Performance Study of Localization Techniques in Zigbee Wireless Sensor Networks Ray Holguin Electrical Engineering Major Dr. Hong Huang Advisor

Introduction Advances in communication technologies electronic components have allowed for efficient and inexpensive wireless sensor nodes. Increased interest in Wireless sensor networks, for commercial and military usage. Adaptive WSNs require efficient routing protocols to transmit data between nodes. Most routing protocols rely on the known location of sensor nodes to implement data routing.

Introduction

Routing requires an efficient localization solution Use of external localization devices results in increased resource usage Propose to study a form of localization using RSSI (received signal strength indication) Analyze the accuracy and precision of using RSSI

Methods Divided into three subsections  Devise an RSSI vs distance model  Localization experimentation  Statistical analysis

Zigbee RSSI RSSI is a measure of the amount of signal power received by one node from another RSSI is non-linear with respect to distance Modeled through the Friis transmission equation P TX =Power transmitted at receiver P RX =Power remaining at receiver G TX =Transmitter gain G RX =Receiver gain λ=Wavelength d=Distance between transmitter and receiver

RSSI vs Distance model In order to get an accurate model, we need actual RSSI data. Measured RSSI values between two zigbee nodes from 1 to 40 meters at 1 meter intervals Used linear regression to model select data intervals

RSSI vs Distance model

Localization Experiment RSSI measurements were taken at a central node with three and four surrounding nodes Location of surrounding nodes was known

Localization Experiment

Analysis To understand the accuracy and precision of the experimentation, we plot the mean value of the data set We also compute the confidence interval to show the precision of each experiment

Analysis – Three nodes

Analysis –Four nodes

Results Distance Errors in meters 3 nodes4 nodes

Conclusions Using RSSI measurements from 4 nodes:  Gives a more accurate average estimation.  Allows for smaller confidence intervals, meaning our estimations will have less deviation.

Future Improvements Increase the number of samples taken during each experiment Increase the number of nodes used Adjust the transmission power