Статті в журналах: "Edge Networking"

1

Adams, Robert. "Data Networking: A Competitive Edge." Industrial Management & Data Systems 86, no. 5/6 (May 1986): 13–15. http://dx.doi.org/10.1108/eb057441.

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2

Gudhka, Drashti. "Computer Network." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 78–87. http://dx.doi.org/10.22214/ijraset.2024.57862.

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Abstract: This paper presents a comprehensive overview of computer networking, covering fundamental concepts and modern advancements. It explores core networking principles, including models, architectures, and essential layers. Emphasising contemporary trends, it delves into topics like network security (Zero Trust Architecture, AI/ML), Software-Defined Networking (SDN), IoT security challenges, 5G and Mobile Edge Computing (MEC), network performance optimisation, Big Data analytics, and eco-friendly networking strategies. Aimed at students, researchers, and professionals, this overview serves as a valuable resource for understanding both foundational principles and cutting-edge developments in networking.

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3

Xin, Jing, and Jia Pin Xu. "Based on Edge Node Network Service Recognition Research." Applied Mechanics and Materials 55-57 (May 2011): 1473–78. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1473.

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At present, the recognition ability of the service recognition technique is limited to single installation. It leads to recognition error easily and it's dangerous for the Next Generation Networks which depend on service recognition and control. This paper is based on TCP/IP architecture. With the studying of the present service recognition techniques, a model of networking service recognition system based on edge node is designed. And using the modeling recognition method, the information exchange among recognition nodes, and the second recognition, dynamic recognition of networking service flow is implemented. The testing result of the networking service recognition system model is that, at different protocol types and different ports, the recognition rate is from 99.39% to 99.89%, which indicates that the networking service recognition system based on edge nodes can meet the service recognition requirements of the Next Generation Networks.

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4

Domet, Jack J., T. M. Rajkumar Domet, and David Yen. "MULTIMEDIA NETWORKING A Leading-Edge Communication Technology." Information Systems Management 11, no. 4 (January 1994): 39–45. http://dx.doi.org/10.1080/07399019408964669.

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5

Shiwen Mao, Y. T. Hou, and Min-You Wu. "Exploiting edge capability for wireless sensor networking." IEEE Wireless Communications 15, no. 4 (August 2008): 67–73. http://dx.doi.org/10.1109/mwc.2008.4599223.

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6

Prieto, Javier, and Ramón J. Durán Barroso. "Emerging Technologies in Edge Computing and Networking." Sensors 24, no. 4 (February 17, 2024): 1271. http://dx.doi.org/10.3390/s24041271.

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The global evolution of the Internet is experiencing a notable and inevitable change towards a convergent scenario known as the Internet of Things (IoT), where a large number of devices with heterogeneous characteristics and requirements have to be interconnected to serve different verticals, such as smart cities, intelligent transportation systems, smart grids, (ITS) or e-health [...]

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7

Wang, Dan, Fangming Liu, Wei Bao, Lin Wang, Jiangchuan Liu, Yuanyuan Yang, and Weisong Shi. "Editorial of CCF transactions on networking: special issue on edge computing and networking." CCF Transactions on Networking 2, no. 1 (May 3, 2019): 1–3. http://dx.doi.org/10.1007/s42045-019-00017-7.

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8

Mastorakis, Spyridon, Abderrahmen Mtibaa, Jonathan Lee, and Satyajayant Misra. "ICedge: When Edge Computing Meets Information-Centric Networking." IEEE Internet of Things Journal 7, no. 5 (May 2020): 4203–17. http://dx.doi.org/10.1109/jiot.2020.2966924.

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9

Wei, Hung-Yu, Tao Zhang, T. Russell Hsing, and Doug Zuckerman. "Future Trends in Fog/Edge Computing and Networking." IEEE Communications Magazine 61, no. 12 (December 2023): 38–39. http://dx.doi.org/10.1109/mcom.2023.10375689.

