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Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

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Emerging technologies rapidly change the essential qualities of modern societies in terms of smart environments. To utilize the surrounding environment data, tiny sensing devices and smart gateways are highly involved. It has been used to collect and analyze the real-time data remotely in all Industrial Internet of Things (IIoT). Since the IIoT environment gathers and transmits the data over insecure public networks, a promising solution known as authentication and key agreement (AKA) is preferred to prevent illegal access. In the medical industry, the Internet of Medical Things (IoM) has become an expert application system. It is used to gather and analyze the physiological parameters of patients. To practically examine the medical sensor-nodes, which are imbedded in the patient's body. It would in turn sense the patient medical information using smart portable devices. Since the patient information is so sensitive to reveal other than a medical professional, the security protection and privacy of medical data are becoming a challenging issue of the IoM. Thus, an anonymity-based user authentication protocol is preferred to resolve the privacy preservation issues in the IoM. In this paper, a Secure and Anonymous Biometric Based User Authentication Scheme (SAB-UAS) is proposed to ensure secure communication in healthcare applications. This paper also proves that an adversary cannot impersonate as a legitimate user to illegally access or revoke the smart handheld card. A formal analysis based on the random-oracle model and resource analysis is provided to show security and resource efficiencies in medical application systems. In addition, the proposed scheme takes a part of the performance analysis to show that it has high-security features to build smart healthcare application systems in the IoM. To this end, experimental analysis has been conducted for the analysis of network parameters using NS3 simulator. The collected results have shown superiority in terms of the packet delivery ratio, end-to-end delay, throughput rates, and routing overhead for the proposed SAB-UAS in comparison to other existing protocols.INDEX TERMS Authentication and key agreement, internet of medical things, security protection and privacy user authentication, random-oracle model and resource analysis, e-healthcare application, biometrics. X ↔ WG Ac Asgn 9 : WG Ac | ≡ Usr i ⇒ Usr i US K ←→ WG Ac Asgn 10 : Usr i | ≡ WG Ac ⇒ Usr i US K ←→ WG AcThirdly, the idealized form of the proposed SAB-UAS scheme is analyzed using BAN-logic rules and assumptions. The proofs of statements are as follows:According to M sg 1, the expression could be:

Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.

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The Internet of Things (IoT) paradigm has integrated the sensor network silos to the Internet and enabled the provision of value-added services across these networks. These smart devices are now becoming socially conscious by following the social Internet of Things (SIoT) model that empowers them to create and maintain social relationships among them. The Social Internet of Vehicle (SIoV) is one application of SIoT in the vehicular domain that has evolved the existing intelligent transport system (ITS) and vehicular ad-hoc networks (VANETs) to the next phase of Intelligent by adding socializing aspect and constant connectivity. SIoV generates a massive amount of real-time data enriched with context and social relationship information about vehicles, drivers, passengers, and the surrounding environment. Therefore, the role of privacy management becomes essential in SIoV, as data is collected and stored at different layers of its architecture. The challenge of privacy is aggravated because the dynamic nature of SIoV poses a major threat in its adoption. Motivated by the need to address these aspects, this paper identifies the challenges involved in managing privacy in SIoV. Furthermore, the paper analyzes the privacy issues and factors that are essential to be considered for preserving privacy in SIoV environments from different perspectives including the privacy of a person, behavior and action, communication, data and image, thoughts and feelings, location and space, and association. In addition, the paper discusses the blockchain-based solutions to preserve privacy for SIoV. INDEX TERMS Privacy management, blockchain, social Internet of Vehicles, Internet of Vehicles, social Internet of things, Internet of things.