# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 1 Introduction  
Recently, the ability to localize persons and objects has become the driving force behind a new context of applications, ranging from environment monitoring to navigation, warehouse management, safety, and entertainment. Developments in telecommunications and electronics have enabled the spread of micro-sensors able to send data via radio through a wireless sensor network (WSN). A WSN consists of numerous small, low-energy, low-cost sensors with limited processing power that communicate in an ad hoc manner. Localization, or positioning, is the process of determining the physical coordinates of these sensor nodes and is essential for applications that require knowledge of the event's origin. Different localization methods have been proposed according to application requirements, most of them exploiting radio signals that can provide indirect information to estimate the sender position. Localization in outdoor environments often relies on the Global Positioning System (GPS). This technology is not the best option for sensor nodes that usually run on limited power provided by batteries. Also, the cost of GPS modules is quite high, and they significantly increase the weight and the dimensions of the sensors. Shadowing and multipath have an impact on this technology's accuracy. The shadowing effect is caused by the obstruction of the signal due to physical obstacles, moving or stationary, that the signal encounters in its path. Electromagnetic waves tend to be disturbed by objects that are the same size as the wavelength. Potentially any obstacle can be a source of disturbance in the 2.4 GHz band used by GPS. The multipath phenomenon [1], as shown in Figure 1, introduces both constructive and destructive interferences [2] since different signals may travel different paths to reach their destination. Nevertheless, the indoor reception from satellites is degraded due to signal attenuation caused by obstacles. Hence, localization in WSN adopts different solutions, especially indoors. As transmissions in a WSN happen via radio, these signals provide an indirect way to calculate the sensor position and then localize objects or persons. This work presents some of the techniques available, and it mainly focuses on the use of a received signal strength indicator (RSSI). This parameter can be used to estimate the distance between a transmitter and a receiver and does not require any additional modules or circuitry as it is inherently supported by most chips. Unlike more complex localization techniques, such as those relying on time-of-flight or angle-of-arrival measurements, RSSI-based localization can be implemented with low-cost, off-the-shelf hardware, such as simple wireless transceivers or low-power microcontrollers. These components are widely available and inexpensive, reducing the overall cost of the sensor network. Furthermore, because RSSI is a relative measure, it does not require sophisticated hardware or additional infrastructure, such as GPSs or specialized sensors. This makes it an ideal choice for developing non-complex sensor systems that are suitable for large-scale deployments in resource-constrained environments. By leveraging RSSI for localization, wireless sensor networks can be built with minimal hardware and maintenance requirements. The primary contribution of this work lies in the development and evaluation of a real testbed for RSSI-based localization within a WSN, which provides valuable insights into the practical application of basic localization techniques in real-world conditions. While this study does not introduce novel filtering algorithms or complex methodologies, it lays a crucial foundation by implementing a functional sensor network, conducting a series of controlled experiments, and comparing basic RSSI-based localization techniques in various scenarios. By constructing and testing a real-world testbed, this work offers practical benchmarks and empirical data, which are essential for understanding the limitations and performance of existing approaches.