A Secure and Decentralized Trust Management Scheme for Smart Health Systems

Secure Computing for Fog-Enabled Industrial IoT

Smart cities are powered by several new technologies to enhance connectivity between devices and develop a network of connected objects which can lead to many smart industrial applications. This network known as the Industrial Internet of Things (IIoT) consists of sensor nodes that have limited computing capacity and are sometimes not able to execute intricate industrial tasks within their stipulated time frame. For faster execution, these tasks are offloaded to nearby fog nodes. Internet access and the diverse nature of network types make IIoT nodes vulnerable and are under serious malicious attacks. Malicious attacks can cause anomalies in the IIoT network by overloading complex tasks, which can compromise the fog processing capabilities. This results in an increased delay of task computation for trustworthy nodes. To improve the task execution capability of the fog computing node, it is important to avoid complex offloaded tasks due to malicious attacks. However, even after avoiding the malicious tasks, if the offloaded tasks are too complex for the fog node to execute, then the fog nodes may struggle to process all legitimate tasks within their stipulated time frame. To address these challenges, the Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT) is proposed for fog nodes. EEOIT proposes a mechanism to detect malicious nodes as well as manage the allocation of computing resources so that IIoT tasks can be completed in the specified time frame. Simulation results demonstrate that EEOIT outperforms other techniques in the literature in an IIoT setting with different task densities. Another significant feature of the proposed EEOIT technique is that it enhances the computation of trustable tasks in the network. The results show that EEOIT entertains more legitimate nodes in executing their offloaded tasks with more executed data, with reduced time and with increased mean trust values as compared to other schemes.

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

This work addresses the functional approach to ensuring cyber resiliency as a kind of adaptive security management. For this purpose, we propose a learning automaton model capable of self-learning and adapting to changes while interacting with the external environment. Each node in the under-controlled system has a set of probable actions with respect to neighboring nodes. The same actions are represented in the graph of the learning automaton, but the probabilities of actions in the graph model are permanently updated based on the received reinforcement signals. Due to the adaptive reconfiguration of the nodes, the system is able to counteract the cyberattacks, preserving resiliency. The experimental study results for the emulated wireless sensor network (WSN) are presented and discussed. The packets loss rate stays below 20% when the number of malicious nodes is 20% of the total number of nodes, while the common system loses more than 70% of packets. The network uptime with the proposed solution is 30% longer; the legitimate nodes detect malicious nodes and rebuild their interaction with them, thereby saving their energy. The proposed mechanism allows ensuring the security and functional sustainability of the protected system regardless of its complexity and mission.

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.