Implementing positioning services over an ubiquitous infrastructure

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Implementing positioning services over an
ubiquitous infrastructure

• Authors D. Cotroneo, S. Russo, F. Cornevilli,
  M. Ficco, and V. Vecchio
• From Proceedings of the 2nd IEEE workshop
  on software technologies for future
  embedded and ubiquitous systems 2004
• Date 2004/11/09

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Outline

• Introduction
• The proposed approach
• Overall architecture
• Implementation issues
• Preliminary results
• Conclusions and future work

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Introduction

• Ubiquitous computing is rapidly emerging
• Giving the right information at the right moment
  to users.
• Not all life situation require the maximum
  achievable accuracy

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• RF-based wireless LANs system is used for
  locating and tracking user inside buildings
  RADAR
• Local positioning using Bluetooth
• Ultrasonic positioning system for determining
  location of the nodes (hop-by-hop) DOLPHIN
• APS, AhLOS
• Self positioning algorithm GPS-free

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• Triangle-based approach is inadequate
• It is not robust to interference of similar
  frequencies and to wall presence.
• It suffers of measurement errors which are
  linearly dependent from the RSS
• We proposed an architecture which considers each
  zone of the environment as a bubble.
• Estimate zones with a power level based
  positioning approach

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The proposed approach

• Zone we consider each room of the building as a
  bubble of radius r.
• Each bubble is covered by Bluetooth or Wi-Fi
  network sensors.
• Mobile device acquires information about the
  received signal strength (RSS) and calculates the
  distance from each network sensor.

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• Triangle-based approach
• Strong interference with thick wall
• More than 3 APs to improve accuracy (complex
  positioning estimation and high cost)
• Zoning approach
• More tolerant to error (Assume that we are not
  interested in the exact position of a mobile
  device and it does not require heavy
  computational activities)

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Overall architecture

• Connectivity layer
• Finding and connecting in range Bluetooth and
  Wi-Fi devices.
• SS_Measurement layer
• Read received signal strength

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• RX_Power layer
• It converts the measured SS to RX power levels
• Zoning layer
• It is responsible for converting the RX power
  levels into a position information
• User layer
• The positioning estimation is provided to the
  user application

RX received
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Implementation issues

• Experiment testbed
• Lab.(15m 40m, 6 rooms)
• Sensors are placed in the middle of each room
• Hardware Compaq iPAQ 3970 mobile device with
  Bluetooth and 802.11 modules
• OS Linux Familiar 0.7.0.
• Sensors ANYCOM Bluetooth dongle/ Printer and
  Orinoco AP 802.11b.

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implementation

• Golden Receive Power Range (GRPR)
• Optimal RX power range
• If RSSI gt 0, RX power level gt GRPR
• If RSSI lt 0, RX power level lt GRPR
• If RSSI 0, RX power level is inside GRPR
• GRPR is limited by upper and lower thresholds. An
  RSSI can be converted to RX power level only if
  upper and lower threshold levels are known.

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30
20
10
0
-10
-90
-80
-70
-10
-20
-30
0
-60
-50
-40
RSSI to RX power level conversion of the ANYCOM
Bluetooth dongle
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• RX power levels conversions into a position
  depends on the tested neighboring sensors.
• where P(d) is the RX power level at distance d
• d0 is a reference distance
• P(d0) is the RX power level at distance d0
• the variable n describes how the RX power
  level decrease with the distance.
• d is the distance from the neighboring sensor
• d0 is the reference distance (1m).

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Scenario 1

• Mobile device moves from Zone 4 to Zone 3.

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Scenario 2

• Mobile device moves from Zone 3 to Zone 4.

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B Bluetooth W WaveLAN
BZ bordering zones R radius used
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Preliminary results

• In order to consider interference issues between
  Bluetooth and Wi-Fi radio frequencies, we
  considered the ß range as an indeterminate zone.
• It does not influence in our system.
• If the device is coming from Z3, it is assumed to
  remain in Z3 in the ß range.
• If the device is coming from Z4, it is assumed to
  belong to Z4 in the ß range.

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Signal strength transition from Z3 to Z4.
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Conclusions and future work

• Idea
• Estimate building zones with a power level based
  positioning approach
• It is independent from the devices used and form
  the wireless infrastructure
• Future work
• Investigate strategies for RSSI noise reduction
• Exporting APIs in the Java language
• Leveraging the robustness of the provided
  positioning services