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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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Information availability is a key factor in the acquisition of knowledge. Access to information either in the general area or even in more specific ones like sciences, languages, and religion become wider since the use of semantics in World Wide Web. Semantic Web technologies assist in the acquiring of information by creating processes that link information to another. However, the technology supports mostly languages using Latin family scripts. Arabic is still not well supported. This paper, reports on the survey of the support for Arabic in some of the existing Semantic Web technologies, and give future scenario in applying Semantic Web for Arabic applications. Finally, multilingual support for these new technologies is also discussed.

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In the modern society, Internet provides massive amounts of heterogeneous information, hence Information overload has become an ubiquitous issue. In this paper, we conduct a large scale quantitative study for articles dealing with (1) information overloading; (2) faceted search; and (3) filtering the data in three major databases, namely, Web of Science, ScienceDirect, and IEEE Explore. These three databases have presented 172 articles, which can be classified into four categories. The first category contains review and survey papers related to information overload. The second category includes papers that concentrate on developing theoretical frameworks to reduce information overloading. The third category contains papers dealing with improving structure or architectural of software for filtering the huge data. The fourth category includes papers that provide criteria to evaluate filtering techniques. Finally, our contribution provides further understanding of information overload, and gives an important basis for future research. Moreover, we illustrate that the dynamic faceted filters are more efficient to reduce the information overload.