The inherent limitations of the network keep on going to be revealed with the continuous deployment of cellular networks. The next generation 6G is motivated by these drawbacks to properly integrate important rate‐hungry applications such as extended reality, wireless brain‐computer interactions, autonomous vehicles, and so on. Also, to support significant applications, 6G will handle large amounts of data transmission in smart cities with much lower latency. It combines many state‐of‐the‐art trends and technology to provide higher data rates for ultra‐reliable and low latency communications. By outlining the system requirements, potential trends, technologies, services, applications, and research progress, this article comprehensively conceptualized the 6G cellular system. Open research issues and current research groups in their field of research are summarized to provide readers with the technology road‐map and the potential challenges to consider in their 6G research.

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations instead of BBU reduces mid-haul bandwidth at the expense of power consumption and vice versa. As a result, the integration of NFV and SDR in V-RAN facilitates dynamic power consumption and processing whenever relaxation is needed. This paper studies several functional splits proposed by ETSI in the NFV of the dualsite network. In addition, network performance is analyzed in terms of data rate, power consumption, and energy efficiency (EE) optimization. Furthermore, the combined optimization of power consumption and mid-haul bandwidth are investigated, and optimal operating parameters are recommended for similar network operators. Thus, regulators/operators can adjust their networks with these parameters to achieve the best performance. Additionally, the UEs switching scheme is introduced to sleep some RRHs in low-density traffic to lessen power consumption.

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Dense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters. KEYWORDS Cognitive radio network (CRN); Cognitive radio sensor network (CRWSN); Internet of things (IoT); Markovian model; Power efficiency (PE); Radio frequency energy harvesting (RF EH); Wireless sensor network (WSN)

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.