Abstract Video conferencing and live streaming are being used in various industries, such as healthcare, gaming, telecommunication, manufacturing and others. As technology progresses, the need for real-time data transmission with minimal latency has increased. Web Real-Time Communication () addresses this need effectively. WebRTC is a technology designed to provide real-time communication through web and mobile browsers. Its low latency and P2P communication capabilities make it a convenient technology for secure, efficient communication in real-time applications. This paper reviews the key features of WebRTC, discusses its strengths and weaknesses and investigates a detailed analysis of 83 existing studies. Moreover, It evaluates all use cases that can be adopted by WebRTC by examining their descriptions, problem statements, and research gaps based on literature to date. Finally, It highlights the open research directions for the emerging technologies and enhancements of WebRTC. to identify their potential applications.

A scarcity of frequencies arises from the increased demand for the Industrial Internet of Things (IIoT) and networked systems in warehouse operations. This puts an additional burden on available bandwidth in cellular networks. This problem can be tackled by cognitive radio networks (CRNs), which use spectrum sensing to track and access unused frequencies, increasing spectrum efficiency. However CRNs have been extensively studied in several IoT projects, this study is the initial to examine the utilisation of CRN to intelligently manage the use of available radio frequencies. Two macro base stations are the central hubs of a network, communicating with IIoT devices in warehouse settings. This paper investigates the utilisation of CRN with machine-learning algorithms to intelligently manage the spectrum for connected IoT devices for intelligent Warehouse settings. A range of machine learning methods, including Support Vector Machine (SVM), k-nearest Neighbors (KNN), Decision Tree, Random Forest, and Naive Bayes, are provided to identify accessible bands for the best possible spectrum allocation and to recognize key users. Based on criteria like accuracy, precision, recall, and score for all ML techniques, the system’s performance is assessed. The numerical results demonstrate a noteworthy 20% reduction in false positives and a substantial improvement in cooperative spectrum sensing accuracy, which in turn improves the effectiveness of IIoT operations in warehouse environments.

Radio Access Network (RAN) deployments are evolving quickly owing to the innovative approaches of the Open Radio Access Network (O-RAN) Alliance. Specifically, they are moving away from closed, customized hardware implementations and toward virtualized instances operating on shared platforms. Future successful and affordable RAN deployments are made possible by this paradigm change, which is characterised by the separation of radio software components from hardware. Real-time network parameter configuration, sufficient computing resources for virtualized RAN (vRAN) deployment, and dependable processing unit sharing among numerous vRAN instances are some of the obstacles still standing in the way of successful O-RAN network implementations. Thus, this paper explored and compared the effectiveness of diverse optimization algorithms for minimising the number of resource blocks (nRBS), including machine learning (RandomForestRegressor), heuristic, and mathematical methods. Moreover, it investigates the lessons learned and the limitations of the proposed system. It demonstrates the practical success of a heuristic approach in O-RAN optimization, achieving significant reductions in resource blocks based on the Throughput-to-Bandwidth Ratio. It also provided insights into challenges with the RandomForestRegressor model, highlighted the importance of considering real-world network dynamics, and offered valuable lessons for future research, emphasizing the need for adaptive solutions and exploring hybrid optimization approaches, ultimately contributing to an enhanced understanding of O-RAN optimization.

With the rise of requirements for high-speed and low-latency connectivity, innovative approaches such as network slicing, Quality of Service (QoS) Provisioning, and reinforcement learning-based resource allocation, including the use of resource blocks (RBs) including radio resources, have to keep pace with these evolving requirements. By utilising network intelligence through machine learning and deep reinforcement learning, there is a potential to enhance QoS provisioning, expand the current network capacity, reduce congestion and latency, improve energy efficiency, and thus support new business models and revenue sources. The progress so far suggests that while considering RBs, network-slicing datasets, intertwined with QoS provisioning, have not been explored comprehensively, and packet drop probability or rate has not been taken into account in resource allocation. This paper proposes a network slicing method that uses seven machine learning algorithms and demonstrates its efficiency and accuracy compared to benchmarks in the literature with respect to QoS. Moreover, a priority algorithm is developed to ensure that packets with a high chance of being dropped (affecting QoS) are queued first. A resource allocation algorithm considering QoS provisioning and RBs is utilised to improve network performance based on a mathematical derivation of packet drop rate. Furthermore, a virtualisation of the processing between Cloud and Edge depends on the network slice. By intelligently distributing tasks between Cloud and Edge resources using deep reinforcement learning and genetic algorithms, an offloading script ensures uninterrupted service availability even when all network resources (i.e., RBs) are in use, thus maintaining the desired QoS.

With the growing popularity of the Internet of Things (IoT), billions of devices are anticipated to be deployed in various industries without establishing trust between them. In environments without pre-established trust, reputation systems provide an effective method of assessing the trustworthiness of IoT devices. There has been considerable literature on deploying reputation systems in industries that have not yet established trust among themselves. Therefore, the article reviews published studies on reputation systems for IoT applications to date, focusing on decentralised systems and decentralised systems using blockchain technology. These studies are evaluated regarding security (including integrity and privacy) and non-security requirements to highlight open research challenges. In alignment with this, an analysis and summary of the existing review studies on reputation systems for particular IoT applications are presented, demonstrating the need for a review article to consider all IoT applications and those that have not been explored. The IoT applications and sub-applications are described, and their problem statement, literature to date and research gap are comprehensively evaluated. Finally, the open research challenges concerning reputation systems are reviewed and addressed to provide the researcher with a road map of potential research directions.

Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges, and implementation strategies to harness its transformative potential effectively. This survey paper investigates the impact of Maritime Blockchain on Supply Chain Management, shedding light on its ability to enhance transparency, traceability, and overall efficiency in the complex realm of maritime logistics. Furthermore, the paper offers a practical roadmap for the integration of blockchain technology into the Maritime Industry, presenting a comprehensive framework that maritime stakeholders can adopt to unlock the advantages of blockchain in their operations. In addition to these aspects, the study conducts a thorough examination of the current network infrastructure in Ports and Vessels. This assessment provides a holistic view of the technological landscape within the maritime sector, which is crucial for understanding the challenges and opportunities for the successful implementation of blockchain technology. Moreover, the research identifies and analyzes specific Blockchain cybersecurity challenges that are pertinent to the Maritime Industry.