## 2 Related Works
### Propagation Models Relevance  
In the context of localization techniques utilizing RSS, it is crucial to consider the precision and variability of the signal at both small and large scales of fading models. Failure to consider these factors can lead to misleading performance assessments on the relevance of any proposed localization solution based on using RSS. Many studies have highlighted the significance of accurate signal propagation models in wireless communication systems, considering phenomena such as multi-path fading and shadowing effects [22].  
The lognormal distribution has been traditionally used to represent large-scale shadowing effects [24], but it often appears inflexible due to its relatively inconvenient algebraic representation. In fact, it involves parameters and assumptions that may not always accurately capture the complexity of real-world scenarios, such as variations that can occur in the large-scale shadowing effects in different environments. Therefore, the conditions and assumptions of the lognormal distribution might not hold in practice, leading to a less flexible model, adaptable to changes in environmental conditions or other factors that can influence large-scale shadowing effects. Hence, the gamma distribution has been largely used as a substitute for lognormal distribution [25]. Nevertheless, in a realistic environment, they happen simultaneously; therefore, composite fading distribution is commonly required in wireless communication modeling. However, even if Nakagami-gamma (generalized K) and Nakagami-inverse Gaussian (NiG) approaches model simply the shadowed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Issue/Paper** & **Technique and Approach** \\ \hline [PERSON] et al., 2005 [9] & In order to obtain the global network layout, the authors proposed an anchor-free clustering-based approach to generate local maps that are then combined together. \\ \hline [PERSON] et al., 2008 [10] & The authors employed clustering but in a distributed manner, initially localizing clusters and then integrating them together to achieve network-wide localization. \\ \hline [PERSON] et al., 2010 [11] & The authors used a clustering technique with the triangle test method to form a local coordinate system. \\ \hline [PERSON] et al., 2015 [16] & The authors introduced an anchor-free localization approach, where the sensor would calculate the angle and distance to a single node as a global reference position. \\ \hline [PERSON] et al., 2016 [12] & The authors used a distributed cluster-based anchor-free node localization algorithm, where they employed a clustering approach for single-hop nodes and the ToA technique to estimate inter-node distances. \\ \hline [PERSON] et al., 2020 [21] & The authors proposed an anchor-free algorithm for one-hop nodes by computing the inter-node distances using RSS and the centroid techniques. \\ \hline [PERSON] et al., 2021 [17] & The authors applied the LDLA approach and introduced the SFO for implementing range-free anchor-free localization in WSNs using Dv-Hop. \\ \hline [PERSON] et al., 2023 [13] & The authors grouped nodes into one-hop clusters and estimated distances between nodes within each cluster using RSS. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anchor-free localization approaches.  
fading, neither of them endures the match over the whole range of fading and shadowing values [24]. Recent studies and analyses presented in the literature show that the composite Nakagami-lognormal (NL) distribution proposed in [26] to obtain the outage probability has been widely employed to model the mixture of small-scale fading and shadowing, and it fits in more measurement campaigns than the other distributions [27; 28].