Blockchain interoperability is an innovative feature of blockchain technology that is rapidly gaining momentum in various fields. The mass adoption of enterprise blockchains has not yet been achieved because blockchain networks act as isolated islands that cannot connect or exchange assets and/or information. In addition, the invocation of smart contracts is restricted only to on-chain executions because of the lack of connectivity between the blockchains. This survey paper aims to conduct a comprehensive and thorough literature review regarding blockchain interoperability, with special highlight on blockchain Oracles being state-of-the-art. Oracles have shown potential as an emerging technology that has revolutionized the blockchain ecosystem by acting as agents that fetch external information into the blockchain ecosystem. A detailed comparative discussion of different blockchain interoperability techniques is presented, showing the strengths and weaknesses of each technique. Being overlooked in the literature, the shortcomings of these techniques in comparison to Oracles are identified, addressing how Oracles have succeeded in overcoming many of these limitations. In addition, the literature lacks a focus on the latest market solutions adopting blockchain Oracles, and only a few studies have considered them in detail. This gap has been addressed through an in-depth assessment of the latest market solutions adopting Oracles in the past few years. Finally, design issues trying to achieve the best practices of Oracles, future directions, and identified research gaps are highlighted.

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer.

Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (R_b), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety.

Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

The rapid increase in data volume over the past years has given rise to big data science. Distributed File Systems (DFS) have become widely employed to handle this vast amount of data, such as Google File System and Hadoop Distributed File System (HDFS). The primary objective of a DFS is to ensure data availability and system reliability in the event of failure. Data availability and system reliability are achieved by replicating files across multiple locations, which, however, results in the consumption of storage space and other resources. The significance of these files varies based on their frequency of use within the system. As a result, specific files are deemed less critical and do not warrant extensive replication, as they hold little importance in the overall system. This paper presents a novel approach called "Optimizing HDFS Replication Policies with Predictive Categorization" for storage efficiency. This approach aims to minimize storage consumption while ensuring data availability and system reliability. Experimental results using the Spotify Hit Predictor Dataset (1960-2019) showcase significant improvements in storage utilization and throughput. This approach not only enhances system performance but also positions organizations for substantial cost efficiencies in managing their data-intensive workloads.