Location-based Systems (LBS) are popular for delivering customized information. However, some issues, such as place credibility, the efficiency of position calculations, and communication latency, pose challenges for indoor LBS. This work proposes the High-performance Perspective Platform (H3P) to help network managers understand network users’ information. The H3P provides indoor positioning services based on Wi-Fi 6 (IEEE 802.11ax) to stabilize service quality and ensure high computation efficiency for rapid service response. It utilizes Apache Kafka and Apache Zookeeper clusters on Kubernetes to handle large amounts of data. Wi-Fi usage data is transmitted to Kafka’s distributed real-time data streaming to enhance position credibility and the immediacy of position calculations. The data structure is also optimized to improve computation efficiency. Experimental results show that H3P improves data latency by up to 69% and data insertion latency by up to 73%. Additionally, H3P offers more stability and efficiency than Chang et al., 2012 in terms of data transmission, with an improvement of approximately 16.15%. This allows administrators to manage the network with user-friendly interfaces and a smooth user experience.

With pervasive of mobile computing and wireless communication, the vehicular ad‐hoc network has become a key technology in modern day's information exchange. Due to the fast growing number of vehicles and saturation of the transport infrastructure, it is inhabitable to come out with better solution considering the available resources. In this study, thorough traffic analysis has been carried out to suggest a better model. Further, an attempt has been made to propose an admission control algorithm based‐on the parameters evolve from the mathematical analysis. Moreover, the performance analysis has been shown to establish the efficacy of the algorithm.

Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.