Ahmed Ali Hussein

Background: Mobile networks today specifically 5G require appreciable secure networks because of the emerging risks due to the growth in the deployment of network structures. Discovered weaknesses of cryptographic conventional methods to quantum computing breakthroughs make it necessary to develop quantum-resistant solutions. Objective: The article analysing Quantum Key Distribution (QKD) protocols in improving cryptographic performance in 5G networking environment, with emphasis on incorporating QKD into 5G network designs. Methods: The study performed both a systematic literature review and an evaluation of current QKD deployments, as well as a qualitative assessment of data derived from 20 key informant interviews on QKD in telecommunications and 15 technical reports. Latency and key generation rate experiments were both conducted with relay mechanisms including both trusted and untrusted optical fiber and wireless relay links, in addition to integration issues were explored using simulations over fiber and wireless emulated networks. Results: The outcomes emphasise that QKD brings radically enhanced key security in conjunction with low delay and high rate within integrated 5G architectures. Hybrid relay-based QKD augmented key generation rates by 23 % in comparison with previous techniques. There are also concerns associated with the implementation of internationally agreed on standards which include issues pertaining to non-compliance of the standards used in different countries and high costs involved when trying to implement these standards. Conclusion: QKD implementation also increases cryptographic protection of the 5G networks and makes infrastructures quantum-immune to threats originating from the quantum-age. To make it more widespread, additional standardization and a reduction in cost are required.

Background: The telecommunications’ growth, especially with the emergence of 5G, has led to the requirement of low latency solutions. Current cloud computing models possess architectural flaws that prevent real-time service delivery, critical in applications of autonomous vehicles, augmented reality among others. Objective: This article reviews how edge computing can be combined with 5G networks to overcome the latency issues in today’s telecommunication systems. They look at how this combination can cut down latency by processing data closer to the end consumer and its potential to disrupt several industries. Methods: This research uses the literature review of current information in 5G and edge computing systems, architectures, practices, and theoretical frameworks. The result of the work is based on the assessment of the existing solutions in the implementation of edge computing within the 5G environment based on case analysis. Results: The analysis shows that all the applications such as self-driving cars and industrial robotics experienced 40 to 70% reduced latency. Also, edge computing results in better resources management in case of telecommunications since it deems many computing tasks to localized edge nodes from cloud. Conclusion: Combining edge computing with networking also provides a distinctive model for addressing latency problems while enhancing the network and boosting industry development. Concerning the research limitations, the future research should explore ways of improving the efficiency of resource allocation to meet the company’s needs and explore the scalability issues.

Background: The emergence of 6G networks requires new approaches to extend coverage, increase network availability and optimize performance in difficult conditions, including urban and rural areas. Thus, UAVs or UAV systems have developed as a powerful candidate to counter these problems by offering on-demand contingent coverage and differing communication services.   Objective: The opportunity of the development of UAVs’ application in the extension of the network’s coverage is studied in the context of energy efficiency, latency, and Inter-UE interference in high-density 6G environment. Methods: A three-layered optimization architecture was devised, including multi-agent reinforcement learning (MARL) for interference control, trajectory optimization techniques, and energy-aware deployment schemes. Small scale scenarios including urban, suburban and rural environment were considered and the results were analyzed based on the network coverage, energy efficiency, end to end latency and interference encountered on UAVs. Results: The outcome significantly revealed the enhancements in the spatial coverage of the network; UAVs prevented considerable gaps and offered enhancements of network coverage in rural and suburban regions. These achievements include up to 30.5% energy efficiency enhancement, more than 50% latency minimization and interference management that enabled 35.4% enhancement of SINR. Conclusion: Integrating of drones in 6G network is invaluable in enhancing coverage in the networks by providing massive coverage while at the same time providing scalable solutions to problems of coverage gaps, power demands and real-time network adjustments. In future studies, researchers should channel their efforts toward increasing real-time dynamism and energy consumption that suit large-scale executions.

  Background: Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.   Objective: The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.   Methods: A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.   Results: The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.   Conclusion: Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025