TY - JOUR
T1 - STRONG
T2 - Synchronous and asynchronous robust network localization, under non-Gaussian noise
AU - Soares, Cláudia
AU - Gomes, João
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-AUT%2F31411%2F2017/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT#
The authors would like to thank Prof. Pinar O?uz-Ekim and Dr. Andrea Simonetto for providing the MATLAB implementations of their published algorithms. Also, we thank Prof. Jo?o Xavier for insightful discussions during this research.
PY - 2021/8
Y1 - 2021/8
N2 - Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data — data classified as outliers. Our work addresses these concerns in the scope of the sensor network localization problem where, despite the abundance of technical literature, prior research seldom considered outlier data. We propose robust, fast, and distributed network localization algorithms, resilient to high-power noise, but also precise under regular Gaussian noise. We use a Huber M-estimator, thus obtaining a robust (but nonconvex) optimization problem. We convexify and change the problem representation, to allow for distributed robust localization algorithms: a synchronous distributed method that has optimal convergence rate and an asynchronous one with proven convergence guarantees. A major highlight of our contribution lies on the fact that we pay no price for provable distributed computation neither in accuracy, nor in communication cost or convergence speed. Simulations showcase the superior performance of our algorithms, both in the presence of outliers and under regular Gaussian noise: our method exceeds the accuracy of alternative approaches, distributed and centralized, even under heavy additive and multiplicative outlier noise.
AB - Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data — data classified as outliers. Our work addresses these concerns in the scope of the sensor network localization problem where, despite the abundance of technical literature, prior research seldom considered outlier data. We propose robust, fast, and distributed network localization algorithms, resilient to high-power noise, but also precise under regular Gaussian noise. We use a Huber M-estimator, thus obtaining a robust (but nonconvex) optimization problem. We convexify and change the problem representation, to allow for distributed robust localization algorithms: a synchronous distributed method that has optimal convergence rate and an asynchronous one with proven convergence guarantees. A major highlight of our contribution lies on the fact that we pay no price for provable distributed computation neither in accuracy, nor in communication cost or convergence speed. Simulations showcase the superior performance of our algorithms, both in the presence of outliers and under regular Gaussian noise: our method exceeds the accuracy of alternative approaches, distributed and centralized, even under heavy additive and multiplicative outlier noise.
KW - Convex relaxation
KW - Distributed iterative network localization
KW - Distributed localization algorithms
KW - Huber function
KW - Nonconvex optimization
KW - Robust estimation
KW - Sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85103086393&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2021.108066
DO - 10.1016/j.sigpro.2021.108066
M3 - Article
AN - SCOPUS:85103086393
SN - 0165-1684
VL - 185
JO - Signal Processing
JF - Signal Processing
M1 - 108066
ER -