Статті в журналах: "Mobile fog computing"

1

Soo, Sander, Chii Chang, Seng W. Loke, and Satish Narayana Srirama. "Proactive Mobile Fog Computing using Work Stealing." International Journal of Mobile Computing and Multimedia Communications 8, no. 4 (October 2017): 1–19. http://dx.doi.org/10.4018/ijmcmc.2017100101.

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A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.

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2

Artem, Volkov, Kovalenko Vadim, Ibrahim A. Elgendy, Ammar Muthanna, and Andrey Koucheryavy. "DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework." Future Internet 14, no. 1 (December 27, 2021): 13. http://dx.doi.org/10.3390/fi14010013.

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Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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3

Daraseliya, Anastasia V., and Eduard S. Sopin. "Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism." Discrete and Continuous Models and Applied Computational Science 29, no. 1 (December 15, 2021): 53–62. http://dx.doi.org/10.22363/2658-4670-2021-29-1-53-62.

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The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

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4

Xu, Qiaozhi, Junxing Zhang, and Bulganmaa Togookhuu. "Support Mobile Fog Computing Test in piFogBedII." Sensors 20, no. 7 (March 29, 2020): 1900. http://dx.doi.org/10.3390/s20071900.

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IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of various applications and services before they are deployed to the production environment, and current evaluations are more based on various simulation tools; however, the fidelity of the experimental results is a problem for most of network simulation tools. PiFogBed is a fog computing testbed built with real devices, but it does not support the testing of mobile end devices and mobile fog applications. The paper proposes the piFogBedII to support the testing of mobile fog applications by modifying some components in the piFogBed, such as extending the range of end devices, adding the mobile and migration management strategy and inserting a container agent to implement the transparent transmission between end devices and containers. The evaluation results show that it is effective and the delay resulting from the migration strategy and container agent is acceptable.

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5

Parlakkılıç, Alaattin. "Responsive Mobile Learning (M-Learning) Application Design And Architecture In Fog Computing." International Journal of Modern Education Studies 3, no. 2 (December 19, 2019): 82. http://dx.doi.org/10.51383/ijonmes.2019.40.

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Today, digital transformation is changing the educational and social life rapidly. In contrast, organizations and educational institutions, developers, and end users do not benefit from cloud-based mobile technologies to the desired level. Mobile systems are used extensively for educational purposes in the Internet of Things (IoT) environment, and the number of online students is increasing. The real problem is how user-friendly, aesthetic mobile learning courses can be effectively delivered on different mobile devices in the desired performance and manner. The responsive design developed with fog computing should be able to provide the ability to design and use mobile learning lessons with sufficient performance, automatically adapted to any browser or device. This should ensure that every person of the target audience can benefit from the lessons without worrying about screen size, resolution, speed and even security. In this study, the fog informatics teaching strategies of mobile learning sensitive teaching design are discussed. The fog computing architecture that can be used with responsive mobile learning, utilizing mobile computing to provide seamless and low latency mobile devices, is described. Finally, a fog-based, responsive designed mobile learning education architecture has been compiled with a better understanding of the lessons and a suitable structure for the use of mobile devices in education.

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6

Francis, T. "A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4646. http://dx.doi.org/10.11591/ijece.v8i6.pp4646-4653.

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Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.

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7

Lin, Fuhong, Lei Yang, Ke Xiong, and Xiaowen Gong. "Recent Advances in Cloud-Aware Mobile Fog Computing." Wireless Communications and Mobile Computing 2019 (January 23, 2019): 1–2. http://dx.doi.org/10.1155/2019/8204394.

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8

Ahmed, Ejaz, Periklis Chatzimisios, Brij B. Gupta, Yaser Jararweh, and Houbing Song. "Recent advances in fog and mobile edge computing." Transactions on Emerging Telecommunications Technologies 29, no. 4 (April 2018): e3307. http://dx.doi.org/10.1002/ett.3307.

