An Adaptive Approach to Multimedia Adaptation in Context-Aware Pervasive Systems - Ayadi, Abdelghafar; Saighi, Asma; Laboudi, Zakaria (Engineering, Technology & Applied Science Research, 2025)
In pervasive systems, contextual constraints can hinder the smooth execution of multimedia documents. Several adaptation approaches have been proposed that operate as client-side, server-side, proxy-based or peer-to-peer applications. Most of these approaches rely on centralized mechanisms that adapt content at a single point. While centralized mechanisms offer several advantages, they also introduce significant drawbacks related to resource overhead. In this work, we propose a novel hybrid approach for multimedia document adaptation that efficiently combines multiple adaptation categories, such as client-side, server-side, proxy-based, and peer-to-peer, to alleviate some of their limitations. The main objective is to improve the quality of service in adaptation processes. The proposed process can operate on different devices, enabling self-reconfiguration by seamlessly switching from one adaptation category to another, depending on the computation context of the users, i.e., the device capabilities. To validate our proposal, we focus on three key aspects: first, real-world prototyping applied to the adaptation of multimedia content for e-learning materials; second, an assessment of performance in terms of response time, considering the number of adaptation requests and the available computing resources of each device; and third, a comparison with related work. The results show that as the number of adaptation requests increases, our approach outperforms traditional methods that rely on a single location. Specifically, it achieves response time reductions of up to 53.3% over the client/proxy/server hybrid scheme, 66.6% over the client/proxy hybrid scheme, and 74.3% over the client-only scheme. These findings are highly satisfying and encouraging, demonstrating that distributed computation can effectively optimize resource consumption, reduce response time, and maintain high scalability and robustness.