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Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be utilized for maximizing the achieved accuracy. This problem, defined as the Sensor Selection Problem (SSP), has also been categorized as NP-Hard. While different metaheuristics have been proposed for attaining a near optimal solution in both problems, no approach has considered the two problems simultaneously, thus resulting in suboptimal solutions since the SSP is biased by the actual node distribution once deployed. In this paper, a combined approach of both problems simultaneously is proposed, thus considering the SSP within the NLP. Furthermore, a novel metaheuristic combining the Black Widow Optimization (BWO) algorithm and the Variable Neighbourhood Descent Chains (VND-Chains) local search, denominated as BWO-VND-Chains, is particularly devised for the first time in the author's best knowledge for the NLP, resulting in a more efficient and robust optimization technique. Finally, a comparison of different metaheuristic algorithms is proposed over an actual urban scenario, considering different sensor selection criteria in order to attain the best methodology and selection technique. Results show that the newly devised algorithm with the SSP criteria optimization achieves mean localization uncertainties up to 19.66 % lower than traditional methodologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]