Cloud computing has revolutionized the way organizations manage and deploy their Information Technology infrastructure. With an increasing emphasis on data security and residency, regulatory compliance, and the need for customization, private cloud platforms have emerged as a pivotal solution in the enterprise Information Technology landscape. This paper presents a comprehensive review of private cloud technologies, delineating their key features, advantages, and capabilities. Through an exhaustive research methodology, we explore various private cloud solutions, ranging from open-source offerings to proprietary systems. A reference architecture is formulated to provide a holistic understanding of the essential components and interactions inherent to a private cloud platform. Furthermore, 18 categories and 43 subcategories of features and capabilities for the 13 most popular private cloud solutions are identified to assist organizations in evaluating and selecting the most suitable platform based on their specific requirements. This study aims to offer valuable insights to enterprises navigating their cloud adoption journey, emphasizing the significance of making informed decisions in the rapidly evolving cloud computing domain.

This paper presents a detailed study focused on the utilization of energy-efficient wireless sensor networks (WSNs) specifically designed for hazard and crack detection in coal mines. The primary objective of this research is to develop a WSN system that operates on low power consumption, enabling real-time monitoring of hazardous conditions and cracks within coal mines. The proposed system incorporates energy-efficient methods and protocols to minimize power usage and prolong the lifespan of sensor nodes. The study encompasses the complete design and implementation of a prototype WSN system, followed by a thorough evaluation of its performance within a simulated environment. The obtained results demonstrate the effectiveness of the proposed system in detecting hazards and cracks in real-time while consuming minimal power. Consequently, this research underscores the potential of energy-efficient WSNs to enhance the safety and efficiency of coal mining operations. Moreover, the findings of this study have broader implications, as they can serve as a foundation for the development of similar systems applicable to other hazardous environments, including oil rigs, nuclear power plants, and forest fires. By adopting the energy-efficient WSN approach outlined in this research, these industries can benefit from improved safety measures and enhanced operational efficiency.

Smart factory represents one of the main Industrial Internet of Things applications that contribute in the industrial activities in the smart cities. This field acquires a special attention with the age of industry 4 from upgrading the passive machine into cyber physical system point of view. However, the process of developing the traditional factory into smart factory faces some issues regarding to handle smart and safe management such as handling the requirements of smart factory applications and the communication networks that should transfer the data among different parts. The current review paper highlights the up-to-date related works in the field of industry 4 in terms of industrial internet of things (IIoT) to filling the gap between the operational technologies and information technologies. Hence, the different architectures of IIoT are taken into consideration of research paper scope in terms of investigation and analysis. This work concentrates on the smart factory application and aims to connect between applications requirements and communication networks in the field of the smart factory in order to hold the optimum management to this aspect. Moreover, the expected cyberthreats and cyberattacks in the smart factory are captured in this work to explain the suitable countermeasures against such cyberattacks.

Wireless multicast networks, especially in the domains of IoT, 5G, and e-health, are experiencing a growing adoption of random linear network coding (RLNC). Nevertheless, the exponential growth of data can lead to congestion problems. This paper presents a review on novel technique called singular value decomposition (SVD) for the identification of network congestion. When SVD combines with statistical concepts like linear regression can to effectively handle large datasets and conduct comprehensive data analytics. The svd improves network performance and reliability by proactively identifying and mitigating congestion. Additionally, it improves the efficiency of data transmission and delivery in different application scenarios.

The single-path transmission protocols, cause throughput degradation for multimedia applications, considering the limited bandwidth and energy constraint of multimedia sensor nodes. Congestion on the network is a result of the issue of transferring a large amount of data. For multimedia data with low reliability, single path routing necessitates real-time Quality of Service (QoS) parameters and large bandwidth congestions that arise in intermediate nodes due to low memory. Consequently, leading to queuing delays and packet loss. Each chosen path must independently fulfill the corresponding QoS criteria while building the multiple paths for a multimedia application. Finding multiple paths with low path costs and high reliability while achieving application-specific QoS parameters is the aim of the effort.