distributed intelligent systems – w12: distributed sensing ... · • power-efficient resource...

Distributed Intelligent Systems – W12:Distributed Sensing using

Sensor Networks –Power-Efficiency and

Mobility

1

Outline• Basic principles for energy saving in static

sensor networks• Power-efficient resource management

– Clustering and pruning– Temporal and spatial suppression

• Mobile sensor networks– The OpenSense project

• Robotic sensor networks– General challenges– An example of distributed search application

2

Distributed Sensing: Problem Statement

Physical domain- engineered or natural- bounded or unbounded- 2D or 3D

Possible missions − searching− coverage− inspecting− patrolling− mapping− …

+ follow-up actions(e.g., cleaning, destroying, repairing)

Devices:- networked- mobile

- size, cost- number

3

Basic Principles for Energy-Saving Design

in Static Wireless Sensor Netwokrs

4

Generalization: Friis Laws

• Basic Friis law (open environment) Pr = received powerPt = transmitted powerGt = gain transmitting antennaGr= gain receiving antenna = signal wavelength R = distance emitter-receiver

• Modified Friis law (cluttered, urban environment)

n between 2 and 5!

f = c/!

5

See also W7

Communication/Computation Technology Projection

Assume: 10kbit/sec. Radio, 10 m range.

Large cost of communications relative to computation continues

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

Source: ISI & DARPA PAC/C Program

6

Communication Power Budget: Not only Transmission

• In general transceiver power consumption dominated by listening (radio on)

• ChipCon CC2420: 19.7 mA in receiving, 17.4 mAtransmitting @ 0 dBm (2.4 GHz)

• SemTech SX 1211: 3 mA in receiving, 25 mA @ +10 dBm in transmitting (900 MHz)

• Synchronization is key• Low power listening protocols are key for

saving power 7

Efficient Monitoring in Sensor Networks

8

MotivationMinimize energy resources used – Maximize field accuracy obtained

sample as little as possible

in space

in time

communicate as little as possible

# of messages

comm. radius

9

Opportunities for Spatiotemporal Suppression in Environmental Data

0 1 2 3 42

4

6

8

10

12

Time (days)Te

mpe

ratu

re(°C

)0 0.2 0.4 0.6 0.8

0

0.25

0.5

0.75

1

Distance (km)

Ambient temperatureSurface temperatureRelative humidity

Cor

rela

tion

coef

ficie

nt

10

Clustering and Threshold-Based Pruning

11

Case study: Estimating an acoustic field

N1 N2 N3 N4

N5 N6 N7 N8

N9 N10 N11 N12

N13 N14 N15 N16

sensing cellsensing cell

• 2n sensing cells• 2n sensor nodes• 1 static sound source

sensor nodesensor node

(1) MSE (data quality)(2) A (number active nodes)

)A,MSE(objfObjective function:

Performance metric:

12

• Multiple roles → layers• Hierarchy:

• Bottom-up measurements• Top-down control

Hierarchical Topology – Quadtree

L2

L1

L0

clusterheadclusterhead

leaf nodeleaf node

Network level

13

• Messaging protocol dependant on:• sender-ID • sender-layer

node

measurements

controlN1 N2 N3 N4

N5 N6 N7 N8

N9 N10 N11 N12

N13 N14 N15 N16

Communication channels

• Channels given by quadtree

Hierarchical Topology – Quadtree

Single node level

14

Distributed Control – State Machine

sample broadcast

process

idlePRUNED

¬ PRUNED

L current = L kmax

L current ≠ L kmax

L current ≠ L kmax

L current = L kmax

• Layer increment: if all child nodes processed• Idle node: if pruned by clusterhead• Data: clusterhead replaces pruned children

When do I prune?

15

From a Centralized Formula to Distributed Control

[1] R. Nowak et al. Estimating inhomogeneous fields using wireless sensor networks. IEEE Journal on Selected Areas in Communications. 22(6):999-1006, August 2004.

ik

k

k

k

ik

k

k

n

CjLjk

kLi

Li

CjLj

Ln

fn

xfRpssT

sTxfR

npsxfR

npsxfRf

npsxfRf

,

1

,

1

)),((6)(

)()),((

)(2)),((

)(8)),(()(ˆ

)(2)),((argminˆ

2

2

2

2

),(

Centralized formulation [1]s: signal noise (homogeneous)p: monot. increasing function of n|θ|: number of active nodes in cluster

Threshold on layer k, with Ck,i set of child nodes at layer k of node ip: constant

approx. error complexity penalizer

Threshold-based pruning

propagated approx. error

if not pruning

if pruning

Distributed control?

