Indoor Location System based on 戴毓廷 吳信賢 許碩仁

Outline  Introduction  Methodology  Simulation  Hardware equipment  Related work  Reference

Introduction  “ Sensing data without location is meaningless. ” [2]  Using Global Position System (GPS) for every sensor is expensive.  Local Position System (LPS): using Received signal strength (RSS) or time of arrival (TOA) to get location information

Introduction  Combining GPS and LPS can give a low cost location system.  In this project, we would like to implementation a fine indoor location estimation based on RSS.

Methodology  Reference Devices (RD) Measuring BD ’ s RSS value, and push RSSI value to coordinator. RD have clear information of their position.  Blindfolded devices (BD) Broadcast a packet so RD can get RSSI value. BD do not know where he is.

Methodology  Coordinator Collecting the RSSI values form every RD, and then push them to server.  Server Running a program that can get value from COM port, getting RSSI values and performing location algorithm.

Methodology Reference Router / Reference Reference Blindfol ded PAN coordinator PC Wireless link Physical wire

Methodology  Location algorithm: 1. RSSI and power relationship. 2. Power and distance relationship. 3. Distance and location relationship.

Methodology 1. RSSI and power relationship: By experimental measuring. 2. Power and distance relationship: [1]

Methodology  Here we choose [1]

Methodology 3. Distance and location relationship: Here we use a method called Geometry method. The Geometry Location Algorithm has some different types: Gravity of Area Apexes Method. Line of Position Method. Section of Area Method. Weighted Area Apexes Method.

Methodology Gravity of Area Apexes Method: Step1: Plotting Cycles. Center: The three positions of reference devices. Radius: The measured distance between RD and BD. Step2: Find the six intersection points. RD

Methodology Step3: Find the “ meaningful ” intersection points. Step4: So far, we have three “ meaningful ” intersection points. Calculate the gravity of the three points and the position of gravity is our location estimation result. BD

Simulation  Consider a right triangular area with three RD which locate at (0,0), (0,10), (5, ) and a single BD locates at some unknown position.  We random choose the position of BD, and add some random noise on the actual distances between the three RDs.

Simulation (1,1) Estimation result: (1.1127, ) (5,5) Estimation result: (4.8548,5.0718) Assume the measurement error of RSS is between -5% and +5%

Hardware equipment  UZ2400 RF chip, UBEC, Taiwan.  8051 platform.

Related work  Create a program to handle RSSI and location estimation on server.  A filter or some solution to deal with RSSI jumping when BD stay at the same position.  Channel access problem.

Related work  We plan to complete one dimension first, and than using the algorithm discussed above to challenge two dimension.

Reference [1] Neal Patwari, Alfred O. Hero, Matt Perkins, Neiyer S. Correal, Robert J. O ’ Dea, “ Relative Location Estimation in Wireless Sensor Networks ” IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 8, AUGUST 2003 [2]J. M. Rabaey, M. J. Ammer, J. L. da Silva, Jr., D. Patel, and S. Roundy, “ Picorodio supports ad hoc ultra-low power wireless networking, ” IEEE Comput., vol. 33, pp. 42 – 48, July 2000.

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