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10

Ros, Seyha, Prohim Tam, Inseok Song, Seungwoo Kang, and Seokhoon Kim. "A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking." Electronic Research Archive 32, no. 2 (2024): 1333–64. http://dx.doi.org/10.3934/era.2024062.

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<abstract> <p>Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.</p> </abstract>

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11

Chen, Junhua, Chenggen Pu, Ping Wang, Xueda Huang, and Yanfei Liu. "A blockchain-based scheme for edge–edge collaboration management in time-sensitive networking." Journal of King Saud University - Computer and Information Sciences 36, no. 1 (January 2024): 101902. http://dx.doi.org/10.1016/j.jksuci.2023.101902.

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12

Dong, Jian, Bin Chen, Pengfei Zhang, Chuan Ai, Fang Zhang, Danhuai Guo, and Xiaogang Qiu. "Evolution Model of Spatial Interaction Network in Online Social Networking Services." Entropy 21, no. 4 (April 24, 2019): 434. http://dx.doi.org/10.3390/e21040434.

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The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities.

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13

Li, Zhiyuan, and Ershuai Peng. "Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking." Sensors 21, no. 3 (February 1, 2021): 955. http://dx.doi.org/10.3390/s21030955.

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With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

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14

Mukherjee, Debangshu, Kevin M. Roccapriore, Anees Al-Najjar, Ayana Ghosh, Jacob D. Hinkle, Andrew R. Lupini, Rama K. Vasudevan, et al. "A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy." Microscopy Today 30, no. 6 (November 2022): 10–19. http://dx.doi.org/10.1017/s1551929522001286.

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Abstract:The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. Here we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

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15

Deng, Der-Jiunn, Shao-Yu Lien, Chun-Cheng Lin, Shao-Chou Hung, and Wei-Bo Chen. "Latency Control in Software-Defined Mobile-Edge Vehicular Networking." IEEE Communications Magazine 55, no. 8 (August 2017): 87–93. http://dx.doi.org/10.1109/mcom.2017.1601165.

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16

Ergen, Mustafa, Feride Inan, Onur Ergen, Ibraheem Shayea, Mehmet Fatih Tuysuz, Azizul Azizan, Nazim Kemal Ure, and Maziar Nekovee. "Edge on Wheels With OMNIBUS Networking for 6G Technology." IEEE Access 8 (2020): 215928–42. http://dx.doi.org/10.1109/access.2020.3038233.

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17

Ahmed, Ejaz, Muhammad Imran, Albert Y. Zomaya, Ammar Rayes, Joel J. P. C. Rodrigues, and Wael Guibene. "Enabling Wireless Communications and Networking Technologies of Edge Computing." IEEE Communications Magazine 56, no. 9 (September 2018): 94–95. http://dx.doi.org/10.1109/mcom.2018.8466362.

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18

Su, Xin, Leilei Meng, and Jun Huang. "Intelligent Maritime Networking With Edge Services and Computing Capability." IEEE Transactions on Vehicular Technology 69, no. 11 (November 2020): 13606–20. http://dx.doi.org/10.1109/tvt.2020.3015751.

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19

Fan, Zhenyu, Wang Yang, Fan Wu, Jing Cao, and Weisong Shi. "Serving at the Edge: An Edge Computing Service Architecture Based on ICN." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–27. http://dx.doi.org/10.1145/3464428.

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Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

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20

Smeliansky, Ruslan L. "Hierarchical Edge Computing." Modeling and Analysis of Information Systems 26, no. 1 (March 15, 2019): 146–69. http://dx.doi.org/10.18255/1818-1015-2019-1-146-169.

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The computing paradigm based on the giant-like DC is replaced by a new paradigm. The urgency of this shift is caused by the requirements of new applications that actively use video, real-time interactivity, new mobile communication technologies, which today cannot be implemented without the usage of cloud computing and virtualization based on SDN&NFV technologies. The presentation considers the requirements dictated by these applications, outlines the architecture of this new paradigm which we call Hierarchical Edge Computing (HEC). Attention is focused on the fact that all these applications are distributed, become more and more real-time applications and require guaranteed quality of service in the networking operation. The main scientific problems that need to be solved for implementing this new paradigm are discussed.