16

An Intuitive IllustrationNaïve Sampling Threshold-based Sampling

17

Experimental Setup

The e-puck robot:• Trinaural microphone array (28.8 Hz)• Short range communication

• subset of 802.15.4 Zigbee• comm. range: 10cm - 5m

Setup:• 1.5m x 1.5m arena • 16 e-puck nodes (static sensor stations)• 1 static sound source (white noise, const. intensity)

18

Experimental Results

Number of active nodes MSESetup: • 10 runs, variable source placements / threshold • 12 thresholds, with s in [0..12’000]• Model with tx = 0.3 19

Spatiotemporal Suppression of Data

Reporting

20

Saving Communication Energy via Efficient Reporting

Temporal suppression• Has my value changed recently?

Spatial suppression• Are my neighbors reporting similar measurements?

21

Ex: Barebones temporal suppression (field change in gray)

Temporal Suppression

22

Ex: Barebones temporal suppression (reporting node in red)


23

Ex: Barebones temporal suppression


24

Ex: Barebones temporal suppression


25

Monitor edge constraints instead of individual nodes

Efficient Monitoring

26



27



28



29

Constraint Chaining

30

• Suppression-based algorithm: Constraint Chaining (Conch) [1]

• Monitor edge constraints instead of sensor values• Historical data to identify performant edges

[1] A. Silberstein, R. Braynard, and J. Yang. Constraint Chaining: on energy efficient continuous monitoring of sensor networks.ACM SIGMOD, 2006.

39

"Conch plan"

Testbed

75m

40m

40

In-Network Power Monitoring

Sensors:• Battery voltage• Solar voltage• Incoming solar current• Datalogger current• Power board current• Sensor chain current

41

Results with Basic CONCH

42

• Four weeks on the outdoor testbed• Limited adaptivity to network changes

• Replanning costs O(|E|) + GPRS• Unstable network led to most nodes

reporting directly to the sink• 45% of messages suppressed while

algorithm was functional

43

Results with Advanced CONCH

• Autoregressive model for more compressed comparison of differences:

• AR-Conch: 57% suppression rate

• Autoregressive model + in-network distributed implementation for enhanced adaptive behavior

• AR-DConch: 64% suppression rate

44

Concrete Energy SavingsNot significant since:• Radio on for several seconds (overhearing)• Transmission takes 6 ms max• Two minutes idle at ~11.8mW

Energy savings given fixed 50% suppression

rate (calibrated simulation in TOSSIM)

Our settings

Mobile Sensor Networks:The OpenSense Project

45

Importance of Air Quality

On March 25, 2014, the WHO reported:

“… in 2012 around 7 million people died – one in eight of total deaths – as a result air pollution exposure. This finding more than doubles previous estimates and confirms that air pollution is now the world’s largest single environmental health risk. ”

http://www.who.int/mediacentre/news/releases/2014/air‐pollution/en/

“The new estimates are not only based on more knowledge about the diseases caused by air pollution, but also upon better assessment of human exposure to air pollutants through the use of improved measurements and technology.”

46

Urban Air Pollution

Air pollution in urban areas is a global concern• affects quality of life and health• urban population is increasing

Air pollution is highly location-dependent• traffic chokepoints• urban canyons• industrial installations

Air pollution is time-dependent• rush hours• weather• industrial activities

47

Objectives in Air Pollution Monitoring

Accurate location-dependent and real-time information on air pollution is needed

Officials• public health studies• environmental engineers: location of

pollution sources• municipalities: creating incentives to reduce

environmental footprint

Citizens • advice for outside activities• assessment of long-term exposure• pollution maps

OpenSense ultra fine particle levels map in Zürich during winter months

48

Monitoring Today

Stationary and expensive stations

Expensive mobilehigh fidelity equipment

Sparse sensor network(Nabel)

