Mobility-Aided Wireless Sensor Network Localization via Semidefinite Programming
In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for eac...
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| Published in | IEEE transactions on wireless communications Vol. 12; no. 12; pp. 5966 - 5978 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.12.2013
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for each model, we first derive the maximum likelihood (ML) location estimator for the case of error-free velocity measurements. As the corresponding optimization problems are non-convex, we resort to semi-definite relaxation (SDR) techniques to find approximate solutions to each problem using semi-definite programming (SDP). We then extend our results to the cases where the velocity measurements are subject to measurement errors. Our simulation results show that exploiting the mobility information in the localization process can significantly improve the performance of the sensor localization. Moreover, mobility-aided localization has the potential to address some of typical positioning problems, such as sensitivity to the ranging measurement errors and the requirement on the number of the anchors needed to uniquely localize the sensor nodes. |
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| AbstractList | In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for each model, we first derive the maximum likelihood (ML) location estimator for the case of error-free velocity measurements. As the corresponding optimization problems are non-convex, we resort to semi-definite relaxation (SDR) techniques to find approximate solutions to each problem using semi-definite programming (SDP). We then extend our results to the cases where the velocity measurements are subject to measurement errors. Our simulation results show that exploiting the mobility information in the localization process can significantly improve the performance of the sensor localization. Moreover, mobility-aided localization has the potential to address some of typical positioning problems, such as sensitivity to the ranging measurement errors and the requirement on the number of the anchors needed to uniquely localize the sensor nodes. |
| Author | Salari, Soheil Shahbazpanahi, Shahram Ozdemir, Kemal |
| Author_xml | – sequence: 1 givenname: Soheil surname: Salari fullname: Salari, Soheil email: soheil.salari@uoit.ca organization: Fac. of Eng. & Appl. Sci., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada – sequence: 2 givenname: Shahram surname: Shahbazpanahi fullname: Shahbazpanahi, Shahram email: shahram.shahbazpanahi@uoit.ca organization: Fac. of Eng. & Appl. Sci., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada – sequence: 3 givenname: Kemal surname: Ozdemir fullname: Ozdemir, Kemal email: kemal@thecableshoppe.com organization: Cable Shoppe Inc., Toronto, ON, Canada |
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| Keywords | Performance evaluation Positioning Wireless telecommunication Speed measurement Convex programming Optimization Remote sensing Relaxation Localization Signal detection Measurement error Mobile radiocommunication Range finding Motion estimation mobile localization semi-definite relaxation Measurement sensor mobile sensor networks semi-definite programming Sensor localization Arrival time Simulation Signal processing Wireless network Maximum likelihood Sensor array Approximate solution Software radio |
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| SubjectTerms | Applied sciences convex programming Detection, estimation, filtering, equalization, prediction Distance measurement Equipments and installations Exact sciences and technology Information, signal and communications theory Kalman filters mobile localization Mobile radiocommunication systems mobile sensor networks Optimization Radiocommunication specific techniques Radiocommunications Robot sensing systems semi-definite programming semi-definite relaxation Sensor localization Services and terminals of telecommunications Signal and communications theory Signal, noise Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Vectors Velocity measurement Wireless sensor networks |
| Title | Mobility-Aided Wireless Sensor Network Localization via Semidefinite Programming |
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