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Cloud/Edge/Fog Computing for Network and IoT

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 20734

Special Issue Editor

School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: Internet of Things; edge computing; trust computing; services computing; deep reinforcement learning; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the Internet of Things (IoT), cloud computing, edge computing, and fog computing are three widely used computing models. Cloud computing distributes computing tasks on a resource pool composed of many computers, enabling various systems to obtain computing power, storage, and information services as needed, thereby realizing the provision of scalable and inexpensive distributed computing capabilities. Cloud computing represents a dynamically scalable network application infrastructure with virtualization technology as the core and low cost as the goal. It is the most representative network computing technology and model to have emerged in recent years.

As a model that complements cloud computing, edge computing has a different way of processing computing tasks. Instead of offloading tasks to the cloud, it decomposes the computing tasks generated by mobile devices, cuts them into smaller and easier-to-manage parts, and distributes them to edge nodes for processing, thereby satisfying the low-latency requirements of some computing-intensive applications.

Fog computing is a distributed computing model that serves as an intermediate layer between cloud data centers and IoT devices and sensors. It provides computing, network, and storage devices, so that cloud-based services can be closer to IoT devices and sensors. The introduction of the concept of fog computing is also to cope with the challenges faced by traditional cloud computing in IoT applications.

This Special Issue aims to study and discuss the latest developments of these three calculation modes. Topics include but are not limited to:

  • Reliability issues in cloud computing;
  • Resource management and task scheduling of edge computing;
  • Intelligent edge computing enhanced by machine learning/deep learning;
  • AI-inspired task offloading algorithms, protocols, and mechanisms for IoT devices;
  • Security and privacy issues with solutions for edge/cloud networks;
  • Cross-computing technologies for edge–cloud;
  • Decentralized or collaborative edge/fog/cloud for future communications and networks;
  • Energy consumption model in fog computing;
  • Novel theories, concepts, and paradigms for edge/fog/cloud computing;
  • Unmannered aerial vehicle systems for edge computing;
  • Trust data collection and computing for distributed IoT;
  • Implementation/testbed/deployment of edge/fog/cloud computing.

Prof. Dr. Anfeng Liu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • cloud computing
  • edge computing
  • fog computing
  • machine learning
  • task offload

Published Papers (9 papers)