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21

Hu, Qing, Chengming Li, Touhidul Hasan, Chengjun Li, and Qingshan Jiang. "A collaborative caching strategy in contentcentric networking." MATEC Web of Conferences 189 (2018): 03018. http://dx.doi.org/10.1051/matecconf/201818903018.

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Content-centric Networking (CCN) is one of the most promising network architectures for the future Internet. In-network caching is an attractive feature of CCN, however, the existing research does not consider the off-path nodes, or gives a large communication overhead for cooperation, which makes the caching utilization lower, and hard to achieve comprehensive performance optimization. To reduce the data redundancy and improve the caching utilization, we propose a Regional Hashing Collaborative Caching Strategy (RHCCS). According to calculate the importance of nodes in the network topology, we divide the network into the core area and edge area. In core area, we select the relevant nodes for cooperation, store the block in the off-path nodes with the hashing algorithm, and add a new table in original data structures for routing in the collaborative areas. As for edge area, we deploy the on-path reversion scheme. By simulating in ndnSIM and comparing with the basic caching strategy in CCN, experimental results indicate that the RHCCS can effectively reduce data redundancy, routing hops, requesting delay, and significantly increase the hit rate.

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22

Harmatos, János, and Markosz Maliosz. "Architecture Integration of 5G Networks and Time-Sensitive Networking with Edge Computing for Smart Manufacturing." Electronics 10, no. 24 (December 11, 2021): 3085. http://dx.doi.org/10.3390/electronics10243085.

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Digitalization and networking are taking on an increasingly important role in manufacturing. Fifth Generation mobile networks (5G) allow us to wirelessly connect multiple assets in factories with guaranteed quality of service (QoS). A 5G non-public network (5G-NPN) realizes a dedicated network with secure communication within the factory. Time-sensitive networking (TSN) provides deterministic connectivity and reliability in local networks. Edge computing moves computing power near factory locations, reducing the latency of edge applications. Making production processes more flexible, more robust, and resilient induces a great challenge for integrating these technologies. This paper presents the benefits of the joint use of 5G-NPN, TSN, and edge computing in manufacturing. To that end, first, the characteristics of the technologies are analyzed. Then, the integration of different 5G-NPN deployment options with edge (and cloud) computing is presented to provide end-to-end services. For enhanced reliability, ways of interworking between TSN and edge computing domains are proposed. Afterward, as an example realization of edge computing, the investigation on the capabilities of the Kubernetes container orchestration platform is presented together with the gap analysis for smart manufacturing requirements. Finally, the different integration options, interworking models, and Kubernetes-based edge computing are evaluated to assist smart factories to use these new technologies in combination in the future.

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23

Chen, Junhua, Chenggen Pu, Ping Wang, Xueda Huang, Yan Zhang, and Yanfei Liu. "Multitask Offloading Strategy for Edge Computing in Time-Sensitive Networking." Mobile Information Systems 2022 (December 30, 2022): 1–17. http://dx.doi.org/10.1155/2022/1750192.

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Edge computing is a creative computing paradigm that enhances the computing capacity of the edge device close to the data source. As the key technology of edge computing, task offloading, which can improve the response speed and the stability of the network system, has attracted much attention and has been applied in many network scenarios. However, few studies have considered the application of task offloading in time-sensitive networking (TSN), which is a promising technology that has the potential to guarantee data delivery with bounded latency and low jitter. To this end, we establish a task offloading stream transmission model for TSN based on the queueing theory. With the model, the average response time can be achieved by quantitative calculation. Then, we introduce the backward method to construct a utility function and formulate an exact potential game to model the task offloading competition among edge devices considering the minimization of the average response time of all tasks. Furthermore, a distributed and sequential decision-making algorithm for multitask offloading (DSDA-MO) is proposed to find the Nash equilibrium. Through numerical studies, we evaluate the algorithm performance as well as the benefit of the multitask offloading mechanism. The results reveal that through the proposed game theoretic approach, we can obtain the optimal multitask offloading strategy, which can significantly reduce the task computation delay in TSN, within a finite number of rounds of calculation.

