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