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Olga Saukh, David Hasenfratz, Christoph Walser and Lothar Thiele Computer Engineering and Networks Laboratory, ETH Zurich saukh@tik.ee.ethz.ch 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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