self–localization of wireless sensor nodes by means of autonomous mobile robots a note on the use...

Self–localization of Wireless Sensor Nodes

by means of Autonomous Mobile Robots

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www.dei.unipd.it/~zanella

Thanks and enjoy!

A note on the use of these ppt slides:We’re making these slides freely available to all, hoping they might be of use for

researchers and/or students. They’re in PowerPoint form so you can add, modify, and delete slides (including this one) and slide content to suit your needs. In return for use,

we only ask the following:If you use these slides (e.g., in a class, presentations, talks and so on) in substantially

unaltered form, that you mention their source.If you post any slides in substantially unaltered form on a www site, that you note that they are adapted from (or perhaps identical to) our slides, and put a link to the authors

webpage:

www.dei.unipd.it/~zanella

Thanks and enjoy!

Department of Information Engineering – University of Department of Information Engineering – University of Padova – ItalyPadova – Italy

Self–localization of Wireless Sensor Nodes by means of Autonomous

Mobile Robots

Andrea Zanella, Emanuele Menegatti and Luca Lazzaretto

2007 Tyrrhenian Workshop on Digital Communications 2007 Tyrrhenian Workshop on Digital Communications

TIWDC 2007TIWDC 2007

This work was supported by the University Project “RAMSES2: integRation of This work was supported by the University Project “RAMSES2: integRation of Autonomous Mobile robots and wireless SEnsor networks for Surveillance and Autonomous Mobile robots and wireless SEnsor networks for Surveillance and

reScue”reScue”

12 September 2007 3 Andrea Zanella

WSN vs AMRWSN vs AMR

• Pros– Low cost (hundreds of devices)

• Cons– Limited computing capabilities– Limited memory– Limited energy capacity– Limited transmission

range/speed– No or very limited mobility

• Cons– High cost (a few devices)

• Pros– High computational capabilities– Large memory– Large energy capacity– Large transmission range/speed– Advanced mobility

WSN: Wireless Sensor NetworkAMR: Autonomous Mobile Robot

RAMSESRAMSES22


RAMSESRAMSES22

• RAMSES2: integRation of Autonomous Mobile robots and wireless SEnsor networks for Surveillance and reScue

• WSN monitors strategic areas– earthquakes, fire, land/snow-slide, chemical hazards,...

• In case of danger, AMRs team is activated – WSN rise a fire alarm?

AMR squad is driven by the WSN to the hot area AMR with fire extinguishers cooperate to extinguish the fire Other AMRs establish ad hoc multihop connection to stream

high–quality video to a control centre


AMR AMR WSN: AMR–aided WSN maintenanceWSN: AMR–aided WSN maintenance

• AMR can work as a data mule, collecting data from nearby nodes and, then, releasing them in another location, perhaps over connectivity holes– Improve WSN connectivity– Alleviate energy consumption– Increase data reliability

• AMR can be used to place new nodes in the WSN where needed

• A single AMR equipped with sophisticated and reliable transducers can be used to calibrate the cheap transducers of WSN nodes

• AMR can be used to improve self-localization of WSN nodes


Self-localizationSelf-localization

• Problem statement:– A bunch of sensor nodes are hand-placed in a given

room– Each node needs to localize itself with respect to a

common reference system– Nodes are only equipped with RSSI transducer– Localization error shall be reduced as much as possible– WSN nodes shall dissipate as few energy as possible

• State of the art:– Plenty of localization algorithms in literature– Range-free

Not require ranging capabilities Good with dense networks and not very harsh propagation

environments Poor performance in indoor and low density WSN


Range-based self localizationRange-based self localization

• A few beacon (anchor) nodes are placed in know positions in the area

• Beacons periodically broadcast their own positions

• Other nodes try to estimate their distance from beacons and infer their own position on the area by using different methods


Range-based self localization: issuesRange-based self localization: issues

• Range-based localization problems– Very sensitive to ranging errors

Channel characteristics (shadowing, multipath, asymmetry,...)

– Very sensitive to loose calibration Nodes with identical setting may reveal differences in

transmission power or reception sensitivity Localization algorithms leveraging on cooperation among

different nodes usually neglect such calibration misalignments

– Extra hardware required for good performance (ultrasounds transceivers, multiple antennae, several beacons)

high costs reduced flexibility

– Very poor performance in indoor and low density WSN


AMR–aided WSN Self-localizationAMR–aided WSN Self-localization

• AMR can alleviate many of such problems!• How does it work?