Coarse models(mesoscale = 1km2)

1E5

2E5

3E5

4E5

particles

/cm

3

08:0

0

09:0

0

10:0

0

11:0

0

12:0

0

13:0

0

14:0

0

15:0

0

16:0

0

17:0

0

Personal exposure with specialized punctual studies

Garage

Vehicle

Road

Indoor

49

High resolution urbanatmospheric pollution maps

Model inputTerrain, Meteorology, Source strength, Background

Measurement dataCrowd‐Sensors, mobile sensors, monitoring stations

ModelingLagrangian dispersion models, Data‐driven methods

50

System Vision

OpenSense: Deployments10 streetcars in Zurich & 10 buses in Lausanne• CO, NO2, O3, CO2, UFP, temperature, humidity• Active sniffing & closed sampling system in

Lausanne• Localization: GNSS for trams, augmented GNSS

for buses • Communication: GPRS

On top of C-Zero electric vehicle• 100% electric, flexible mobility• system test bed, targeted investigation tool

On top of “LuftiBus”• Since March 2013, covers whole Switzerland

At NABEL stations in Dübendorf & Lausanne• Calibration and sensor drift evaluation• Testing new sensors

51

OpenSense: Lausanne Node

Particle sampling module• Ultrafine particle

measurements using NaneosPartector

• Measures directly lung-deposited surface area

Gas sampling module• CO, NO2, O3, CO2,

temperature & relative humidity

• Hybrid active sniffer/closed chamber sampling operation

• Enables absolute concentration mobile measurements

Enhanced localization & logger• mounted inside bus• Fused GPS, gyro and vehicle

speedpulses• Accurate sample geolocation even in

difficult urban landscapes• GPRS communication

52

OpenSense: Zurich Node

Inside the OpenSense Zurich node

OpenSense Zurich node

Installation on top of VBZ Cobra tram

53

Personal Mobile Sensing

Smartphone connected to ozone sensor and various application software for Android

[Hasenfratz et al., Mobile Sensing 2012] [Predic et al., PerCom 2013]

Zurich prototype Lausanne prototype AirQualityEgg

Low-cost devices for home deployment (calibration tests close

to a NABEL station)

54

Localization Accuracydoors open Current stop: Sallaz

Next stop: Valmont

Next stop: Sallaz

• Accurate chemical sampling requiresaccurate positioning

• Low-cost, embedded sub-meter accuracyin urban settings requires sensor fusion &light map matching algorithms

• Large set of rich data (stop coordinates,heading, odometer, acceleration, vehiclecontext data, etc.)

55

Calibration Procedure• Gas sensor drift (aging) -> periodic

recalibration needed• Gas sensors are installed on

mobile vehicles• Few expensive reference stations

within city limits• Two recipes:

– Calibration upon rendezvous of mobile vehicles and references

– Passing of calibration data from vehicle to vehicle: Multi-hop Calibration

[Hasenfratz et al., EWSN 2012]

56

Choice of Models

Data-driven statistical and machine learning

Context-aware machine learning

Physics-based

Types of models

57

Street segment-based space discretization better suited than grid-based

Types of models considered so far:• Log-linear regression –

NABEL station & meteorological explanatory variables only

• Network-based log-linear regression – explanatory variables + measurements on other segments

• Probabilistic Graphical Model

[Marjovi et al., DCOSS’15]

IDEA: Use sensor measurements in conjunction with other explanatory data to interpolate/extrapolate pollution data

58

Data-Driven Models

Setup• GRAMM (Graz Mesoscale Model): mesoscale flow

simulations for city region (100 m resolution) accounting for topography & land use

• GRAL (Graz Lagrangian Model): flow and air pollutant dispersion simulations at building resolving scale (5 m) for city area forced by GRAMM meteorological data

Achievements• Successful setup for Lausanne, including

preparation of emissions and other inputs• Hourly maps for NO2 pollution generated,

good match with observations

Next steps• Extension to other pollutants (e.g., PM10/PM2.5)• Setup for Zürich

[Berchet et al., under review] 59

Physics-based Dispersion Model: The GRAMM/GRAL Modeling System

Robotic Sensor Networks

60

Typical Applications– Cleaning, mowing

(Jaeger & Nebel 2002)

