Fog computing is a paradigm that utilizes the advantages of both the cloud and the edge devices providing quality services, reducing latency, providing mobility support, multi-tenancy, and many other functions that support modern computing systems. It is sometimes referred to as fog networking or fogging. This paper reviews and discusses cloud computing, briefly highlighting the implemented paradigms before fog computing. These paradigms include cloud, mobile cloud computing, and mobile edge computing. All the paradigms targeted improving the quality of service between the end devices and the cloud itself. A fog computing Taxonomy is presented based on contemporary fog computing research about security challenges, services issues, operational issues, and data management. The standard for elucidating the taxonomy is built on the functional and vital issues in fog computing. Challenges and potential applications are identified. The review shows that security, privacy, application, and communication challenges are prominent among scholars contributions. Potential applications in fog computing are also identified, including healthcare applications, innovative city applications, and farm applications. 

A review on Fog Computing: Issues, Characteristics, Challenges, and Potential Applications

Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

https://www.mdpi.com/1424-8220/23/5/2445/pdf?version=1677216779

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.

https://www.mdpi.com/2073-8994/15/4/779/pdf?version=1679622110

Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance

Hydrothermal synthesis was utilized to achieve the direct growth of molybdenum disulphide (MoS2) nanoflakes on carbon nanotubes (CNT). Through this method, MoS2/CNT heterostructures with engineered microstructure have been obtained by suitably optimizing the synthesis conditions. Raman, X-ray photoelectron spectroscopy, and electron microscopy have been utilized to characterize the heterostructure. The present study investigated the influence of varying amounts of CNT and MoS2 within the heterostructure on its electrochemical properties. The optimization of the heterostructure morphology has resulted in enhanced electrochemical performance, with a high specific capacitance of ∼436 F/g at 1 A/g, along with good rate capability. The charge storage mechanism was explained by using Dunn's power law. Further, the MoS2/CNT heterostructure was employed to fabricate a solid-state symmetric supercapacitor. The device exhibited a specific capacitance of ∼164 F/g at 1 A/g, along with good cyclic stability, retaining 96 % of its capacitance after 1000 cycles. Additionally, the device demonstrated high energy and power density values of ∼27 Wh/Kg and 603 W/Kg, respectively. The present study indicates that the MoS2/CNT heterostructure holds great promise as an electrode material for high-performance solid-state supercapacitor devices. 

Engineered nano-architecture for enhanced energy storage capabilities of MoS2/CNT-heterostructures: A potential supercapacitor electrode

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.

/pdf/energy-efficient-task-scheduling-and-resource-allocation-for-2cumkw6j.pdf

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Enhanced gravimetric capacitance of CeO2 by compositing with the hybridized phase of MoS2

Influence of different nutrient management schedules on physiological attributes of organic cabbage (Brassica oleracea var. capitata L.)

Spacing and propagule size on yield and quality of Curcuma aromatica Salisb.

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Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Enhanced gravimetric capacitance of CeO2 by compositing with the hybridized phase of MoS2

Influence of different nutrient management schedules on physiological attributes of organic cabbage (Brassica oleracea var. capitata L.)

Spacing and propagule size on yield and quality of Curcuma aromatica Salisb.