Entropy-aware ambient IoT analytics on humanized music information fusion

Abstract

Musical information fusion in the era of Internet is participatory multi-sensor based heterogeneous musical data recognition and computing. Participatory devices enhance the progression of intelligent multimedia data fusion and analytics in the participated edge computing devices in the context of ambient Internet of Things. Sensed data streams coming from multi-sensors encounter the conventional methodologies for data analytics and are further transmitted to emerging big data archetype. The proposed contribution analyses, validates and evaluates a set of qualitative music data collected from wearable sound sensors. The authors present system architecture with three committed layers of participated devices for music fusion in the Internet of Things environment. Besides, an analytical case study on music fusion challenges is discussed along with the elucidation of their unique features in terms of Big data V-Scheme, followed by the demonstration of edge-cloud computing paradigm with deliberate evaluations. In this work, the system requirements in terms of data transmission latency and relevant power dissipation are visualized. The information and proposed system entropy of stochastic source of music data are evaluated in order to measure system efficiency and stability for performing multimedia communication. Quantitative evaluations are studied for comparison of heterogeneous system architectures in terms of system entropy that illustrate significant improvement in music fusion efficiency upon employing the proposed system archetype.