Development of a sleep monitoring system using cloud computing and big data techniques

Among the most significant sleep concerns is obstructive apnea syndrome in sleep (OASS) which harms the quality of life. A few effects of OASS include cognitive decline, impaired psychiatric function, behavior problems, and personality ailments. Real-time monitoring of this condition is thus essential for clinical services. There seem to be numerous OASS-detecting techniques. Hence, the present experiment provides a new system both for detecting and assisting therapies of OASS in elderly individuals. This design was developed for tracking a variety of variables including the surroundings where a person goes to sleep, their sleeping quality, physical activity levels, and physiological variables by utilizing open data from smarter city systems. 2 sorts of processing are performed by the suggested method. In the instance of a crisis, real-time notices can be sent to those in charge of caring for the old aged owing to the rule-relied pre-processing technique. This method fundamentally relies on a fog computing strategy applied in a smarter device functioning at networking edges which also provides enhanced interoperable ability services, including technological, semantic, and syntactic capabilities. Big data devices on cloud computing are utilized in this process. The effectiveness of the system has also been assessed given latencies. The outcomes obtained indisputably show that pre-processing of data at networking edges increases system effectiveness.