# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 1 Introduction
A-TDOA systems make use of the receive and retransmit strategy of the positioning signal in the target sensor of the signals emitted in the worker sensors of the architecture to a coordinator sensor in which all the time measurements are collected. This methodology allows to get rid of the need to synchronize all the architecture clocks as in the TDOA architecture. This allows the minimum clock uncertainties of the three architectures [25] but increases the signal path used for the target location thus cumulating more noise uncertainties in the transmission. Consequently, no a priori suitable time-based architecture can be defined in LPS applications [26] and a deep analysis on the environment characteristics must be performed to objectively determine the most appropriate architecture for a particular scenario. In this paper, we analyze the deployment of the synchronous TDOA architecture in a complex urban scenario for their extended use in the literature [27; 28] and due to their trade-off among noise and clock uncertainties in LPS applications. TDOA architecture allows the independence from the emission timestamp of the positioning signal thus reducing the synchronization errors of the TOA architecture and with a reduction in the signal path noise cumulated during the transmission with regards to the A-TDOA architecture which is particularly relevant in complex urban scenarios. Besides, the consideration of all the architecture sensors as coordinator sensors in the TDOA synchronous architecture makes the system more versatile and less dependent on particular sensors of the architecture compromising the system availability in failure cases or partial bad connections with the coordinator sensor of the A-TDOA architecture [19; 29]. However, regardless the architecture selected, LPS require an optimization of the node distribution to achieve acceptable results. This problem is known as the Node Location Problem (NLP) [30] and has been considered as NP-Hard [31]. Even, the exclusive consideration of guaranteeing the target coverage by the architecture sensors in the whole coverage design area is already considered as NP-Hard [32]. Consequently, the NLP in localization LPS is addressing a double NP-Hard combinatorial problem [33]. Therefore, heuristic methods have been applied in the literature for solving this complex problem in the field of the Wireless Sensor Networks (WSN). Genetic algorithms (GA) [34; 35], the firefly algorithm [36], simulated annealing [37], the bacterial foraging algorithm [38], the grey wolf optimizer [39], the bat algorithm [40], tabu search [41], the elephant herding optimization [42], differential evolution [43], or memetic algorithms (MA) [44] have been applied for achieving practical results for the NLP. These methodologies require an optimization function for measuring the quality of every node distribution proved in the heuristic algorithms. In the localization field, the Cramer-Rao Bound (CRB) [45] has been widespread since it is a maximum likelihood estimator which provides the minimum achievable error for a particular sensor distribution regardless the positioning algorithm used for determining the target location. However, the CRB cannot be jointly derived in the entire target coverage area [46] which also promotes the utilization of heuristic techniques for solving the NLP. In our recent works, we have modeled the CRB through the introduction of the Line-of-Sight (LOS) [47] and Non-Line-of-Sight (NLOS) [48] noise uncertainties introduced in the communications channel during the positioning signal transmission and the clock uncertainties due to the initial-time offset of the synchronization process, the drift of the clocks that compute the time measurement and the resolution of the clock [25]. In this paper, we apply this combined uncertainty model for characterizing the sensor distribution uncertainties.