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Research On RSSI-based Node Localization Algorithm In Wireless Sensor Networks

Posted on:2019-01-17Degree:DoctorType:Dissertation
Country:ChinaCandidate:J Z DuFull Text:PDF
GTID:1318330566464491Subject:Computational Mathematics
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With the rapid development of wireless communication technology,sensor technology and MEMS technology,miniaturized,low-power and low-cost wireless sensor nodes have emerged.Wireless sensor networks consist of a large number of wireless sensor nodes.Wireless sensor networks have become a very hot network research direction and are used in many different types of areas,for example,exploration of marine resources,air pollution monitoring,typhoon disaster warning,looking for mineral resources and so on.In many application areas,the location of nodes is critical.Positioning sensor nodes has also become particularly important.Researchers have designed a large number of localization algorithms suitable for node positioning in wireless sensor networks,in order to determine the location information of sensor nodes in a timely and accurate manner,so as to better serve the applications that are closely related to location information.In this thesis,we focus on RSSI-based indoor localization algorithms that can be used for node localization in wireless sensor networks.In this study,we use the logarithmic shadow path loss model as the RSSI theoretical model,which is used to estimate distance values.In order to establish the RSSI model in the actual environment,we have set up an experimental positioning test system.The RSSI data collection experiment was conducted in an indoor hall.Based on a large number of RSSI test data,we have established an experimental RSSI model.To further confirm the accuracy of the experimental RSSI model,we use a ray tracking system to simulate the characteristics of RSSI data in indoor environment.In the four simulation test scenarios,we did a lot of RSSI data acquisition experiments.By observing the relationship between RSSI data and distance and the relationship between noise standard deviation and distance,we find that the simulation results are consistent with experimental test results.Based on the established RSSI model,we described in detail the principle of distance estimation.On the basis of distance value estimation,we further analyzed the source of the distance estimation error and deduced the calculation formula of the variance value of the distance error.Based on the distance estimation error variance resulting expression,we obtained some strategies to reduce the distance estimation error.Combined with the strategies of reducing the distance estimation error,we proposed four localization algorithms based on the Multilateration algorithm and the mean RSSI data: TLS,WLS,TSDP,and WSDP.We used simulation data and measured data to verify the accuracy and calculation time of the proposed algorithms.In the simulation verification,we use the constructed RSSI model to provide RSSI data to estimate the distance value,and further verify the TLS,WLS,TSDP and WSDP positioning algorithms proposed in this paper.Compared with the existing LLS,NLS,POCS and SDP algorithms,the positioning accuracy and time overhead of all algorithms are analyzed.In the measured data validation,we did a comparison of the accuracy of LLS,WLS,WSDP,NLS,POCS and SDP.The verification results of the measured data and that of the simulation data show consistency in the positioning accuracy.
Keywords/Search Tags:Wireless sensor networks, Received signal strength indication(RSSI), Indoor localization, Linear least square(LLS), Nonlinear least squares(NLS), Projection onto convex set(POCS), Semi-positive definite programming(SDP)
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