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9

Shuminoski, Tomislav, Stojan Kitanov, and Toni Janevski. "Advanced QoS Provisioning and Mobile Fog Computing for 5G." Wireless Communications and Mobile Computing 2018 (June 7, 2018): 1–13. http://dx.doi.org/10.1155/2018/5109394.

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This paper presents a novel QoS and mobile cloud and fog computing framework for future fifth generation (5G) of mobile and fixed nodes with radio network aggregation capability. The proposed 5G framework is leading to high QoS provisioning for any given multimedia service, higher bandwidth utilization, traffic load sharing, mobile cloud plus fog computing features, and multi-radio interface capabilities. The framework is user-centric, targeted at always-on connectivity with using radio network aggregation for available mobile broadband connections, and empowered with mobile cloud and fog computing advantages. Moreover, our proposed framework is using Lyapunov drift-plus-penalty theorem that provides a methodology for designing algorithm to maximize the average throughput and stabilize the queuing. Also, we are showing the upper bound of the consumed power and the lower bound of the battery lifetime for the proposed 5G terminal. The advanced performance of our 5G QoS plus MCC framework is evaluated using simulations and analysis with multimedia traffic in heterogeneous mobile and wireless environment. The simulation results are showing that the maximal network utilization, maximal throughput, minimal end-to-end delay, efficient energy consumption, and other performance improvements are achieved.

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10

Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

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11

Biswash, Sanjay Kumar, and Dushantha Nalin K. Jayakody. "A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Networks." Sensors 20, no. 21 (October 23, 2020): 6017. http://dx.doi.org/10.3390/s20216017.

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The fog computing-based device-driven network is a promising solution for high data rates in modern cellular networks. It is a unique framework to reduce the generated-data, data management overheads, network scalability challenges, and help us to provide a pervasive computation environment for real-time network applications, where the mobile data is easily available and accessible to nearby fog servers. It explores a new dimension of the next generation network called fog networks. Fog networks is a complementary part of the cloud network environment. The proposed network architecture is a part of the newly emerged paradigm that extends the network computing infrastructure within the device-driven 5G communication system. This work explores a new design of the fog computing framework to support device-driven communication to achieve better Quality of Service (QoS) and Quality of Experience (QoE). In particular, we focus on, how potential is the fog computing orchestration framework? How it can be customized to the next generation of cellular communication systems? Next, we propose a mobility management procedure for fog networks, considering the static and dynamic mobile nodes. We compare our results with the legacy of cellular networks and observed that the proposed work has the least energy consumption, delay, latency, signaling cost as compared to LTE/LTE-A networks.

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12

Alzaghir, A. "Flying Fog Mobile Edge Computing Based on UAV-Assisted for IoT Nodes in Smart Agriculture." Proceedings of Telecommunication Universities 8, no. 4 (January 10, 2023): 82–88. http://dx.doi.org/10.31854/1813-324x-2022-8-4-82-88.

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Flying Fog Mobile Edge Computing can play a pivotal part in the field of smart agriculture. Moreover, is an ideal choice for the significant features it enjoys such as its capability of functioning in remote locations, its wide coverage of areas, sufficient bandwidth, as well as its ability of dealing with connectivity issues. Hence, it is essential for smart agriculture provided with IoT devices to utilize offloading data in a real time and execution the satisfactory steps for a certain circumstance by using flying fog mobile edge computing. Flying Fog Mobile Edge Computing is a good choice to treat connectivity issues. In this paper, proposed a cooperation paradigm of UAVs and IoT devices towards smart agriculture for offloading and executing the computation tasks on-behalf IoT nodes by using dynamic programming algorithm and get satisfactory solution for constrained optimization problem and achieving minimize delay to accomplish tasks.

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13

Farooqi, Abdul Majid, M. Afshar Alam, Syed Imtiyaz Hassan, and Sheikh Mohammad Idrees. "A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation." Applied Sciences 12, no. 4 (February 17, 2022): 2083. http://dx.doi.org/10.3390/app12042083.

