The rapid growth of web and mobile technologies has allowed people to access multimedia content from a wide range of heterogeneous client devices that have different characteristics and capabilities. In order to deliver the best presentation of content requested, the web system must possess a mechanism that is able to accurately discover the characteristics and capabilities of a client’s device. Existing content negotiation techniques mainly focus on static profiling approach without considering the combination of static and dynamic approaches that are capable of overcoming the device scalability issues. In view of these issues, we propose a hybrid approach for recognizing devices and their capabilities using token-based method. By proposing such solution, we can provide flexible, extensible, and scalable method that provides more accurate information in resolving the content negotiation issues. To validate the effectiveness of the proposed method, we construct a laboratory and field studies to investigate its performance and accuracy. The experimental results show that the proposed hybrid approach has better performances in several aspects compared to the static profiling. 1. Introduction The concept of universal multimedia access (UMA) has created a remarkable need to access multimedia content not only from personal computers (PCs) but also from mobile devices. Due to the growing number of new mobile devices, providing content in a usable format for UMA is challenging and still difficult to accomplish. Moreover, client access through mobile device has several limitations in terms of bandwidth, battery capacity, screen resolution, processing power, capabilities, and communication costs. In order to make content accessible on both PCs and mobile devices in UMA, a flexible content negotiation strategy is required for providing different representations or contents of the same resource to requested client [1]. The web system should automatically detect the devices’ capabilities, and hardware and software constraints and provide a suitable content based on the client characteristics, capabilities, and preferences. There are several standards and mechanisms that have been proposed for this matter in content negotiation such as hypertext transfer protocol (HTTP) header mechanisms, resource description framework (RDF) profiles that consists of composite capabilities/preferences profile (CC/PP) [2], user agent profile (UAProf) [3], and WURFL (Wireless Universal Resource FiLe) [4]. However, these standards and mechanisms offer limited possibilities in device
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