# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 1 Introduction  
Recently, the quick evolution of embedded systems and radio waves has led to the advent of wireless sensor networks (WSNs), which have become a foremost research field in the last period. In general, a WSN is a collection of sensor nodes distributed over the area of interest to sense or monitor an event or set of events. A sensor node is an embedded device which consists of a transceiver, limited power resource, microcontroller, and array of sensors. Each sensor node can perform gathering and processing and communicate with nearby sensors. WSNs have been deployed widely in several applications, including industrial, military, environmental, home monitoring, and medical applications [1; 2; 3; 4]. The positioning field has become an interesting field recently and has been adopted in several applications [5; 6; 7]. Localizing stationary and mobile targets in the area of WSNs is an interesting research field and involves estimating a location of a target object based on the existence of stationary sensor nodes distributed over the area of interest. Several WSN-based localization systems have been developed recently with different localization methods, accuracy, and costs. For instance, several types of measurements can be considered as position estimation methods, such as time difference of arrival (TDOA) [8], received signal strength (RSS) [9], time of arrival (TOA) [10], and angle of arrival (AOA) [11]. RSS-based localization systems offer reasonable localization accuracy outdoors; however, in indoor environments, the localization error becomes high due to the obstacles and walls that may exist, which usually weaken or strengthen the radio waves, hence increasing the localization error [12]. Mainly, WSN-based localization systems can be categorized into two main categories: triangulation and fingerprinting. The former triangulates the location of the target node using the RSS values received from stationary sensor nodes, whereas thelatter is based on collecting RSS values from stationary sensor nodes in a database and then estimates the position of the target node based on the stored RSS values. On the other hand, the area of artificial intelligence (AI) represented by machine learning (ML) and deep learning (DL) techniques has been deployed widely in several WSN applications, including routing data, coverage problem, localization, and fault tolerance [13]. In this paper, we focus on the area of sensor node localization due to its significance in a wide range of applications, for instance, locating objects in a lab area, tracking patients in hospital, and localizing children in school. There are several existing WSN-based localization methods and systems available to localize objects in indoor environments [14; 15]; however, these systems are either high in cost, complicated, or inaccurate in complex environments. In addition, despite the available wide range of WSN-based localization using fingerprint systems, these systems are usually ineffective in real environments as most of the research studies focused on simulation experiments. Therefore, this paper discusses the research and development of an efficient localization system to accurately localize objects and items using the RSS technique and the investigation of several ML models. Hence, this project aims to:  
1. Research the recently developed WSN-based fingerprinting localization approaches. 2. Construct a real RSS fingerprint dataset to allow researchers and developers to implement a real and efficient localization system for WSN application in indoor environments. 3. Develop an efficient device-free localization system using the RSS approach and tailored ML algorithms. 4. Investigate the impact of reference point density on the performance of localization accuracy. 5. Validate the efficiency of the developed system using real experiments conducted in the IIRC labs.