Healthcare and Fitness Services: A Comprehensive Assessment of Blockchain, IoT, and Edge Computing in Smart Cities

Abstract

Edge computing, blockchain technology, and the Internet of Things have all been identified as key enablers of innovative city initiatives. A comprehensive examination of the research found that IoT, blockchain, and edge computing are now major factors in how efficiently smart cities provide healthcare. IoT has been determined to be the most used of the three technologies. In this observation, edge computing and blockchain technology are more applicable to the healthcare industry for assessing intelligent and secured data. Edge computing has been touted as an important technology for low-cost remote access, cutting latency, and boosting efficiency. Smart cities are incorporated with intelligent devices to enhance the person's day-to-day life. Intelligent of Medical Things (IoMT) and Edge computing (EC) are these things’ bases. The increasing Quality of Services (QoS) of healthcare services requires supercomputing that connects IoMT with intelligent devices with edge processing. The healthcare applications of smart cities need reduced latencies. Therefore, EC is necessary to reduce latency, energy, bandwidth, and scalability. This paper developed a deep Q reinforcement learning algorithm with evolutionary optimization and compared it with the traditional deep learning approaches for process congestion to reduce the time and latency related to patient health monitoring. The energy consumption, latency computation, and cost computation of the proposed model is less when compared to existing techniques. Among 100 tasks, nearly 95% of the tasks are offloaded efficiently in the minimum time.