De-Duplication of Big Data using Immutable Hashing Algorithms for Wearable Heat-Stroke Detection System

Abstract

As global warming progresses, the frequency and intensity of heat waves increase. Heat cramps, heat exhaustion, and heat stroke are just a few of the numerous problems caused by high temperatures. High temperatures frequently cause severe heat stroke without a breeze. Numerous studies have shown that age influences the risk of heat stroke at high temperatures. A wearable device with several biosensors might be used to monitor physiological changes. Although wearable devices are a simple and practical approach to collecting physiological data and providing feedback on the user's body and health statistics, there is no specific method for alerting users to situations that might lead to heat stroke. The temperature values are duplicated in the device after multiple times of heat detection. Continuously monitoring the temperature in the human body, the exponential growth of data is continuous, and the values are stored frequently. So, the storage is filled, and the repeated values are stored as duplicate values in the cloud storage. Therefore, we want to de-duplicate the values to optimize storage resources, enhance data quality, and improve processing efficiency. In this paper, we processed the de-duplication of big data using the proposed Immutable Hashing Algorithm. Where an immutable hashing algorithm is used, we use the Secure Hashing Algorithm (SHA1 with SHA1) method for a better de-duplication process. These methods quickly and securely de-duplicate the data from the Kaggle dataset. The result shows the high reliability and low latency values of de-duplication in the heat stroke prediction process.