FinTech has proven its true potential in traditional financial offerings by delivering digital financial services to individuals worldwide. The pandemic has accelerated how people interact with financial services and has resulted in long-term changes to societies and economies. FinTech has expanded access to financial services and has made such changes possible. FinTech or Financial Technology refers to using new technologies for financial services. Artificial Intelligence, Blockchain, and cloud computing are a few technologies currently being applied to FinTech. In this paper, we consider FinTech, which partly uses blockchain technology. Blockchain technology plays a vital role in the financial sector as it ultimately lifts trust and the need for third-party verification by using consensus-based verification. This survey provides a comprehensive summary of the most relevant blockchain-based FinTech implementations and an overview of FinTech sectors and segments. For each segment, we provide a critique and a discussion on how each blockchain implementation contributes to solving the majority of problems faced by FinTech companies and researchers. This research aims to direct the future of financial solutions by providing an outline of the applications of blockchain technology and distributed ledger technology (DLT) for FinTech. We discuss various implementations, limitations, and challenges of blockchain-based FinTech applications. We conclude this work by exploring possible strengths, weaknesses, opportunities, and threats (SWOT) analysis and future research directions.

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Blockchain technology has been prominent recently due to its applications in cryptocurrency. Numerous decentralized blockchain applications have been possible due to blockchains' nature of distributed, secured, and peer-to-peer storage. One of its technical pillars is using public-key cryptography and hash functions, which promise a secure, pseudo-anonymous, distributed storage with non-repudiation. This security is believed to be difficult to break with classical computational powers. However, recent advances in quantum computing have raised the possibility of breaking these algorithms with quantum computers, thus, threatening the blockchains' security. Quantum-resistant blockchains are being proposed as alternatives to resolve this issue. Some propose to replace traditional cryptography with post-quantum cryptography-others base their approaches on quantum computer networks or quantum internets. Nonetheless, a new security infrastructure (e.g., access control/authentication) must be established before any of these could happen. This article provides a theoretical analysis of the quantum blockchain technologies that could be used for decentralized identity authentication. We put together a conceptual design for a quantum blockchain identity framework (QBIF) and give a review of the technical evidence. We investigate its essential components and feasibility, effectiveness, and limitations. Even though it currently has various limitations and challenges, we believe a decentralized perspective of quantum applications is noteworthy and likely.

Electronic visual display enabled by touchscreen technologies evolves as one of the universal multimedia output methods and a popular input intermediate with touch–interaction. As a result, we can always gain access of an intelligent machine by obtaining control of its display contents. Since remote screen sharing systems are also increasingly prevalent, we propose a cross-platform middleware infrastructure which supports remote monitoring and control functionalities based on remote streaming for networked intelligent devices such as smart phone, computer and smart watch, etc. and home appliances such as smart refrigerator, smart air-conditioner and smart TV, etc. We aim to connect all these devices with display screens, so as to make possible remote monitoring and controlling a certain device by whichever one (usually the nearest one) of display screens among the network. The system is a distributed network consisting of multiple modular nodes of server and client, and is compatible to prevalent operating systems such as Windows, macOS, Unix-like/Linux and Android, etc.

Internet of Things (IoT) devices frequently utilize wireless networks operating in the Industrial, Scientific, and Medical (ISM) Sub-1 GHz spectrum bands. Compared with higher frequency bands, the Sub-1 GHz band provides broader coverage and lower power consumption, which are desirable properties for low-cost IoT applications. However, low-power and low-cost IoT modules cause high variability in network performance. The varying influence from real-world environments additionally undermines wireless propagation and aggravates this variability. We explore these influences and provide a checklist of potential factors affecting wireless network performance in real-world environments. Using multiple low-cost IoT modules, we conduct multiple experiments in five real-world scenarios: indoor, street, open field, ground-to-drone (G2D), and drone-to-drone (D2D). Specifically, the tests are conducted inside a building, on a straight street with wooded sidewalks and aligned houses, on an open field golf course, and high up in the air between drones. To understand the difficulty of reproducibility in IoT deployments, we studied the effect of factors in four categories. This includes the effect of path (line of sight, distance, and obstruction), configuration (transmit power level), weather (precipitation, temperature, and humidity), and installation (IoT module mobility and position). We find that some of the factors in the path and weather categories have the most influence among all the factors, while the rest have moderate to low impacts. In the end, we provide a complete checklist of all the tested factors, which we believe would be constructive not only to academics but also to industrial practitioners working on wireless IoT systems.

Despite AI's significant growth, its "black box" nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, and financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However, designing appropriately fast and accurate XAI is still challenging, especially in numerical applications. Here, we propose a universal XAI model named Transparency Relying Upon Statistical Theory (TRUST), which is model-agnostic, high-performing, and suitable for numerical applications. Simply put, TRUST XAI models the statistical behavior of the AI's outputs in an AI-based system. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these variables and pick only the most influential ones on the AI's outputs and call them "representatives" of the classes. Then we use multi-modal Gaussian distributions to determine the likelihood of any new sample belonging to each class. We demonstrate the effectiveness of TRUST in a case study on cybersecurity of the industrial Internet of things (IIoT) using three different cybersecurity datasets. As IIoT is a prominent application that deals with numerical data. The results show that TRUST XAI provides explanations for new random samples with an average success rate of 98%. Compared with LIME, a popular XAI model, TRUST is shown to be superior in the context of performance, speed, and the method of explainability. In the end, we also show how TRUST is explained to the user.

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