Research On Location Algoirthms In Wireless Sensor Networks

In recent years, with the rapid development and wide application of wirelesssensor networks, wireless sensor network positioning technology is becoming one ofthe important technologies in wireless sensor networks, and with significant researchvalue. In wireless sensor network applications, such as target identification,monitoring and tracking, location information has become a necessary prerequisite forthese applications.The data which is collected by the nodes can makes sense onlywhen it is combined with location information in the measurement coordinate system.The nodes always with GPS transceiver, but taking the relationship between cost andenergy consumption into account, it is not applicable in a wireless ad hoc sensornetworks. So, obtain the accurate location information is very important in wireless adhoc sensor network self-positioning technology.Now，the most widely used localization algorithm are DV-Hop localizationalgorithm and RSSI location algorithm, this thesis is doing some research on thesealgorithm. First of all, for the DV-hop algorithm, proposed GDV-hop positioningalgorithm. Improvements have been made in the beacon node selection and rangingmethod. The simulation results show the new algorithm’s positioning accuracy isimproved and has a good adaptability and scalability.Secondly, for the RSSI algorithm, first starting from the main factors of affecting thewireless sensor network positioning accuracy， analysis of signal propagation model, selectthe appropriate signal attenuation model-logarithmic decrement model. And thenmade improvements in the selection of the beacon node. Select the beacon nodewhich is near from the unknown node. The greater the distance, the greater theimpact of positioning.For the average measured distance, use the triangle centroid algorithm toimprove the accuracy. Finally, simulate the improved algorithm. The results show thatthe accuracy of the improved algorithm is improved, and the improved algorithm hasgood adaptability and scalability than the original algorithm.