## 2 Related Works  
### Anchor-Free Localization  
In the field of anchor-free localization within WSNs, several techniques and algorithms have been proposed. [PERSON] et al. [9] introduced a clustering-based approach, dividing the network into clusters, each with its gateway node responsible for generating a local relative map. These local maps are then combined to obtain the global network layout. However, it is worth noting that managing these clusters can introduce significant complexity into the localization process. [PERSON] et al. [10] also employed clustering but in a distributed manner, initially localizing clusters and then integrating them together to achieve network-wide localization. While cluster-based approaches offer advantages in certain scenarios, they frequently require intricate coordination and management, adding to the overall complexity of the localization system. The work in [11] took advantage of the sensor clustering approach to organize the network topology and the triangle test method to determine cluster heads. A local coordinate system was formed within each cluster, and a fusion algorithm was proposed to unify all clusters into a uniform coordinate system. In the work referenced as [12], [PERSON] et al. introduced a distributed cluster-based anchor-free node localization algorithm by employing a clustering approach for single-hop nodes and the time of arrival (ToA) technique to estimate inter-node distances. Then, a cluster synchronization was employed, where all nodes within a cluster are harmonized, and local coordinates are determined based on angle and distance information. The final step was the global localization phase. The most recent work in [13] presented an efficient anchor-free algorithm (EAFLA), which grouped nodes into one-hop clusters and estimated distances between nodes within each cluster using RSS. For nodes not within one hop, the Kleinrock model [14] and Al-Kashi's theorem (cosine law) were used to derive the positions of sensorsin the network. In contrast, our CRSSA streamlines the localization process by avoiding the complexities associated with clustering. Utilizing contextual connectivity information alongside the readily available RSS metric, the CRSSA aims to achieve precise localization without the overhead of managing multiple clusters or gateway nodes, thereby simplifying operations and reducing costs. The authors in [15] proposed a range-based anchor-free approach, where distances between all pairs of sensors within communication range were known, while distances beyond this range were considered unknown. Contrasting with this approach, the CRSSA operates on the principle that no distances are pre-known, even among proximate sensors. This challenges the traditional reliance on pre-established distances within communication range, as seen in [15]. An anchor-free localization approach was introduced in [16], utilizing a single node as a global reference position. This node sends a special active packet through the network, and when received by a sensor, the sensor would calculate the angle and distance to the node. However, this approach required all sensors to be equipped with angle-measuring devices. The authors in [6] proposed the ladder diffusion node localization algorithm (LDLLA) anchor-free localization algorithm, in which each sensor calculated its relative position with the sink node. Activation packets were broadcast by the sink node, and when received, a sensor would relay them using the ladder diffusion method. This allowed sensors to adjust their coordinate systems based on received packets from source nodes and neighbor node positions, but this method also shared the same limitation of requiring angle-measuring devices for effective localization. The work in [7] presented a method that achieved positioning through beacon data interaction between a rotating antenna on the base station and sensor nodes.