Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 7; p. 2458
• Main Authors: Verde, Paula, Díez-González, Javier, Ferrero-Guillén, Rubén, Martínez-Gutiérrez, Alberto, Perez, Hilde
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.04.2021; MDPI
• Subjects: Accuracy; Algorithms; Autonomous navigation; Chains; Clocks & watches; Computer architecture; Cramèr-Rao Bound; Genetic algorithms; Heuristic; Indoor environments; local positioning system; Localization; memetic algorithm chains; node location problem; Noise; Optimization; Satellites; Sensors; Site selection; time difference of arrival; variable neighborhood descent; Wireless sensor networks; Cramèr-Rao Bound; node location problem; variable neighborhood descent; memetic algorithm chains; time difference of arrival; local positioning system
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21072458

Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.