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Connected vehicles are a vital part of smart cities, which connect over a wireless connection and bring mobile computation and communication abilities. As a mediator, fog computing resides between vehicles and the cloud and provides vehicles with processing, storage, and networking power through Vehicular Ad-hoc networks (VANET). VANET is a time-sensitive technology that requires less time to process a request received from a vehicle. Delay and latency are the notorious issues of VANET and fog computing. To deal with such problems, in this work, we developed a priority-based fog computing model for smart urban vehicle transportation that reduces the delay and latency of fog computing. To upgrade the fog computing infrastructure to meet the latency and Quality of Service (QoS) requirements, 5G localized Multi-Access Edge Computing (MEC) servers have also been used, which resulted tremendously in reducing the delay and the latency. We decreased the data latency by 20% compared to the experiment carried using only cloud computing architecture. We also reduced the processing delay by 35% compared with the utilization of cloud computing architecture.

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14

Zheng, Hongyun, Yongxiang Zhao, Xi Lu, and Rongzhen Cao. "A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme." Wireless Communications and Mobile Computing 2018 (August 28, 2018): 1–10. http://dx.doi.org/10.1155/2018/6283957.

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Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

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15

Sheltami, Tarek R., Essa Q. Shahra, and Elhadi M. Shakshuki. "Fog Computing: Data Streaming Services for Mobile End-Users." Procedia Computer Science 134 (2018): 289–96. http://dx.doi.org/10.1016/j.procs.2018.07.173.

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16

Li, He, Kaoru Ota, and Mianxiong Dong. "Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing." ACM Transactions on Internet Technology 19, no. 2 (April 24, 2019): 1–18. http://dx.doi.org/10.1145/3234463.

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17

Pham-Nguyen, Hoang-Nam, and Quang Tran-Minh. "Dynamic Resource Provisioning on Fog Landscapes." Security and Communication Networks 2019 (May 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/1798391.

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A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

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18

Moysiadis, Vasileios, Panagiotis Sarigiannidis, and Ioannis Moscholios. "Towards Distributed Data Management in Fog Computing." Wireless Communications and Mobile Computing 2018 (September 2, 2018): 1–14. http://dx.doi.org/10.1155/2018/7597686.

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In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.

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19

Pimenov, Andrey, Ivan Fedorov, and Sergey Bezzateev. "Fog computing architecture using blockchain technology." Information and Control Systems, no. 5 (October 28, 2022): 40–48. http://dx.doi.org/10.31799/1684-8853-2022-5-40-48.

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Introduction: Due to the growth in the number and variety of devices connected to the Internet, the requirements for network performance and data transmission security are increasing. Today, performance problems are usually solved through cloud, fog and edge computing, while the problem of data storage and transmission security remains relevant. One of the effective ways to solve this problem is to use blockchain technology. Purpose: Designing the architecture of a fog computing network based on blockchain technology. Results: Based on the research in the field of fog computing, the requirements for the fog computing architecture were determined, such as: autonomy, scalability, flexibility, hierarchy, security, reliability, availability, serviceability. The selected criteria for building an architecture led to the choice in favor of a private blockchain due to its higher performance compared to a public blockchain A comparative analysis of the consensus algorithms that are most often used in private blockchains was carried out and the most suitable one was chosen. Based on the requirements put forward and the results of the analysis, a fog computing architecture model based on a private blockchain was designed. The architecture consists of four elements: end devices, fog nodes, orchestration nodes, and cloud infrastructure. The blockchain includes fog nodes and orchestration nodes, which ensures the confidentiality, availability and integrity of data in the fog network. Practical relevance: Paper results can be used in the design of fog computing networks both separately and as part of 5G mobile networks.

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20

Elhadad, Ahmed, Fulayjan Alanazi, Ahmed I. Taloba, and Amr Abozeid. "Fog Computing Service in the Healthcare Monitoring System for Managing the Real-Time Notification." Journal of Healthcare Engineering 2022 (March 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/5337733.

