# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 1 Introduction  
The precise location of objects in a defined environment is essential for many different activities such as personalized mobile user applications, vehicles navigation, surveillance, or traffic organization. The development of novel cutting-edge technologies is requiring higher localization accuracy and stability for guaranteeing the successful operation of these modern devices. Traditionally, the localization services have been provided by Global Navigation Satellite Systems (GNSS) which are based on a constellation of satellites in space covering the entire Earth with a reduced number of satellites due to the launching and maintenance costs of each of the satellites of the constellation. Therefore, in some regions, the coverage of these systems is not stable, or the accuracy reached by the system is ineffective for high-demanded applications such as underwater localization [1], autonomous navigation [2], low-level flights [3], or indoor localization [4]. This is due to path signal degradation [5], synchronization effects on system clocks [6], multipath appearance [7], changing in thepropagation speed of the radioactive waves [8], or ionospheric scintillation [9]. Current research on GNSS systems (e.g., GPS) attempts to reduce these limitations through coordinating the satellite signals with cyber-physical systems located in the area of interest for reducing the system uncertainties [10]. Other approaches are considering the design of sensor networks for addressing directly the localization problem with independence of GNSS signals. As a consequence, Local Positioning Systems (LPS) have emerged as promising systems for addressing the limitations of the GNSS [11]. LPS consist on ad-hoc deployments of sensors which particularly adapt to the characteristics of the environment of application, improving the accuracy and stability of GNSS allowing the precise localization in complex conditions. LPS are classified depending on the physical property measured for determining the target location: Power [12], angle [13], time [14], phase [15], frequency [16], or even combinations of them, thus decreasing the percentage probability of error that each one have individually [17; 18]. Among LPS, those based on temporal measurements have the best trade-off among accuracy, stability, robustness, easy-to-implement hardware architectures and costs [19]. Time-Based Positioning architectures are divided considering three main categories depending on the time lapses computed to determine the target location: Time of Arrival (TOA) systems [20], Time Difference of Arrival (TDOA) systems [21], and Asynchronous Time Difference of Arrival (A-TDOA) [22]. TOA systems measure the total time of flight of the positioning signal from an emitter to an architecture receiver. This architecture forces to the synchronization among all the system devices to compute the time measurements. These measurements are then processed to obtain the equation of spheres to solve the target location needing at least 4 receivers to unequivocally determine the target in space. TDOA systems collect the time measurements resulting from the relative time lapse from the reception of the positioning signal in two different architecture receivers [23]. The measurement of the time without considering the target timestamp allows the unnecessary synchronization of the system devices with the target clock, thus reducing the uncertainties in the position determination generated in the synchronization process. These systems require 5 different receivers to unequivocally determine the target location through the intersection of the hyperboloid equations generated. However, we have demonstrated in [24] that under optimized node distributions, the ambiguity in the position determination through the non-linear intersection of the hyperboloids containing the potential target location, can be solved securely with four sensors.