– AMR moves in the room and estimates its own position using on-board motion sensors (odometers)

– Periodically AMR broadcasts its current positions

• Then? – The number of (virtual) beacons can be indefinitely

increased– Transmissions are performed by a single device, then

calibration issues are mitigated – AMR self-estimates its own position, then handy beacons

placement is avoided – AMR might support expensive equipments since they

have not to be replicated in several devices


Experimental Set upExperimental Set up

• EyesIFX sensor nodes– Infineon Technologies.– 19.2 kbps bit rate @ 868

MHz– Light, temperature, RSSI

sensors

SIGNET IAS• AMR Bender

– self-made, based on Pioneer 2 ActivMedia platform

– Linux OS with Miro middleware– ATX motherboard – 1,6 GHz Intel Pentium 4, 256

MB RAM, 160 GB HD

EyesIFX connected to ATX via USB + EyesService

class added to Miro

– Omnidirectional camera, odometers


Measurements settingMeasurements setting

• Empty corridor of 4.5m × 10m (ceiling 4m). • Robot moves along three parallel lines @ average speed 240 cm/s• Robot coordinates broadcasted every 50ms through the on-board EyesIFX

node• Ten static sensor nodes form an incomplete lattice• Nodes receive messages & store coordinates & RSSI


Channel modelChannel model

• Path loss channel model: received power Pi @ distance di

Received power

Transmitted power

Path loss coefficient

reference distance

environmental constant

real transmitter-receiver distance

Shadowing Shadowing

fast fading


How harsh is the indoor radio channel?How harsh is the indoor radio channel?

• Random variations due to shadowing and fading obscure the log-decreasing law for the received power vs distance

• RSSI based ranging is VERY noisy!


Channel model parameters fittingChannel model parameters fitting

• Low-pass filtering data we can extract the underlying log law

• The best fitting of the filtered measures with the theoretical relation gives

• PTx + K = −30.5 dBm = 1.5

• d0 = 10 cm

010log10d

dKPdBmP i

Ti x


Shadowing distributionShadowing distribution

• QQ.-plot shows that shadowing (in dB) is approximately Gaussian with

• μ=−0.0348 ± 0.0860 dB

= 6.339 ± 0.0614 dB• (95% confidence

interval)


Experimental results: multilaterationExperimental results: multilateration

• Multilateration is a range-based localization algorithm that offers– very basic calculations (low complexity) – limited memory occupancy– no need for node transmissions reduce interference & energy cost

• In theory– For each received message, nodes trace a circle centered on the beacon

and having radius equal to the estimated distances from the beacon– Ideally, circles intersect in a single point on a surface which gives the

node location• In practice

– Nodes have limited computation capabilities area is divided in cells– Channel impairments require to consider rings instead of circles– For each received message, nodes increased by one the weight of the

cells covered by the ring centered on the beacon and having radius equal to the estimated distances from the beacon

– The node us located within the cells that scores the maximum weight


Results: localization with N virtual Results: localization with N virtual beaconsbeacons

• Multilateration on RSSI samples randomly picked from the full data set

• Conversely to what expected, more samples do not improve localization!


Why taking highest RSSI?Why taking highest RSSI?

Noise free RSSI

RSSI+=RSSI + ||

RSSI-=RSSI - ||

d+ d-


Results: localization with the Results: localization with the “highest” “highest” RSSI RSSI

• Localizing over sorted RSSI yields much better performance– Ranging errors

are more relevant when considering lower RSSI values due to the logarithmic nature of the path loss model

• Localization improves as the number of samples reduces!


ConclusionsConclusions

• RSSI-based localization show very poor performance in indoor environments– Shadowing, fading, calibration errors, ...

• Although AMR can alleviate some of the primary causes of localization errors, standard localization techniques still yield poor performance in indoor environment!

• Nevertheless, the possibility of drastically enlarging the number of collected samples and the greater computational capabilities of AMRs permit to define more performing algorithms!


DiscussionDiscussion

Question time!Question time!Thanks for the attentionThanks for the attention

self–localization of wireless sensor nodes by means of autonomous mobile robots a note on the use...

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