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A new computing paradigm that has been growing in computing systems is fog computing. In the healthcare industry, Internet of Things (IoT) driven fog computing is being developed to speed up the services for the general public and save billions of lives. This new computing platform, based on the fog computing paradigm, may reduce latency when transmitting and communicating signals with faraway servers, allowing medical services to be delivered more quickly in both spatial and temporal dimensions. One of the necessary qualities of computing systems that can enable the completion of healthcare operations is latency reduction. Fog computing can provide reduced latency when compared to cloud computing due to the use of only low-end computers, mobile phones, and personal devices in fog computing. In this paper, a new framework for healthcare monitoring for managing real-time notification based on fog computing has been proposed. The proposed system monitors the patient’s body temperature, heart rate, and blood pressure values obtained from the sensors that are embedded into a wearable device and notifies the doctors or caregivers in real time if there occur any contradictions in the normal threshold value using the machine learning algorithms. The notification can also be set for the patients to alert them about the periodical medications or diet to be maintained by the patients. The cloud layer stores the big data into the cloud for future references for the hospitals and the researchers.

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21

Shruthi, G., Monica R. Mundada, B. J. Sowmya, and S. Supreeth. "Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing." Applied Computational Intelligence and Soft Computing 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.1155/2022/2131699.

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Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.

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Zhou, Yiqing, Lin Tian, Ling Liu, and Yanli Qi. "Fog Computing Enabled Future Mobile Communication Networks: A Convergence of Communication and Computing." IEEE Communications Magazine 57, no. 5 (May 2019): 20–27. http://dx.doi.org/10.1109/mcom.2019.1800235.

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23

Son, Yunsik, and Yangsun Lee. "Offloading Method for Efficient Use of Local Computational Resources in Mobile Location-Based Services Using Clouds." Mobile Information Systems 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1856329.

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With the development of mobile computing, location-based services (LBSs) have been developed to provide services based on location information through communication networks or the global positioning system. In recent years, LBSs have evolved into smart LBSs, which provide many services using only location information. These include basic services such as traffic, logistic, and entertainment services. However, a smart LBS may require relatively complicated operations, which may not be effectively performed by the mobile computing system. To overcome this problem, a computation offloading technique can be used to perform certain tasks on mobile devices in cloud and fog environments. Furthermore, mobile platforms exist that provide smart LBSs. The smart cross-platform is a solution based on a virtual machine (VM) that enables compatibility of content in various mobile and smart device environments. However, owing to the nature of the VM-based execution method, the execution performance is degraded compared to that of the native execution method. In this paper, we introduce a computation offloading technique that utilizes fog computing to improve the performance of VMs running on mobile devices. We applied the proposed method to smart devices with a smart VM (SVM) and HTML5 SVM to compare their performances.

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Liutkevičius, Agnius, Nerijus Morkevičius, Algimantas Venčkauskas, and Jevgenijus Toldinas. "Distributed Agent-Based Orchestrator Model for Fog Computing." Sensors 22, no. 15 (August 7, 2022): 5894. http://dx.doi.org/10.3390/s22155894.

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Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed.

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25

Lim, Jongbeom. "Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling." Applied Sciences 11, no. 22 (November 19, 2021): 10996. http://dx.doi.org/10.3390/app112210996.

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As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

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侯, 严严. "Research on Task Offloading of Fog Computing in Mobile Terminals." Computer Science and Application 12, no. 02 (2022): 315–22. http://dx.doi.org/10.12677/csa.2022.122031.

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27

Bai, Wenle, Zhongjun Yang, Jianhong Zhang, and Rajiv Kumar. "Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing." Security and Communication Networks 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/4348511.

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Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

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Mohammed Jameel, Shymaa, and Muayad Sadik Croock. "Mobile learning architecture using fog computing and adaptive data streaming." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 5 (October 1, 2020): 2454. http://dx.doi.org/10.12928/telkomnika.v18i5.16712.

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Li, Keqin. "Heuristic Computation Offloading Algorithms for Mobile Users in Fog Computing." ACM Transactions on Embedded Computing Systems 20, no. 2 (January 4, 2021): 1–28. http://dx.doi.org/10.1145/3426852.

