Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments.

Area of Science:

• Computer Science
• Ubiquitous Computing
• Service-Oriented Architecture

Background:

• The Web of Things (WoT) integrates numerous devices as RESTful services, creating complex systems.
• Resource discovery and selection face challenges due to functional redundancy, dynamic availability, and mobile object locations.
• Quality of Resource (QoR) is crucial for selecting appropriate services among many similar options.

Purpose of the Study:

• To develop a location-aware resource discovery mechanism for the Web of Things.
• To propose a Quality of Resource (QoR)-driven approach for selecting and composing web resources.
• To address challenges in resource discovery and selection caused by resource abundance, transient availability, and mobility.

Main Methods:

• A 3-dimensional indexing schema is proposed for location-aware resource identification.
• A Selection Strategy Adaptor (SSA) is introduced for QoR-driven resource composition.
• The SSA considers QoR constraints, I/O matching, resource availability, and user needs for optimal and optimistic compositions.

Main Results:

• The proposed location-aware discovery method enhances resource identification.
• The QoR-driven selection approach effectively forms resource compositions with alternatives.
• Evaluations demonstrate the service quality model's effectiveness against existing methods.
• Experimental results validate the solution's performance in diverse environments.

Conclusions:

• The developed system improves resource discovery and selection in the dynamic Web of Things environment.
• The location-aware and QoR-driven approach offers a robust solution for complex service composition.
• The SSA provides flexibility in forming resource compositions based on various criteria.