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24

Hu, Shaoliu, Yueping Cai, Shengkai Wang, and Xiao Han. "Enhanced FRER Mechanism in Time-Sensitive Networking for Reliable Edge Computing." Sensors 24, no. 6 (March 7, 2024): 1738. http://dx.doi.org/10.3390/s24061738.

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Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9.

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25

Phelps, Nick, Donald M. McNeill, and N. Parsons. "IN SEARCH OF A EUROPEAN EDGE URBAN IDENTITY: TRANS-EUROPEAN NETWORKING AMONG EDGE URBAN MUNICIPALITIES." European Urban and Regional Studies 9, no. 3 (July 1, 2002): 211–24. http://dx.doi.org/10.1177/0967642002009003034.

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26

Zeydan, Engin, Ejder Bastug, Mehdi Bennis, Manhal Abdel Kader, Ilyas Alper Karatepe, Ahmet Salih Er, and Merouane Debbah. "Big data caching for networking: moving from cloud to edge." IEEE Communications Magazine 54, no. 9 (September 2016): 36–42. http://dx.doi.org/10.1109/mcom.2016.7565185.

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27

Wang, An, Zili Zha, Yang Guo, and Songqing Chen. "Software-Defined Networking Enhanced Edge Computing: A Network-Centric Survey." Proceedings of the IEEE 107, no. 8 (August 2019): 1500–1519. http://dx.doi.org/10.1109/jproc.2019.2924377.

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28

Zhao, Yongli, Wei Wang, Yajie Li, Carlos Colman Meixner, Massimo Tornatore, and Jie Zhang. "Edge Computing and Networking: A Survey on Infrastructures and Applications." IEEE Access 7 (2019): 101213–30. http://dx.doi.org/10.1109/access.2019.2927538.

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29

Gilchrist, A. "The well-connected community: networking to the edge of chaos." Community Development Journal 35, no. 3 (July 1, 2000): 264–75. http://dx.doi.org/10.1093/cdj/35.3.264.

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30

Ahmadi, Kaveh, S. Pourya Miralavy, and Mona Ghassemian. "Software-defined networking to improve handover in mobile edge networks." International Journal of Communication Systems 33, no. 14 (June 29, 2020): e4510. http://dx.doi.org/10.1002/dac.4510.

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31

Chen, Sian, and Linqiang Tang. "Flexible English Learning Platform using Collaborative Cloud-Fog-Edge Networking." Scalable Computing: Practice and Experience 24, no. 3 (September 10, 2023): 339–54. http://dx.doi.org/10.12694/scpe.v24i3.2224.

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In the modern age, developing practical online learning tools for English language learners is challenging due to existing systems’ shortcomings. These systems often need proper instructional design, are well-connected to motivational theories, and have limited infrastructure for data sharing, leading to poor learning outcomes and low motivation. To tackle these issues, a new approach called OAELT has been proposed in this paper. OAELT is an Online Assisted English Learning Tool that uses the Fuzzy Analytical Hierarchy Process (FAHP) and collaborative cloud-fog-edge networking to create a flexible learning design that adapts to the needs and preferences of individual learners. Using the FAHP approach, OAELT provides an improved learning experience by tailoring its design to each learner’s unique needs. The collaborative cloud-fog-edge networking approach uses each computing layer’s strengths to deliver a personalized and seamless learning experience. OAELT employs adaptive and dynamic approaches within a flexible instructional paradigm to ensure effective instructional design. This paradigm facilitates collective learning data exchange across cloud, fog, and edge computing layers. The effectiveness of OAELT was evaluated using a descriptive statistics approach, which included a five-dimension questionnaire for students covering cognition, emotion, action, cooperation, and literacy. The results demonstrated that OAELT could enhance learning effectiveness and motivation while providing a flexible and seamless learning experience. According to the experimental data of the proposed model, 46.8% of learners often read English magazines and newspapers to improve their flexibility in English learning. Additionally, 50.4% classified and memorized English according to their categories, while 59% of learners often used context to memorize. These findings suggest that the traditional methods for flexible English learning are not adequate, and the average score of the student’s methods and strategies is mediocre. However, after using OAELT, some students have been able to use different learning curricular reading. Overall, OAELT’s integration of cloud-fog-edge computing with a flexible English learning design can create a more effective and personalized learning system that addresses the challenges of modern learning.