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30

Zhao, Dongcheng, Gang Sun, Dan Liao, Shizhong Xu, and Victor Chang. "Mobile-aware service function chain migration in cloud–fog computing." Future Generation Computer Systems 96 (July 2019): 591–604. http://dx.doi.org/10.1016/j.future.2019.02.031.

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Khujamatov, Halimjon, Khaleel Ahmad, Nargiza Usmanova, Jamshid Khoshimov, Mai Alduailij, and Mona Alduailij. "Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario." Sustainability 14, no. 13 (July 1, 2022): 8070. http://dx.doi.org/10.3390/su14138070.

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With the development of Internet technologies, huge amounts of data are collected from various sources, and used ‘anytime, anywhere’ to enrich and change the life of the whole of society, attract ways to do business, and better perceive people’s lives. Those datasets, called ‘big data’, need to be processed, stored, or retrieved, and special tools were developed to analyze this big data. At the same time, the ever-increasing development of the Internet of Things (IoT) requires IoT devices to be mobile, with adequate data processing performance. The new fog computing paradigm makes computing resources more accessible, and provides a flexible environment that will be widely used in next-generation networks, vehicles, etc., demonstrating enhanced capabilities and optimizing resources. This paper is devoted to analyzing fog computing capabilities for big data provisioning, while considering this technology’s different architectural and functional aspects. The analysis includes exploring the protocols suitable for fog computing by implementing an experimental fog computing network and assessing its capabilities for providing big data, originating from both a real-time stream and batch data, with appropriate visualization of big data processing.

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Hu, Jianqiang, Keshou Wu, and Wei Liang. "An IPv6-based framework for fog-assisted healthcare monitoring." Advances in Mechanical Engineering 11, no. 1 (January 2019): 168781401881951. http://dx.doi.org/10.1177/1687814018819515.

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The new generation healthcare monitoring system combines technologies of wireless body sensor network, cloud computing, and Bigdata, and there are still limitations in protocol security, response delay, and prediction of potential severity disease. In response to the above situation, an Internet Protocol Version 6 (IPv6)-based framework for fog-assisted healthcare monitoring is proposed. This framework is composite of body-sensing layer, fog layer, and cloud layer. The body-sensing layer generates physiological data, and fog computing node in fog layer collects and analyses time-sensitive data. Fog layer sends physiological data to cloud computing node in cloud layer for further processing. Mobile intelligent device connects fog computing node and helps individuals to predict the potential disease with its level of severity. The proposed framework uses advanced techniques such as IPv6-based network architecture, cloud–fog resource scheduling algorithm based on time threshold, and classification model of chronic diseases based on cascaded deep learning and so on. In order to determine the validity of the framework, health data were systematically generated from 45 patients for 30 days. Results depict that the proposed classification model of chronic diseases has high accuracy in determining the level of severity of potential disease. Moreover, response delay is much lower than Internet Protocol Version 4 (IPv4)-based cloud-assisted environment.

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Markus, Andras, Mate Biro, Karolj Skala, Zorislav Šojat, and Attila Kertesz. "Modeling Dew Computing in DISSECT-CF-Fog." Applied Sciences 12, no. 17 (September 1, 2022): 8809. http://dx.doi.org/10.3390/app12178809.

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Fog computing provides an effective solution to various problems by extending the cloud’s functionality to typically more limited computing units closer to user devices. Fog computing can provide a higher level of user experience due to its geographic and network topology location and distribution. IoT services also need to be managed seamlessly to ensure adequate QoS (due to the mobility of devices or the temporary periods without an internet connection). Such domains are combined under the auspices of Dew computing, as in critical cases, extending an IoT service to the end user’s device is a feasible task. Such scenarios can hardly be investigated at a large scale due to the lack of dedicated simulation environments. In this paper, we present an extension of the DISSECT-CF-Fog simulator with a Dew computing model, to enable the simulation of IoT-Dew-Fog systems in a cost-effective manner. In particular, we focus on service migration options for mobile devices and cases with temporary internet access limitations. Finally, we performed measurements of real-world use cases with the extended simulator as an evaluation. Our simulation results show that the proposed proactive strategy reduces the processing time of IoT data, exploiting an IoT-Dew-Fog environment.

