## 2 Related Works
The experimental results on both synthetic and real-world datasets demonstrate the superiority of WEC-FCA, consistently achieving comparable or better clustering performance than state-of-the-art methods, all while addressing the fundamental shortcomings of traditional approaches. The rapid development of advanced communication technologies has positioned location information as a cornerstone for enabling context-aware and location-aware intelligent services. Among these, modern Intelligent Transportation Systems (ITS) present the most stringent demands for real-time, highly accurate, and privacy-preserving location data. To address these challenges, the authors in [38] introduced a novel Spatial-Temporal Federated Transfer Learning (ST-FTL) framework, which combines multi-sensor data fusion with advanced privacy-preserving techniques to enhance cooperative positioning in urban ITS. The framework employs a three-layer architecture that integrates Federated Learning (FL) with Transfer Learning (TL), enabling a faster convergence of the global model, reduced communication costs, and improved prediction accuracy, even in scenarios with limited local data, such as urban canyons. A density-based spatial-temporal clustering algorithm is developed to identify optimal source domains for transfer, ensuring similarity between regions and improving cross-region model performance. Additionally, a convolutional-gated unit is proposed to filter irrelevant features and generate meaningful new features, enhancing global model initialization and weight aggregation. Locally, a multi-sensor data fusion model combines GPS and inertial measurement unit data through an improved time-aware asymmetric attention mechanism, which dynamically adjusts the importance of each data source based on contextual relevance. Furthermore, a streamlined Siamese network structure enables lightweight data augmentation by leveraging historical data from individual vehicles, addressing the challenge of insufficient training data. Experimental evaluations using two public datasets demonstrate the superiority of the proposed ST-FTL framework over state-of-the-art methods, achieving enhanced positioning accuracy, faster convergence, and robust performance in urban ITS scenarios. The references [39; 40; 41; 42] cite works that are extremely pertinent to our suggested protocol. In [39], a PSO-based energy-efficient clustering protocol (PSO-ECC) is introduced with the purpose of improving network lifespan. This protocol utilizes the PSO algorithm to select CHs and relay SNs within the network. CHs are chosen based on an FF derived from the PSO algorithm, which takes into account the SN energy ratio (initial and RE), distance from SNs to CHs, and SN degree to determine the most suitable SN for the CH role. For data transmission to the sink node, PSO-EEC employs a fitness value based on the RE of CHs and distance to the sink node to select relay SNs for multi-hop data transfer. The simulation results demonstrate that the proposed protocol outperforms existing techniques in terms of performance parameters, including energy consumption, network lifetime, and throughput. To develop effective clusters, a linearly decreasing inertia weight PSO-based Clustering technique (LDIWPSOC) is presented in [40]. In LDIWPSOC, PSO initially employs a particle encoding mechanism to indicate the indices of SNs selected as CHs by representing particles using SN coordinates. Coordinates and velocity are generated at random during particle initialization. Subsequently, in order to find the global and personal optimum solutions, an FF is derived considering parameters such as the intra-cluster distance, the distance between CHs and the sink node, and the RE of selected CHs. Finally, in order to update the position and velocity of the particles, a linearly decreasing inertia weight has been provided, where personal and global best solutions are iteratively updated until the global best solution is achieved.