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32

Goshi, Nosheen, Sohail Zafar, Tabasam Rashid, and Juan L. G. Guirao. "A Combinatorial Approach to the Computation of the Fractional Edge Dimension of Graphs." Mathematics 9, no. 19 (September 23, 2021): 2364. http://dx.doi.org/10.3390/math9192364.

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E. Yi recently introduced the fractional edge dimension of graphs. It has many applications in different areas of computer science such as in sensor networking, intelligent systems, optimization, and robot navigation. In this paper, the fractional edge dimension of vertex and edge transitive graphs is calculated. The class of hypercube graph Qn with an odd number of vertices n is discussed. We propose the combinatorial criterion for the calculation of the fractional edge dimension of a graph, and hence applied it to calculate the fractional edge dimension of the friendship graph Fk and the class of circulant graph Cn(1,2).

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33

George, Sherron Kay. "Local-Global Mission: The Cutting Edge." Missiology: An International Review 28, no. 2 (April 2000): 187–97. http://dx.doi.org/10.1177/009182960002800204.

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In the search for new paradigms and models of mission, one cutting edge in the 1990s is local-global mission with emphasis placed on the hyphen. The primary mission agents today are the local congregation and the global church. Not only are the local-global players significant, but also the networking that goes on between them and the diverse challenges and barriers that we encounter in our community and world. It is essential to move beyond dichotomies and isolation to develop a symbiotic relationship between local and global and between sending and receiving.

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34

Leivadeas, Aris, George Kesidis, Mohamed Ibnkahla, and Ioannis Lambadaris. "VNF Placement Optimization at the Edge and Cloud †." Future Internet 11, no. 3 (March 9, 2019): 69. http://dx.doi.org/10.3390/fi11030069.

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Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers’ goal of low deployment costs.

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Bokhary, Syed Ahtsham Ul Haq, Khola Wahid, Usman Ali, Shreefa O. Hilali, Mohammed Alhagyan, and Ameni Gargouri. "Resolvability in Subdivision Graph of Circulant Graphs." Symmetry 15, no. 4 (April 5, 2023): 867. http://dx.doi.org/10.3390/sym15040867.

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Circulant networks are a very important and widely studied class of graphs due to their interesting and diverse applications in networking, facility location problems, and their symmetric properties. The structure of the graph ensures that it is symmetric about any line that cuts the graph into two equal parts. Due to this symmetric behavior, the resolvability of these graph becomes interning. Subdividing an edge means inserting a new vertex on the edge that divides it into two edges. The subdivision graph G is a graph formed by a series of edge subdivisions. In a graph, a resolving set is a set that uniquely identifies each vertex of the graph by its distance from the other vertices. A metric basis is a resolving set of minimum cardinality, and the number of elements in the metric basis is referred to as the metric dimension. This paper determines the minimum resolving set for the graphs Hl[1,k] constructed from the circulant graph Cl[1,k] by subdividing its edges. We also proved that, for k=2,3, this graph class has a constant metric dimension.

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Pan, Wenwen, Jianzhi Wang, and Jingsheng Ji. "Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking." Mobile Information Systems 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5596291.

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The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

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., Naveen, Abhishek Sharma, and Neha Ahlawat. "SD-WAN: The Future of Networking." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 328–31. http://dx.doi.org/10.22214/ijraset.2023.51475.