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Roig, Pedro Juan, Salvador Alcaraz, Katja Gilly, and Carlos Juiz. "Modelling VM Migration in a Fog Computing Environment." Elektronika ir Elektrotechnika 25, no. 5 (October 6, 2019): 75–81. http://dx.doi.org/10.5755/j01.eie.25.5.24360.

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Fog Computing is created to efficiently store and access data without the limitations challenging Cloud Computing deployments, such as network latency or bandwidth constraints. This is achieved by performing most of the processing on servers located as close as possible to where data is being collected. When mobile devices are equipped with limited resources and small capabilities, it would be convenient to make their associated computing and network resources follow them as much as possible. In this paper, migration process is studied and an algorithmic model is designed, selecting a generic Fat Tree architecture as the underlying topology, which may be useful to get a list of all devices being traversed through each of the redundant paths available.

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Scarpiniti, Michele, Enzo Baccarelli, and Alireza Momenzadeh. "VirtFogSim: A Parallel Toolbox for Dynamic Energy-Delay Performance Testing and Optimization of 5G Mobile-Fog-Cloud Virtualized Platforms." Applied Sciences 9, no. 6 (March 19, 2019): 1160. http://dx.doi.org/10.3390/app9061160.

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It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared.

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Sharma, Shivi, and Hemraj Saini. "Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony." International Journal of Ambient Computing and Intelligence 10, no. 4 (October 2019): 60–77. http://dx.doi.org/10.4018/ijaci.2019100104.

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Fog computing is a set of mobile cloudlets which can fulfil the demand of the user who is already considered a mobile job in this architecture. The main aim of Fog computing is to provide the user with an optimal solution which is quick and cost-efficient. This article focuses on a load balancing mechanism for cloudlets along with keeping the cost-effectiveness as an optimal selection parameter. This article utilizes the Artificial Bee Colony (ABC) in order to prioritize the user demand using a fitness function. This work evaluates quality of service (QoS) parameters such as schedule length runtime (SLR), schedule length vm ratio (SLVMR), energy consumed (EC) and energy consumption ratio (ECR) and shows the effectiveness of proposed work.

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Vergis, Spiridon, Vasileios Komianos, Georgios Tsoumanis, Athanasios Tsipis, and Konstantinos Oikonomou. "A Low-Cost Vehicular Traffic Monitoring System Using Fog Computing." Smart Cities 3, no. 1 (March 19, 2020): 138–56. http://dx.doi.org/10.3390/smartcities3010008.

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With the rapid increase of vehicles in use worldwide, the need for efficient traffic monitoring systems has arisen. This work proposes a low-cost vehicular traffic monitoring system using IoT devices and fog computing. The system is based on a three-tiered architecture which is composed of (i) the mobile tracking system that records the positions of the vehicles using GPS technologies; (ii) the information gathering system which gathers all the data collected by the mobile tracking system; and (iii) the fog devices that process the data collected and extract the information needed. The system is tested in the town of Corfu during a period of increased tourism when the traffic is considered to be relatively dense. The mobile tracking system devices are placed on taxis and with the help of professional taxi drivers the accuracy of the data collected is evaluated. The system is able to record the movement of the vehicles accurately using its own independent data. The results can be remotely accessed by utilizing fog and cloud computing infrastructure established to process the data and upload it on a server. The system is used to give a better understanding of the speed variance in the center of the town during different dates and hours. In conclusion the system presented in this study can be utilized to monitor the traffic and provide vital information about its behavior in relation to time.

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Brooks, Tyson. "Authenticating Devices in Fog-mobile Edge Computing Environments through a Wireless Grid Resource Sharing Protocol." International Journal of UbiComp 13, no. 2 (April 30, 2022): 1–17. http://dx.doi.org/10.5121/iju.2022.13201.

