STAMQRW: DESIGN OF AN EFFICIENT SPATIAL AND TEMPORAL ANALYSIS MODEL FOR QOS-AWARE ROUTING IN HIGH-PERFORMANCE WIRELESS DEPLOYMENTS

Abstract

The demand for high-performance wireless networks is rapidly increasing due to the proliferation of mobile devices and the growth of data-intensive applications. Quality of Service (QoS) is a crucial factor in ensuring the reliability and efficiency of such networks, and QoS-aware routing is an essential component of achieving this goal. However, the complex nature of wireless deployments, combined with the dynamic and unpredictable nature of the wireless medium, poses significant challenges to the design of effective QoS-aware routing algorithms. In this paper, we propose a novel spatial and temporal analysis model for QoS-aware routing in high-performance wireless deployments. Our model integrates multiple individual models to capture the spatial and temporal characteristics of the wireless environment, including interference, fading, and mobility. We use a combination of statistical and machine learning techniques to derive accurate and efficient predictions of network performance, including packet loss, delay, and throughput levels. We evaluate the effectiveness of our proposed model using extensive simulations and experiments in an augmented set of real-world wireless network testbeds. Our results demonstrate that our model outperforms existing QoS-aware routing algorithms in terms of both network performance and computational efficiency. Specifically, our model achieves a significant improvement in QoS metrics, including packet loss, delay, and throughput, while reducing the computational overhead of routing decision-making process. Our proposed spatial and temporal analysis model for QoS-aware routing has significant implications for the design of high-performance wireless networks, particularly in complex and dynamic environments. Our model can provide network operators with valuable insights into the performance of their networks and enable them to make informed decisions about network optimization and resource allocations.