# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 1 Introduction  
The convergence of Micro-Electro-Mechanical Systems (MEMS) and the Internet of Things (IoT) has contributed to the development of tiny networked devices capable of detecting, monitoring, processing, and transmitting physical phenomena such as pressure and temperature [1; 2]. RFID, Zigbee, and 5G are among the communication technologies used by these devices [3; 4; 5; 6; 7]. Each sensor node, which is associated with CPUs, power units, and transceivers, has the ability to transmit data to a central base station (BS) for analysis and interpretation [8]. These devices play an important role in WSNs, and are used in such diverse fields as military operations, target tracking, and environmental monitoring. Localization is a critical process in IoT applications such as smart cities, healthcare monitoring, traffic management, disaster alerts, and geographic routing [9; 10; 11; 12; 13; 14; 15]. Accurately determining a device's location allows for in-depth analysis of events reported by sensor nodes. The interpretation and meaningful understanding of detected events are significantly limited because of the lack of accurate localization. Figure 1 depicts the importance and necessity of localization in a variety of application fields. As illustrated by Figure 1, precisely determining a device's location is a key challenge for IoT applications. GPS [16] is a widely recommended technology for accurately determining device locations in various application domains. However, due to the high cost of extra hardware, integrating auto-positioning technologies such as GPS into sensor devices may not consistently be the most cost-effective strategy. This has the potential to decrease the lifetime of the network. To address this concern, several localization techniques have been formulatedrevolving around the deployment of a limited set of GPS-equipped sensors referred to as anchor nodes. These anchor nodes have known positions and are used to approximate the unknown nodes' positions within wireless sensor networks. They aid in determining the location of randomly deployed nodes throughout the network using a localization technique, offering an alternative approach to achieve localization in IoT applications without the need for alternative devices to be associated with GPS receivers or auto-positioning capabilities. This cost-effective approach enables accurate device localization, thereby enhancing the comprehensive assessment and comprehension of real-time events. However, as depicted in Figure 2, obtaining precise sensor node locations remains a significant challenge. Over the last few years, several localization approaches have been presented; these can be grouped into two main technologies, range-based and range-free [17]. Range-based categories include technologies such as Time of Arrival (ToA), Received Signal Strength Intensity (RSSI), Time Difference of Arrival (TDoA), and Angle of Arrival (AoA) [18; 19; 20; 21]. These approaches apply either triangulation or trilateration techniques to determine the sensors' location by applying angles or distance information between synchronized sensor nodes. Range-free approaches operate within critical scenarios; they need extra hardware, resulting in increased overhead, particularly in large-scale WSNs, as discussed in [22]. Alter  
Figure 1: Relevance of localization in various domains. Figure 2: Example showing the process of localization. natively, range-free techniques drive the sensor node positions from anchor node positions without requiring extra hardware. Range-free methods consider only information about the interconnection between anchors and unknown nodes, making them a more feasible alternative. These approaches use the exact location of nearby anchors to enable unknown nodes to calculate their locations within WSNs. Examples of range-free approaches include the DV-Hop algorithm, APIT technique, Centroid algorithm, and Amorphous [23; 24; 25; 26]. APIT [24] calculates the locations of sensor nodes using the positions of a minimum of three anchor nodes.