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The rapid growth of the Internet of Things (IoT), cloud computing, Fog computing, mobile edge computing and wireless grids has resulted in the widespread deployment of relatively immature technology. These technologies, which will primarily use 5G wireless communication networks, are becoming popular because they can be deployed quickly with little infrastructure and lends themselves to environments utilizing numerous internet connected devices (ICD). There are, however, many significant challenges faced by security designers, engineers and implementers of these networks in ensuring that the level of security afforded is appropriate. Because of the threat of exploitation, these networks have to be protected by a robust security architecture due to these technologies being plagued with security problems. The authentication of smart ICDs to IoT networks is a critical mechanism for achieving security on these new information system platforms. This article identifies an authentication process required for these ICDs, which will need to prove their identity to authenticate to an IoT fog-mobile edge computing (FMEC) cloud network through a wireless grid authentication process. The purpose of this article is to begin to hypothesize a generic authentication methodology for these FMEC clouds uses in an IoT architecture. The proposed methodology, called wg-IoT, must include the integration of Fog computing, wireless grids and mobile edge computing clouds to create this new IoT architecture. An authentication process developed from the resource sharing protocol (RSP) from a wireless grid is first developed and proposed for the authentication of ICDs. The wireless grid core components must be embedded in IoT devices or sensors depending on their capability to handle five primary functions: management of identification [ID] and presence, permissions management, data transferability, application-programming interface [API] and security.

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Mohiuddin, Khalid, Mohamed Nadhmi Miladi, Mohiuddin Ali Khan, Mohammad Abdul Khaleel, Sajid Ali Khan, Samreen Shahwar, A. Nasr, and Mohammad Aminul Islam. "Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency." Wireless Communications and Mobile Computing 2022 (September 5, 2022): 1–17. http://dx.doi.org/10.1155/2022/6151168.

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Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.

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Peng, Kai, Victor C. M. Leung, Lixin Zheng, Shangguang Wang, Chao Huang, and Tao Lin. "Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4680867.

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Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

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Zhou, Yutong, Wei Shi, and Fei Song. "A Smart Collaborative Policy for Mobile Fog Computing in Rural Vitalization." Wireless Communications and Mobile Computing 2018 (November 5, 2018): 1–10. http://dx.doi.org/10.1155/2018/2643653.

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Mobile Fog Computing (MFC), as a crucial supplement to cloud computing, has its own special traits in many aspects. As smart mobile devices grow and vary in shapes and formats over the years, the need for real-time interactions and an easy-to-use network is imminent. In this paper, we propose a smart collaborative policy for MFC scenarios by considering the target of rural vitalization. The challenges and drawbacks of extending cloud to fog are reviewed at the beginning. Then, the analysis of policy design is presented from the perspectives of feature comparisons, urgent requirements, and possible solutions. The details of policy establishment are introduced with necessary examples. Finally, performance evaluations are provided based on simulation platforms. Validation results related to round trip time and transmission time illustrate the significant improvements of our proposal in certain ways compared to the original candidate, which enables larger deployment in impoverished areas.

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42

Di, Xiaofei, Yu Zhang, Tong Liu, Shaoli Kang, and Yue Zhao. "Mobile Fog Computing-Assisted Resource Allocation for Two-Hop SWIPT OFDM Networks." Wireless Communications and Mobile Computing 2018 (September 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/7606513.

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The mobile fog computing-assisted resource allocation (RA) is studied for simultaneous wireless information and power transfer (SWIPT) two-hop orthogonal frequency division multiplexing (OFDM) networks, where a decode-and-forward (DF) relay first harvests energy from signals emitted by a source and then helps the source to forward information to its destination by using the harvested energy. Power splitting (PS) strategy is adopted at the relay and a different PS (DPS) receiver architecture is proposed, where the PS factors of all subcarriers are different. A RA problem is formulated to maximize the system’s achievable rate by jointly optimizing subcarrier pairing, power allocation, and PS factors. Since the RA problem is a nonconvex problem and is difficult to solve, an efficient RA algorithm is designed. As the wireless channels are fast time-varying, the computation is performed in mobile fog node close to end nodes, instead of remote clouds. Results demonstrate that the achievable rate is significantly increased by using the proposed RA algorithm. It is also found that the computation complexity of RA algorithm of DPS receiver architecture is much lower than the existing identical PS (IPS) receiver architecture, and thus the proposed DPS architecture is more suitable for computation-constrained fog system.