– Communication backbone (McLurkin & Smith 2006)

– Guiding facility for other robots (Payton, Estkowski & Howard 2003)

– Distributed sensor (Schwager, McLurkin & Rus 2006)

– Mapping(Zlot, Stentz, Dias & Thayer, 2002), (Burgard, Moors, Stachniss & Schneider, 2005)

– Urban search and rescue(Kumar, Rus & Singh 2004)

© Husquarna

© Andrew Howard, USC/JPL

© James McLurkin, MIT/iRobot

© Kumar, Rus & Singh © Zlot, Stentz, Dias & Thayer61

Typical Performance Metrics• Coverage: spatial exhaustiveness > time of completion

(i.e. minimize redundant coverage but better redundancy than uncovered areas)

• Search: time of completion > spatial exhaustiveness (speed is the most important issue)

• Depending on a priori knowledge (e.g., target features, # of targets) some overlap possible

• Both problem classes should be solved by minimizing system resources (e.g., energy, # of robots, node cost)

62

Case Study:Distributed Odor Localization

63

Odor Source Localization

Wind flowOdor source

e.g.:leaking gas pipebomb / minefood

64

Odor Source Localization

Khepera III robot with odorand wind direction sensor

Wind flowOdor source

e.g.:leaking gas pipebomb / minefood

65

Odor Source Localization • Given:

– Area to search (GPS bounds, enclosures, etc.)– Number of robots and hardware capabilities

• Three tasks (or phases though not always serial):– Plume finding– Plume traversing– Source declaration

66

Plumes: A Tricky Field to Traverse

Courtesy by L. Marques, simulated plume

67

Algorithms

biased random walk

infotaxis

probabilisticmax. likelihood

function optimization(PSO, ...)

Braitenberg vehicle

surge-spiral

surge-cast

castingprobabilisticrobotic search

dung beetle

silkworm

computationally cheapcomputationally cheap computationally expensivecomputationally expensive

probabilisticodor compass

crosswindformation

68

Embedded Olfaction and Anemometry• Khepera III robot (K-Team SA):

− 13 cm diameter − differential-drive− 400 MHz ARM CPU− No floating point unit (FPU)

• Odor sensor board− VOC sensor, ppm level− Open sampling system−Actively sniffing (micro-pump)

• Wind direction sensor board− Thermistor-based array−Accuracy < 10 degrees

69

Systematic experiments inthe wind tunnel

Odor source (ethanol)

Arena: 18 x 4 mWind speed: 1 m/s (~laminar)

70

Single Robot Algorithms

71

Bio-Inspired AlgorithmsState machines, reactive

Combination of surging, casting, spiraling

Binary odor concentration(in plume, not in plume)

Wind direction

72

Surge-Spiral: Algorithm

Wind flow

Surge-spiral

Wind direction measurement

73

Surge-Spiral: Real Robot Implementation

74

Surge-Spiral: Sample Trajectory

Upwind in the plume, spiraling for reacquisition

75

From Single Robot to Multi-Robot Algorithms

76

With Collaborationunpublished

N robots

N runs with 1 robot

1 robot

Dis

tanc

e ov

erhe

ad (l

ower

is b

ette

r)

Distance overhead = Dsf/Dmin normalized for single robot 77


N robots

N runs with 1 robot

1 robot

Dis

tanc

e ov

erhe

ad (l

ower

is b

ette

r)

78


Improve? Redesign?

79

Improve? Redesigned solution!

