Measuring and Assessing Performance of Mobile Broadband Networks and Future 5G Trends

El‐Saleh, Ayman A.; Alhammadi, Abdulraqeb; Shayea, Ibraheem; Alsharif, Nizar; Alzahrani, Nouf M.; Khalaf, Osamah Ibrahim; Aldhyani, Theyazn H. H.

doi:10.3390/su14020829

Cited by 43 publications

(29 citation statements)

References 18 publications

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“…e LSTM layer has a large number of series LSTM units, collectively referred to as the LSTM model [39][40][41]. ree multiplicative units are contained within the LSTM models: first, the input gate, which is used to store information from the current time; second, the output gate, which is responsible for displaying the results; third, the forget gate, which is used to select some already forgotten information from the past.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Alzahrani

Aldhyani

Alsubari

et al. 2022

Security and Communication Networks

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Industry 4.0, also known as the Internet of Things, is a concept that encompasses the joint applicability of operation, the Internet, and information technologies to expand the efficiency expectation of automation to include green and flexible processes and innovative products and services. Industrial network infrastructures must be modified to accommodate extra traffic from a variety of technologies in order to achieve this integration. In order to successfully implement cutting-edge wireless technologies, high-quality service (QoS) must be provided to end users. It is thus important to keep an eye on the functioning of the whole network without impacting base station throughput. Improved network performance is constantly needed, even for already-deployed cellular networks, such as the 4th generation (4G) and 3rd generation (3G). For the purpose of forecasting network traffic, an integrated model based on the long short-term memory (LSTM) model was used to combine clustering rough k-means (RKM) and fuzzy c-means (FCM). Clustering granules derived from FCM and RKM were also utilized to examine the network data for each calendar year. The novelty of our proposed model is the integration of the prediction and forecasting results obtained using existing prediction models with centroids of clusters. The WIDE backbone network’s live network traffic statistics were used to evaluate the proposed solution. The integrated model’s outcomes were assessed using a variety of statistical markers, including mean square error (MSE), root mean square error (RMSE), and standard error. The suggested technique was able to provide findings that were very accurate. The prediction error of LSTM with FCM was less on the basis of the MSE of 0.00783 and RMSE of 0.0885 at the training phase, where the prediction values of LSTM with the RKM had an MSE of 0.00564 and RMSE of 0.7511. Finally, the suggested model may substantially increase the prediction accuracy attained using FCM and RKM clustering.

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Section: Long Short-term Memorymentioning

confidence: 99%

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Alzahrani

Aldhyani

Alsubari

et al. 2022

Security and Communication Networks

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“…In Figure 5, the X axis represents the number of nodes, and the Y axis represents the Average packet delay (s). The average packet delay of the techniques decreases as the number of sensor nodes increases, due to the ability to discover more qualifying nodes in their region to relay packets [46]. It is also found that the ECCAR has a longer average packet delay than the other methods.…”

mentioning

confidence: 89%

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Kanthavel

Dhaya

Khalaf

et al. 2022

Sensors

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Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

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“…[60] presented research to map out the current state of knowledge on energy efficiency's impact on cloud computing and, in turn, help to build a better picture of the future of this area of study. No emphasis was given to techniques to reduce energy consumption [132][133][134][135][136][137][138][139][140][141][142][143][144][145].…”

Section: Related Surveysmentioning

confidence: 99%

“…In order to lower the carbon footprint of data centers, this research focused on decreasing brown energy and increasing renewable energy consumption. The authors [7] proposed a self-adaptive architecture based on microservices and renewable energy for interactive and batch workloads [140][141][142][143][144][145][146].…”

Section: Related Surveysmentioning

confidence: 99%

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A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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Measuring and Assessing Performance of Mobile Broadband Networks and Future 5G Trends

Cited by 43 publications

References 18 publications

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

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