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Abstract: Software-defined wide area network (SD-WAN) is a networking technology that enables organizations to build secure, high-performance WANs (Wide Area Networks) using software-based controls that provide simplified network management, help businesses offer seamless connectivity to their users in this current world trend of cloud technology and Edge computing. It operates in a centralized manner, allowing for the control of the entire network at once surpassing the limitations of traditional WANs. This paper discusses the SD-WAN technology, including why it was created, its benefits, the components that make up the technology, the challenges associated with its adoption, the security features it offers, and the future of SD-WAN in networking.

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Antonio Santos da Silva, Paulo Mendes, Denis Rosario, Eduardo Cerqueira, and Edison Pignaton de Freitas. "Airborne fog computing framework: instant primers and design choices." ITU Journal on Future and Evolving Technologies 5, no. 3 (September 2, 2024): 368–82. http://dx.doi.org/10.52953/unzo8724.

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This article focuses on the challenge of providing services and applications by a swarm of mobile flying devices, which may belong to the same tenant or not. Starting by introducing the concepts of mobile edge computing and fog computing, this work shows how the latter can be leveraged to create a novel framework of airborne fog computing. This article defines the applicability, design principles, and challenges inherent to such a framework, starting by showing how the technical challenges can be tackled based on the combination of three different networking paradigms: opportunistic networking, information-centric networking, and software-defined networking. This article shows how these three networking paradigms can be combined to define an airborne fog computing framework based on the notion of in-network computing dynamically programming the data plane to support the execution of services and the integration of the paradigms, in which services are defined as chains of computational functions that are dynamically installed and executed in different flying devices.

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Paterson, Nancy E. "End User Privacy and Policy-Based Networking." Journal of Information Policy 4, no. 1 (January 1, 2014): 28–43. http://dx.doi.org/10.5325/jinfopoli.4.2014.28.

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Abstract Are privacy concerns over end user surveillance by network operators such as AT&T and Verizon, and by “edge providers” such as Google and Amazon, being dangerously overshadowed by the predominant emphasis on government surveillance? Quite possibly so, Professor Paterson concludes, after reviewing currently-used packet analysis technologies and their advertised capabilities. Paterson concludes that the problem faced by privacy and public interest advocates is that no one has a truly comprehensive grasp of how widely network operators have deployed such tools, what information is collected, or how it is put to use. Paterson asserts that the potential risks call for additional vigorous research in this area.

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Xue, Hai, Kyung Tae Kim, and Hee Yong Youn. "Packet Scheduling for Multiple-Switch Software-Defined Networking in Edge Computing Environment." Wireless Communications and Mobile Computing 2018 (November 18, 2018): 1–11. http://dx.doi.org/10.1155/2018/7659085.

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Software-defined networking (SDN) decouples the control plane and data forwarding plane to overcome the limitations of traditional networking infrastructure. Among several communication protocols employed for SDN, OpenFlow is most widely used for the communication between the controller and switch. In this paper two packet scheduling schemes, FCFS-Pushout (FCFS-PO) and FCFS-Pushout-Priority (FCFS-PO-P), are proposed to effectively handle the overload issue of multiple-switch SDN targeting the edge computing environment. Analytical models on their operations are developed, and extensive experiment based on a testbed is carried out to evaluate the schemes. They reveal that both of them are better than the typical FCFS-Block (FCFS-BL) scheduling algorithm in terms of packet wait time. Furthermore, FCFS-PO-P is found to be more effective than FCFS-PO in the edge computing environment.

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Chen, Ning, Tie Qiu, Laiping Zhao, Xiaobo Zhou, and Huansheng Ning. "Edge Intelligent Networking Optimization for Internet of Things in Smart City." IEEE Wireless Communications 28, no. 2 (April 2021): 26–31. http://dx.doi.org/10.1109/mwc.001.2000243.