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43

Yang, Ke. "Application of Basketball Training System Based on Dynamic Intelligent Fog Computing Network." Mobile Information Systems 2021 (August 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/3221639.

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Although the development of the mobile Internet and the Internet of Things has greatly promoted the progress and development of society, it has also created many problems for people on the road of scientific and technological exploration. In order to meet the problems and requirements of high bandwidth, high load, and low latency in the current network development, the emergence of the concept of mobile edge computing has attracted extensive attention from the academic community. This article focuses on the representative mode of mobile edge computing-fog computing (in this model, data, (data)processing, and applications are concentrated in devices at the edge of the network, instead of being stored almost entirely in the cloud). By applying it to the development and operation of basketball training system, it explores the performance of dynamic intelligent fog computing in intelligent end user services. This paper proposes a fog resource scheduling scheme based on linear weighted genetic algorithm, which converts the problem of multiobjective optimization into a single-objective optimization problem. When applying the genetic algorithm based on weighted sum, preference is given to delay, communication load, and service cost. Value is integrated into an objective function to perform genetic operations to get a better solution. From the experimental data, the system can support 20 DCTU terminals with a pressure request of 10 messages per second per terminal under the pressure environment created by the pressure test input data. The barrier-free transmission distance is 200 m, and the barrier transmission distance is 50 m. It has strong fault tolerance.

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Ali, Abid, Muhammad Munawar Iqbal, Harun Jamil, Faiza Qayyum, Sohail Jabbar, Omar Cheikhrouhou, Mohammed Baz, and Faisal Jamil. "An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing." Sensors 21, no. 13 (July 1, 2021): 4527. http://dx.doi.org/10.3390/s21134527.

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Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

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Shi, Gang, and Yuechen Yang. "Analysis of an Automatic Early Warning System Based on Fog Architecture." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012046. http://dx.doi.org/10.1088/1742-6596/2095/1/012046.

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Abstract In the process of analysing and processing terminal sensor information, a large number of terminal sensors are needed to collect front-end information. These front-end data collection, analysis and processing require high real-time, and need the support of location aware mobile computing services. Traditional cloud computing architecture is not the best choice for service scenarios with high real-time requirements. The fog computing architecture is to extend cloud computing services to the edge of the sensor network, coupled with appropriate fitness algorithms, can effectively improve the information analysis and early warning response speed of the geological disaster information early warning system.

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Feng, Zhenqiang. "Protocol for reliable energy data collection based on mobile fog computing." Sustainable Energy Technologies and Assessments 44 (April 2021): 101086. http://dx.doi.org/10.1016/j.seta.2021.101086.

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47

Guo, Yimin, and Yajun Guo. "FogHA: An efficient handover authentication for mobile devices in fog computing." Computers & Security 108 (September 2021): 102358. http://dx.doi.org/10.1016/j.cose.2021.102358.

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R. Sreekanth, G., S. Ahmed Najat Ahmed, Marko Sarac, Ivana Strumberger, Nebojsa Bacanin, and Miodrag Zivkovic. "Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes." Computer Systems Science and Engineering 41, no. 2 (2022): 751–65. http://dx.doi.org/10.32604/csse.2022.020534.

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Li, Jirui, Xiaoyong Li, Jie Yuan, Rui Zhang, and Binxing Fang. "Fog Computing-Assisted Trustworthy Forwarding Scheme in Mobile Internet of Things." IEEE Internet of Things Journal 6, no. 2 (April 2019): 2778–96. http://dx.doi.org/10.1109/jiot.2018.2874808.

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50

Rahbari, Dadmehr, and Mohsen Nickray. "Task offloading in mobile fog computing by classification and regression tree." Peer-to-Peer Networking and Applications 13, no. 1 (February 1, 2019): 104–22. http://dx.doi.org/10.1007/s12083-019-00721-7.

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