Ahmed Tarek1, Basem Amer2, Prof. Aboelmagd Noureldin3
Navigation and Instrumentation Research Group1 College of Engineering & Technology , Arab Academy for Science & Technology, Egypt.2 Sabratha Engineering College, Az-zawia University, Libya3 Royal Military College of Canada | Queen’s University, Canada
Intelligent Indoor Localization System Utilizing Zigbee
Wireless Networks and Inertial Sensor Integration
Outline
• Introduction
• Indoor positing System (IPS) Techniques
– Non-radio technologies
– Wireless technologies
• Research Objective
• Proposed System
• Experiment
• Results
• Conclusion
• Future Work
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Introduction
• What is Indoor positing System (IPS)?
– Indoor positioning system (IPS) is a system to locate objects or people inside a building using radio waves, magnetic fields, acoustic signals, or other sensory information collected by mobile devices.
• Why we use IPS?
• Uses of IPS
– Self localization
– People localization
– Assets localization
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IntroductionApplications of IPS
Shopping MallsSchools Hospitals
Universities Train Stations
Prisons
Museums
Airports
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IPS Techniques
• Non-radio technologies
– Pedestrian Dead reckoning (PDR)
• Wireless technologies
– Wi-Fi-based positioning system (WPS)
– Bluetooth
– Visible light
– Zigbee Wireless Networks
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IPS Techniques
• Non-radio technologies
– Pedestrian Dead reckoning (PDR)
• What is PDR?– It is the process of calculating one's current position by using a
previously determined position
• Why we use PDR?– Available with most of the people
– Low cost solution
– Provide good accuracy in short paths
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IPS Techniques
PDR
Distance
Count Steps
X-Accelerometer
X Y-Accelerometer
X Y Z-Accelerometer
Step Size
Azimuth
Gyroscope
Compass
Integration of Gyro and
Compass
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IPS Techniques
• Wireless technologies– ZigBee
• What is ZigBee?
• Why using ZigBee?– Low power
– Low cost
– Small is size
– Can be used in research easily
• Received Signal Strength Indication(RSSI)
– Trilateration
– Fingerprinting
Node 1
Node 2
Node 3
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Research Objective
• Design an Indoor Positioning system
– Low cost
– More Accurate
– Portable
– User Friendly
– Provide more positioning reliable solution.
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ZigBee
PDR
Accelerometer
Gyroscope
Step Detection
Step Size
Step Direction
RSSI
Integration
PDR Position
Distance
CorrectedPosition
Proposed System
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• Correction Algorithm
– Case 1
ZigBee
Node
PDR
6 m
Proposed System
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ZigBee
Node
PDR
6 m
– Case 2
Proposed System
• Correction Algorithm
– Case 3
ZigBee
Node
PDR
6 m
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ZigBee
Node
PDR
6 m
– Case 4
Proposed System
• Correction Algorithm
– Case 5
ZigBee
Node
PDR
6 m
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ZigBee
Node
PDR
6 m
– Case 6
Experiment
• The experiments were conducted in the 5th floor, at the Electrical Engineering building, at Royal Military Collage of Canada, Kingston, Ontario, Canada.
• Zigbee network consisting of ten Zigbee senders were placed at known locations.
Lay out of the Building indication the path the pedestrian during the experiment and the position of
the Zigbee senders
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Installing Zigbee Nodes
Experiment
• The proposed method was implemented inside the android application in the form of three modules:
– PDR (Pedestrian Dead reckoning) module• To obtain pedestrian navigation using inertial sensors.
– Communication module• To establish a connection between ZigBee nodes and android
tablet.
– The correction module • To correct the measurements of PDR solution using ZigBee
modules.
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Results
• PDR with Correction algorithm has an rmserror less than two meters compared to more than five meters in case of PDR only.
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Experiment
• The experiment was implemented using an Android tablet (Samsung), connected with ZigBee receiver.
• The tablet and the Zigbee receiver are being carried by a pedestrian mounted on the chest.
• The pedestrian was walking normally during the experiments to collect data at different locations.
The layout of the android application
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Results
PDR Error: 4.27 m
PDR/ZigBee Error: 1.86 mTrue Path
PDRPDR / ZigBee
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Results
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Conclusion
• In this work, we proposed an Integrated Indoor positioning system employing:– Pedestrian Dead reckoning module (PDR)
– ZigBee wireless module.
• The proposed integration was made as the position determined by the PDR only method has an exponential error, which leads to in accuracy especially in long distances.
• The experimental results showed that the proposed integration algorithm PDR and Zigbee can accurately determine the position with error distance less than two meters compared to more than five meters in case of PDR only.
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Future work
• Automatic Decision Making (Fuzzy logic, neural network)
• Testing the proposed method on several indoor environments having different applications.
• Sending Position of Zigbee modules using Bluetooth
• Integrating our algorithm with several wireless technologies as Wi-Fi, Bluetooth.
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