors, their duration, and their frequency
Rendezvous
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- measurements taken by sensor u
- measurements taken by sensor v
temporal distance between the measurements
spatial distance between the measurements Rendezvous
Rendezvous pairs
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§ A rendezvous is a temporal and spatial vicinity of two sensors § Temporal and spatial locality of a physical process impacts the number of
rendezvous between sensors, their duration, and their frequency
§ Important observation:
We expect spatially and temporally close sensor readings to be similar (!)
Rendezvous
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time
time
temporal and spatial closeness
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§ Given a set of rendezvous pairs Φ (| Φ | > 1000), compute Pearson correlation coefficient to quantify the similarity (linear dependency)
§ Does not need the sensors to be calibrated if calibration curve is linear § Prone to sensor noise
§ Aggregate measurements before computing correlation (!)
Data Correlations without Data Aggregation
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Data Correlations with Data Aggregation
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§ A pair of sensors makes 220 parallel measurements per day § The total number of pairwise rendezvous is high, but
§ Varies considerably over time § Depends on the pair of sensors
§ For the chosen setting, high number of rendezvous with reference stations § However, not all sensors pass by a reference station
Rendezvous, fixed
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§ A large number of rendezvous pairs does not ensure that one can compare measurements of any two sensors in a mobile network
§ Rendezvous connection graph as an undirected graph with a set of sensors as its vertices, and a set of edges between sensors, which make a rendezvous (| Φ | > K)
§ Connectivity of the rendezvous connection graph is required for identifying sensor failures and updating sensor calibration
Rendezvous Connection Graph
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Application: Sensor Fault Detection
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Obviously faulty internal temperature sensor on device 3. Average corr. 0.21 Average correlation among correct sensors: 0.96
Faulty humidity sensor on device 6. Average corr. 0.32 Average correlation among correct sensors: 0.98
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Application: Sensor Calibration
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§ Reduce measurement error by calibrating the sensor when passing by one of the reference stations
§ One day of measurements is taken to compute a new calibration curve
Device 7: Temperature sensor improves calibration from 2.1±1.6ºC to 0.4±0.5ºC Ozone sensor improves calibration from 10.5±5.3ppb to 4.2±5.1ppb Mean error after calibration is 0
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§ Operates on the rendezvous connection graph to calibrate the sensors § On average, 6.5 sensors are calibrated per day § Average calibration errors: temp.: 0.8±0.6ºC, ozone: 6.6±6.1ppb
Application: Sensor Network Calibration
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Network calibration error distribution (4 months)
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§ Rendezvous § … relate temporal and spatial closeness of two measurements with
their similarity § … depend of the locality of the process of interest § ... parameters define the number of rendezvous, their frequency,
and connectivity of the rendezvous connection graph § … can be successfully used for
§ Detecting sensor faults § Sensor and sensor network calibration
Conclusions
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www.OpenSense.ethz.ch
Thank You for Your Attention!
Any Questions?
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