Event-Aware Framework for Dynamic Services Discovery and Selection in the Context of Ambient Intelligence and Internet of Things

TLDR

The Internet of Things extends Ambient Intelligence by integrating numerous smart objects that enable on‑the‑fly composition of services, yet discovering and selecting services that meet user needs and quality‑of‑service requirements remains challenging due to dynamic and uncertain environments. This paper proposes a user‑centered, event‑aware framework that automatically monitors services to respond to events in ambient settings. The framework achieves this by dynamically discovering and selecting services to adapt to unforeseen changes and maintain continuous, high‑quality service delivery. Implementation and extensive testing in an Ambient‑Assisted Living scenario demonstrate the framework’s efficiency and feasibility at large scale.

Abstract

The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) (with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users' requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.