Fog Computing-inspired Smart Home Framework for Predictive Veterinary Healthcare

Domestic Pet Care has been an important domain in the healthcare industry. In the presented study, a comprehensive framework of the Smart VetCare system for the health monitoring of domestic pets has been presented. The work is focused on the remote surveillance of domestic animals' health conditions inside the home environment using IoMT Technology. Specifically, pet health is analyzed for vulnerability in the ambient home environment and ubiquitous activities over a fog computing platform of FogBus. Furthermore, a temporal data granule is formulated and the Probability of Health Vulnerability (PoHV) is defined for determining the health severity of the animal. Additionally, the Temporal Sensitivity Measure (TSM) is defined for real-time pet healthcare analysis, which is visualized using the Self Organized Mapping (SOM) Technique. For validation purposes, the framework is deployed in the smart home environment using 12 IoMT WiSense Nodes and Health Sensor belt for monitoring a domestic dog of American Bully breed over the dynamic resource management platform of FogBus and iFogSim simulator. Based on the comparison with numerous state-of-the-art techniques, the proposed framework can register a better precision value (94.78%), accuracy value (95.38%), sensitivity value (93.71%), and f-measure value (94.41%).