###### Abstract  
Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%. positioning data for correctly positioning the target in any specific location [4]. Hence, it requires a highly significant and complex effort to analyze the accurate localization technique for WSNs [5]. Numerous non-GPS-based localization techniques have been exploited, which can be classified into two categories, namely range-free and range-based methods [6]. A range-based localization approach employs point-to-point distance evaluation or angle-based assessment amongst SNs [7]. In this, a location can be estimated via the trilateration of anchor nodes (ANs) (whose locations can be recognized) [8]. Range-free localization approaches do not need range data among ANs and target nodes (TNs) but rely on topological data [9]. The range-based method provides a higher accuracy as compared to range-free localization techniques; however, it is not very cost effective [10]. The range-based localization of SNs is achieved using two stages, namely ranging and position estimation stages. In the primary stage, all TNs estimate their distance from the ANs utilizing the intensity of the receiving signal or the signal transit time. Accurate distance evaluation is impossible due to noise [11]. In the secondary stage, the location of the SNs is detected by the data acquired from the ranging stage. This can be performed through both geometric methods and optimization methods. Previously, several challenging techniques have been employed to face the difficulty of WSN node localization (NL) [12]. Some bio-inspired methods for localization are presented that can also be analyzed in this paper, namely a bat optimization algorithm (BA), swarm-based algorithms, evolutionary algorithms, and others stimulated by animal social activity [13]. It is a new technique for emerging innovative computing algorithms that depend on natural development [14]. Various models have been exploited in the development of these techniques like mentioning the responses and behaviors of animals in the wild and making a paradigm to represent their behaviors. Alternatively, bio-inspired optimization approaches could be analyzed to obtain parameter settings for enhancing the performance of this developed approach [15]. This paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA system is to define the localization of unknown nodes (UNs) based on the ANs as a reference point.