Crosswind formation

[Lochmatter et al, DARS 2010] 80

Most Recent Results

81[Soares et al., ICRA 2015]

Comparative Performance Evaluation (6 Algorithms)

82

Conclusion

84

• Environmental data are (usually) highly redundant• Intelligent node behavior allows us to remove that

redundancy and save energy• Well studied in literature, difficult to bring to real

systems• Design for dynamic environments is difficult because

both dynamic environmental processes and dynamic network conditions must be considered

• Network stack may limit potential gains in energy saving of the intelligent algorithm; it is often a question of robustness versus efficiency

85

Take Home Messages

Take Home Messages• Two type of mobility in sensor networks: parasitic

(exploit a carrier) and controlled (robotic node)• The advantages of mobile sensor networks vs. static ones

lies in their deployment and gathering mechanisms, wider coverage with the same number of nodes, and possibly targeted reconfiguration (robotic nodes)

• Their drawbacks are additional complexity at the node level (to handle mobility) and increased power consumption (because of self-locomotion when available)

• Networking plays a crucial role both from an inter-node coordination perspective as well as gathering data at the user interface

• Target localization, coverage, mapping, and inspection are concrete applications for mobile/robotic sensor networks

86

Additional Literature – Week 12• Lai T. T.-T., Chen W.-J., Chen Y.-H. T., Huang P., and Chu H.-H., “Mapping Hidden

Water Pipelines Using a Mobile Sensor Droplet”, ACM Trans. Sensor Networks, 9(2): Article 20, 2013.

• Schwager M., Detweiler C., Vasilescu I., Anderson D. M., and Rus D., “Data-Driven Identification of Group Dynamics for Motion Prediction and Control”.J. of Field Robotics, 25(6-7):305-324, 2008.

• Saukh O., Hasenfratz D. , and Thiele L., “Route Selection for Mobile Sensor Nodes on Public Transport Networks”. J. of Ambient Intelligence and Humanized Computing, 2013.

• Hasenfratz D., Saukh O., and Thiele L., “Model-Driven Accuracy Bounds for Noisy Sensor Readings”. Proc. of the 9th IEEE Conference on Distributed Computing in Sensor Systems, Cambridge, MA, USA, 2013.

• Correll N. and Martinoli A., “Multi-Robot Inspection of Industrial Machinery: From Distributed Coverage Algorithms to Experiments with Miniature Robotic Swarms”. IEEE Robotics and Automation Magazine, 16(1): 103-112, 2009.

• Rutishauser S., Correll N., and Martinoli A., “Collaborative Coverage using a Swarm of Networked Miniature Robots”. Robotics and Autonomous Systems, 57(5): 517-525, 2009.

• Amstutz P., Correll N., and Martinoli A., “Distributed Boundary Coverage with a Team of Networked Miniature Robots using a Robust Market-Based Algorithm”. Special Issue on Multi-Robot Coverage, Search, and Exploration, Kaminka G. A. and Shapiro A., editors, Annals of Mathematics and Artificial Intelligence, 52(2-4): 307-333, 2009. 87

Additional Literature – Week 12• Hayes A. T., Martinoli A., and Goodman R. M., “Swarm Robotic Odor Localization:

Off-Line Optimization and Validation with Real Robots”. Special issue on Biological Robotics, McFarland D., editor, Robotica, 21(4): 427-441, 2003.

• Lochmatter T., Raemy X., Matthey L., Indra S. and Martinoli A., “A Comparison of Casting and Spiraling Algorithms for Odor Source Localization in Laminar Flow”. Proc. of the 2008 IEEE Int. Conf. on Robotics and Automation, May 2008, Pasadena, U.S.A., pp. 1138 – 1143.

• Lochmatter T. and Martinoli A., “Theoretical Analysis of Three Bio-Inspired Plume Tracking Algorithms”. Proc. of the 2009 IEEE Int. Conf. on Robotics and Automation, May 2009, Kobe, Japan, pp. 2661-2668.

• Lochmatter T. and Martinoli A., “Understanding the Potential Impact of Multiple Robots in Odor Source Localization”. Proc. of the Ninth Int. Symp. on Distributed Autonomous Robotic Systems, November 2008, Tsukuba, Ibaraki, Japan; Distributed Autonomous Robotic Systems 8 (2009), pp. 239-250.

• Soares J. M., Aguiar A. P., Pascoal A. M., and Martinoli A., “A Graph-Based Formation Algorithm for Odor Plume Tracing”. Proc. of the Twelfth Int. Symp. on Distributed Autonomous Robotic Systems, November 2014, Daejeon, Korea, Springer Tracts in Advanced Robotics (2016).

88

distributed intelligent systems – w12: distributed sensing ... · • power-efficient resource...

Documents