Jul 2026· Annals of Telecommunications· 0 citations· 8 references
TL;DR
The design and validation of a physical testbed composed of low-cost, single-board computers for wireless multipath video transmission is presented and it is demonstrated that while simulators capture general trends, high-motion video significantly degrades latency, suggesting the occurrence of intermediate queue bottlenecks.
Abstract
Multipath routing is a key strategy for meeting the Quality of Service (QoS) requirements of video applications in the Internet of Things (IoT). However, the objective functions used in heuristic-based multipath selection mechanisms are often defined through mathematical models or network performance estimators and typically validated only in simulators, which may fail to capture the full complexity of real-world wireless environments. This paper presents the design and validation of a physical testbed composed of low-cost, single-board computers for wireless multipath video transmission. Beyond evaluating hardware resource usage (CPU, memory, and thermal load), we conduct a comprehensive cross-layer case study to assess the predictive reliability of the FITPATH and QSOpt routing heuristics. Transitioning from traditional QoS metrics to perception-based Quality of Experience (QoE), we evaluate the Structural Similarity Index (SSIM) across video sequences with varying degrees of motion and network load. Our findings reveal that while simulators capture general trends, high-motion video significantly degrades latency, suggesting the occurrence of intermediate queue bottlenecks. Furthermore, our video quality gap analysis demonstrates that while FITPATH provides highly accurate relative path rankings, both heuristics struggle to predict absolute performance drops caused by real-world interference. Ultimately, this work underscores the necessity of physical testbed validation for interference-aware objective functions in IoT multimedia networks.
Overall, routing efficiency depends on convergence behavior and traffic heterogeneity; Dijkstra suits latency-sensitive applications, whereas Bellman-Ford is more appropriate for adaptive routing scenarios, contributing to QoS-aware SD-IoT design.
Abdelali Grif, A. Zyane· International journal of inn...· 0 citations
The proposed SPN model enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment, and enables the identification of communication bottlenecks without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations
This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA) to ensure that the chain and reward functions align with the Quality of Service (QoS).
M. Dhanalakshmi, M. Karthiga· International Journal of Com...· 0 citations
The proposed QoS-Aware Routing (QAR) framework incorporates application-specific QoS requirements directly into the routing process, enabling route selection to better satisfy the communication demands of diverse FANET applications.
R. Mylsamy, M. Jaikumar· international journal of eng...· 0 citations
An Adaptive Hybrid Routing Framework that integrates RPL and GPSR under a machine learning (ML)-driven decision engine that offers a resilient and energy-efficient routing solution for next-generation smart grid neighborhood area networks is proposed.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations
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