Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

Improved capacity and fairness of massive machine type communications in millimetre wave 5G network

In the Fifth Generation (5G) wireless standard, the Internet of Things (IoT) will interconnect billions of Machine Type Communications (MTC) devices. Fixed and mobile wearable devices and sensors are expected to contribute to the majority of IoT traffic. MTC device mobility has been considered with three speeds, namely zero (fixed) and medium and high speeds of 30 and 100 kmph. Different values for device mobility are used to simulate the impact of device mobility on MTC traffic. This work demonstrates the gain of using distributed antennas on MTC traffic in terms of spectral efficiency and fairness among MTC devices, which affects the number of devices that can be successfully connected. The mutual use of Distributed Base Stations (DBS) with Remote Radio Units (RRU) and the adoption of the millimetre wave band, particularly in the 26 GHz range, have been considered the key enabling technologies for addressing MTC traffic growth. An algorithm has been set to schedule this type of traffic and to show whether MTC devices completed their traffic upload or failed to reach the margin. The gains of the new architecture have been demonstrated in terms of spectral efficiency, data throughput and the fairness index