LAUSR

The existing Internet of Things (IoT) architectures are employed for different real-time smart city applications adaptively. The architecture's adaptability to the application depends on the robust quality of experience (QoE) granted to the end-users. This article introduces an Accessible Application-Centric Framework (AACF) for application-specific IoT architectures by considering the robust requirements of end-user applications. The framework aims to provide flexible application-service disseminations for end-user requests. The state of the application and its longevity are taken into account for satisfying the in-time need of the requests. The service requiring scalable dissemination is performed based on state learning decisions to improve the success rate and enduring responses. In this learning process, the balance between response and service dissemination is retained to pursue robust user-specific performance. The proposed AACF is assessed using the metrics response ratio, response delay, and backlogs.