Fog-inspired smart home environment for domestic animal healthcare

Domestic veterinary care is contemplated as one of the significant domains of the healthcare industry. Conspicuously, this research presents a Smart Home-based healthcare monitoring framework for domesticated animals in real-time. The research work employs the Internet of Things (IoT)-based data acquisition in the ambient environment of the home. Acquired IoT-data is pre-processed for feature extraction over the Fog-Cloud computing platform. Moreover, a temporal data granule is formulated using the Temporal Data mining technique, which is used to quantify healthcare vulnerability in terms of Scale of Health Adversity (SoHA) and Temporal Adversity Estimate (TAE). Based on this, a Multi-scaled Long Short Term Memory (M-LSTM) based vulnerability prediction is performed for preventive veterinary healthcare services. Moreover, a fogassisted real-time alert generation module is presented in the proposed framework to notify the concerned veterinary doctor in the case of a medical emergency. To validate the proposed framework, the experimental simulations are performed over challenging dataset comprising of nearly 34,120 instances. Results show that the presented framework is able to register enhanced performance in comparison to several state-of-the-art decision-making techniques in terms of Temporal Effectiveness, Classification Efficiency, Prediction Efficacy, and System Stability.