The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve.

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

With the growth in food products’ usage, ensuring their quality and safety has become progressively difficult. Specifically, food traceability turns out to be a very critical task for retailers, sellers, consumers, surveillance authorities, and other stakeholders in the food supply chain system. There are requirements for food authenticity verification (correct declaration of cultivation, origin, and variety), quality checks (e.g., justification for higher prices), and preventing food products from fraudsters in the food industry. The ubiquitous and promising technology of blockchain ensures the traceability of food trade networks with high potential and handles the aforementioned issues. Blockchain makes the food industry more transparent at all levels by storing data immutably and enabling quick tracking across the stages of the food supply chain. Hence, commodities, stakeholders, and semi-finished food items can be recognized significantly faster. Motivated by these facts, in this paper, we present an in-depth survey of state-of-the-art approaches to the food industry’s security, food traceability, and food supply chain management. Further, we propose a blockchain-based secure and decentralized food industry architecture to alleviate security and privacy aspects and present a comprehensive solution taxonomy for a blockchain-based food industry. Then, a comparative analysis of existing approaches with respect to various parameters, i.e., scalability, latency, and food quality, is presented, which facilitates the end-user in selecting approaches based on the merits over other approaches. Finally, we provide insights into the open issues and research challenges with concluding remarks.

Mobile applications have rapidly grown over the past few decades to offer futuristic applications, such as autonomous vehicles, smart farming, and smart city. Such applications require ubiquitous, real-time, and secure communications to deliver services quickly. Toward this aim, sixth-generation (6G) wireless technology offers superior performance with high reliability, enhanced transmission rate, and low latency. However, managing the resources of the aforementioned applications is highly complex in the precarious network. An adversary can perform various network-related attacks (i.e., data injection or modification) to jeopardize the regular operation of the smart applications. Therefore, incorporating blockchain technology in the smart application can be a prominent solution to tackle security, reliability, and data-sharing privacy concerns. Motivated by the same, we presented a case study on public safety applications that utilizes the essential characteristics of artificial intelligence (AI), blockchain, and a 6G network to handle data integrity attacks on the crime data. The case study is assessed using various performance parameters by considering blockchain scalability, packet drop ratio, and training accuracy. Lastly, we explored different research challenges of adopting blockchain in the 6G wireless network.

The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.

Quantum computing (QC) and quantum machine learning (QML) are emerging technologies with the potential to revolutionize the way we approach complex problems in mathematics, physics, and other fields. The increasing availability of data and computing power has led to a rise in using Artificial Intelligence (AI) to solve real-time problems. In space science, employing AI-based approaches to address various challenges, including the potential risks posed by asteroids, is becoming increasingly necessary. Potentially Hazardous Asteroids (PHAs) can cause significant harm to humans and biodiversity through wind blasts, overpressure shock, thermal radiation, cratering, seismic shaking, ejecta deposition, and even tsunamis. Machine Learning (ML) algorithms have been employed to detect hazardous asteroids based on their parameters. Still, there are limitations to the current techniques, and the results have reached a saturation point. To address this issue, we propose a Quantum Machine Learning (QML)-based framework for asteroid hazard prediction, employing Variational Quantum Circuits (VQC) and PegasosQSVC algorithms. The framework aims to leverage the quantum properties of the data to improve the accuracy and precision of asteroid classification. Our study focuses on the impact of PHAs, and the proposed supervised QML-based method aims to detect whether an asteroid with specific parameters is hazardous or not. We compared several classification algorithms and found that the proposed QML-based framework employing VQC and PegasosQSVC outperformed the other methods, with an accuracy of 98.11% and an average F1-score of 92.69%.