Statistical Analysis of Remote Health Monitoring Based IoT Security Models & Deployments From a Pragmatic Perspective

Abstract

Remote health monitoring-based Internet of Things (IoT) network security is a multidomain task, that involves identification of network attack, evaluation of mitigation strategies, design of performance aware data security models, integration of privacy models, and modelling of device-level security methods. Internal designs for each of these models is highly complex, and varies in terms of quantitative & qualitative performance measures. This is due to their variation in terms of design nuances, functional advantages, context-based limitations, and possible deployment-specific future scopes. Due to this variation, it is highly ambiguous to select these models for performance-specific IoT deployments. Moreover, these models also vary in terms of security level, Quality of Service (QoS) parameters, scalability performance, computational complexity, deployment costs, and other performance metrics. Thus, to identify optimum models, researchers & network designers are required to test & validate multiple security models for their deployments. Due to which, the cost & time to market for IoT devices is increased, thereby affecting viability of IoT products. To overcome these selection issues, an empirical survey of different IoT security models including blockchains, encryption techniques, hashing models, privacy preservation techniques, machine learning based security methods, etc. are discussed in this text. This text also discusses various attack mitigation models that provide node-level security, network-level security, physical security, & route-level security. This discussion will assist in initially evaluating different operating characteristics of these models, which will allow readers to identify most suited models for their application-specific use cases. This article also assesses the models' performance in terms of computational latency, energy consumption, security levels, deployment complexity, and scalability measures. These metrics are compared between different security models, which will further assist readers to identify optimum models for their performance-specific use cases. To further assist in model selection, this text proposes evaluation of a novel IoT Security Performance Rank (ISRP), that combines various performance metrics to form a singular rank which can be used to describe overall performance of these models. Readers will be able to consider optimal security approaches for new and current IoT installations based on this ranking.

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“…15 Normalize the parameters using Equation 23. 16 Assign weights W i to the parameters. 17 Compute the experience metric E using Equation 32.…”

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