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Research

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24 pages, 1131 KiB  
Article
Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
by Roman Glazkov, Dmitri Moltchanov, Srikathyayani Srikanteswara, Andrey Samuylov, Gabriel Arrobo, Yi Zhang, Hao Feng, Nageen Himayat, Marcin Spoczynski and Yevgeni Koucheryavy
Sensors 2024, 24(4), 1120; https://doi.org/10.3390/s24041120 - 08 Feb 2024
Viewed by 461
Abstract
Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As [...] Read more.
Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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16 pages, 665 KiB  
Article
Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks
by Long Suo, Han Ma, Wanguo Jiao and Xiaoming Liu
Sensors 2024, 24(1), 216; https://doi.org/10.3390/s24010216 - 30 Dec 2023
Viewed by 589
Abstract
Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT [...] Read more.
Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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22 pages, 2161 KiB  
Article
Vehicle Trajectory Prediction Method for Task Offloading in Vehicular Edge Computing
by Ruibin Yan, Yijun Gu, Zeyu Zhang and Shouzhong Jiao
Sensors 2023, 23(18), 7954; https://doi.org/10.3390/s23187954 - 18 Sep 2023
Cited by 1 | Viewed by 850
Abstract
Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory [...] Read more.
Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory data. Meanwhile, machine learning is difficult to deploy on edge servers. In this paper, we propose a vehicle trajectory prediction method based on the vehicle frequent pattern for task offloading in VEC. First, in the initialization stage, a T-pattern prediction tree (TPPT) is constructed based on the historical vehicle trajectory data. Secondly, when predicting the vehicle trajectory, the vehicle frequent itemset with the largest vehicle trajectory support is found in the vehicle frequent itemset of the TPPT. Finally, in the update stage, the TPPT is updated in real time with the predicted vehicle trajectory results. Meanwhile, based on the proposed prediction method, the strategies of task offloading and optimization algorithm are designed to minimize energy consumption with time constraints. The experiments are carried out on real-vehicle datasets and the Capital Bikeshare datasets. The results show that compared with the baseline T-pattern method, the accuracy of the prediction method is improved by more than 10% and the prediction efficiency is improved by more than 6.5 times. The vehicle trajectory prediction method based on the vehicle frequent pattern has high accuracy and prediction efficiency, which can solve the problem of vehicle trajectory prediction for task offloading. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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14 pages, 1250 KiB  
Article
JUTAR: Joint User-Association, Task-Partition, and Resource-Allocation Algorithm for MEC Networks
by Ling Kang, Yi Wang, Yanjun Hu, Fang Jiang, Na Bai and Yu Deng
Sensors 2023, 23(3), 1601; https://doi.org/10.3390/s23031601 - 01 Feb 2023
Viewed by 1403
Abstract
Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency [...] Read more.
Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21% system performance gain in the MEC network with 100 pieces of UE. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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23 pages, 1753 KiB  
Article
Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing
by Tahira Sarwar Mir, Hannan Bin Liaqat, Tayybah Kiren, Muhammad Usman Sana, Roberto Marcelo Alvarez, Yini Miró, Alina Eugenia Pascual Barrera and Imran Ashraf
Sensors 2022, 22(22), 8778; https://doi.org/10.3390/s22228778 - 14 Nov 2022
Viewed by 1203
Abstract
The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still [...] Read more.
The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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20 pages, 2540 KiB  
Article
Deployment and Allocation Strategy for MEC Nodes in Complex Multi-Terminal Scenarios
by Danyang Li, Yuxing Mao, Xueshuo Chen, Jian Li and Siyang Liu
Sensors 2022, 22(18), 6719; https://doi.org/10.3390/s22186719 - 06 Sep 2022
Cited by 3 | Viewed by 1489
Abstract
Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an [...] Read more.
Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an important impact on system performance and has become an essential issue in end–edge–cloud architecture. In this article, we consider specific factors, such as spatial location, power supply, and urgency requirements of terminals, with respect to building an evaluation model to solve the allocation problem. An evaluation model based on reward, energy consumption, and cost factors is proposed. The genetic algorithm is applied to determine the optimal edge node deployment and allocation strategies. Moreover, we compare the proposed method with the k-means and ant colony algorithms. The results show that the obtained strategies achieve good evaluation results under problem constraints. Furthermore, we conduct comparison tests with different attributes to further test the performance of the proposed method. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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26 pages, 6924 KiB  
Article
InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback
by Muhammad Munir Ud Din, Nasser Alshammari, Saad Awadh Alanazi, Fahad Ahmad, Shahid Naseem, Muhammad Saleem Khan and Hafiz Syed Imran Haider
Sensors 2022, 22(12), 4627; https://doi.org/10.3390/s22124627 - 19 Jun 2022
Cited by 4 | Viewed by 1987
Abstract
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the [...] Read more.
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR). Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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Review

Jump to: Research

27 pages, 2886 KiB  
Review
Cloud-Native Workload Orchestration at the Edge: A Deployment Review and Future Directions
by Rafael Vaño, Ignacio Lacalle, Piotr Sowiński, Raúl S-Julián and Carlos E. Palau
Sensors 2023, 23(4), 2215; https://doi.org/10.3390/s23042215 - 16 Feb 2023
Cited by 9 | Viewed by 5497
Abstract
Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods [...] Read more.
Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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35 pages, 2421 KiB  
Review
Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review
by Gleiston Guerrero-Ulloa, Carlos Rodríguez-Domínguez and Miguel J. Hornos
Sensors 2023, 23(2), 790; https://doi.org/10.3390/s23020790 - 10 Jan 2023
Cited by 5 | Viewed by 5918
Abstract
Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, [...] Read more.
Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose. Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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