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Duan, Qiang, Shangguang Wang, and Nirwan Ansari. "Convergence of Networking and Cloud/Edge Computing: Status, Challenges, and Opportunities." IEEE Network 34, no. 6 (November 2020): 148–55. http://dx.doi.org/10.1109/mnet.011.2000089.

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Amin, Rahul, David Ripplinger, Devanshu Mehta, and Bow-Nan Cheng. "Design considerations in applying disruption tolerant networking to tactical edge networks." IEEE Communications Magazine 53, no. 10 (October 2015): 32–38. http://dx.doi.org/10.1109/mcom.2015.7295460.

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Cheng, Bow-Nan, Greg Kuperman, Patricia Deutsch, Logan Mercer, and Aradhana Narula-Tam. "Group-centric networking: addressing information sharing requirements at the tactical edge." IEEE Communications Magazine 54, no. 10 (October 2016): 145–51. http://dx.doi.org/10.1109/mcom.2016.7588284.

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45

Amadeo, Marica, Claudia Campolo, and Antonella Molinaro. "NDNe: Enhancing Named Data Networking to Support Cloudification at the Edge." IEEE Communications Letters 20, no. 11 (November 2016): 2264–67. http://dx.doi.org/10.1109/lcomm.2016.2597850.

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46

Lim, Sung-Hwa, Young-Bae Ko, Gue-Hwan Jung, Jaehoon Kim, and Myeong-Wuk Jang. "Inter-Chunk Popularity-Based Edge-First Caching in Content-Centric Networking." IEEE Communications Letters 18, no. 8 (August 2014): 1331–34. http://dx.doi.org/10.1109/lcomm.2014.2329482.

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Li, Hui, Lishuang Pei, Dan Liao, Ming Zhang, Du Xu, and Xiong Wang. "Achieving privacy protection for crowdsourcing application in edge-assistant vehicular networking." Telecommunication Systems 75, no. 1 (May 8, 2020): 1–14. http://dx.doi.org/10.1007/s11235-020-00666-w.

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Jin, Yong, Hang Shen, Daniele D’Agostino, Nadjib Achir, and James Nightingale. "Guest editorials: Special issue on fog/edge networking for multimedia applications." Peer-to-Peer Networking and Applications 12, no. 6 (September 9, 2019): 1477–79. http://dx.doi.org/10.1007/s12083-019-00812-5.

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Saleem, Munqith, Hanan Burhan Saadon, Marwa S. Mahdi Hussin, Tamarah Alaa Diame, and Raaid Alubady. "Application of Edge Computing-Based Information-Centric Networking in Smart Cities." Journal of Intelligent Systems and Internet of Things 8, no. 2 (2023): 72–85. http://dx.doi.org/10.54216/jisiot.080208.

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Many data resources and network availability are needed for the smart city applications to execute at their highest efficiency level. Demand for these objects is driving up data traffic, which in turn is placing strain on the network. The 5G-enabled Internet of Things applications address these difficulties in smart city applications. This article proposes an Information-centric Networking System using Multiaccess Edge Computing (ICNMEC) to reduce computation offloading and optimize data traffic. This system's 5G network slicing approaches combine edge computing and software characterization. Internet of Things applications have been used to store and analyze the information gathered. In addition, an algorithm known as OMNM (Optimized Memory Network Management) is created to control network traffic better and better use of storage. With minimal delays, network traffic, and storage ratio, the system's modeling tests demonstrate that it is very efficient. This method can progressively enhance the pace at which one can access and use the system. The performance assessment shows that the proposed method can improve the efficiency ratio of 95.141%, storage utilization ratio of 60.1%, and access rate by 0.9, reducing network traffic and delay by 0.6.

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Subratie, Kensworth, and Renato Figueiredo. "EdgeVPN.io: Seamless Software-defined Layer 2 Virtual Networking for Edge Computing." Journal of Open Source Software 9, no. 100 (August 26, 2024): 6638. http://dx.doi.org/10.21105/joss.06638.

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