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Edge/Fog Computing for Intelligent IoT Applications

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

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 17025

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Special Issue Editors

Prof. Dr. Weizhe Zhang

E-Mail Website
Guest Editor

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: parallel computing; distributed computing; cloud and grid computing; and computer networks
Special Issues, Collections and Topics in MDPI journals

Dr. Ibrahim A. Elgendy

E-Mail Website
Guest Editor

Department of Computer Science, Faculty of Computers and Information, Menouﬁa University, Menofia Governorate, Egypt
Interests: Cloud Computing; Edge/Fog Computing; Internet of Things; Security and Distributed Computing

Special Issue Information

Dear Colleagues,

Recently, edge/fog computing architecture has emerged as a new paradigm to extend the cloud infrastructure in which the computational nodes of cloud computing are pushed at the edge of the radio access network, resulting in cost-efﬁcient and low latency. Moreover, edge/fog computing enables intelligent IoT (IIoT) devices and applications to improve energy efficiency, quality of service, quality of experience, system scalability, alleviate the transport network’s burdens and traffic, and exploit computational node resources to analyze the collected data with low latency. However, many challenges and open issues still need to be addressed to improve and fully utilize the architecture of edge/fog computing for IIoT applications, such as innovative and efficient schemes for mobility management, smart homes/hospitals/cities, intelligent vehicles/unmanned vehicles, effective optimization architecture for managing the computing and storage resources, intelligent network management services, privacy and security concerns, balancing the load among edge/fog servers, and a variety of other AI-led computing environments. Moreover, cloud computing and edge/fog computing cooperation need to be further studied to provide scalable services.

This Special Issue invites researcher in both academia and industry researchers to explore and discuss vital problems as well as propose innovative and viable solutions in the area of edge/fog computing for intelligent IoT applications.

Potential topics include but are not limited to the following:

Theoretical foundation and models for edge/fog-based IIoT.
IIoT management and networking services.
Communication and network architecture and protocols for edge/fog-based IIoT.
Energy-aware and resource allocation solutions in edge/fog for IIoT.
Quality of service/quality of experience for edge/fog computing.
Privacy and security issues in edge/fog architecture.
Mobility management in edge/fog for IIoT.
Cooperation of edge/fog computing and cloud computing for IIoT.
Load balancing and scheduling in edge/fog nodes for IIoT.

Prof. Dr. Weizhe Zhang
Dr. Ibrahim A. Elgendy
Guest Editors

Manuscript Submission Information

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Keywords

edge computing
fog computing
privacy and security
intelligent Internet of Things
resource allocation
energy-efficient
mobility management
quality of service/quality of experience

Published Papers (7 papers)

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Research

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20 pages, 3089 KiB

Open AccessArticle

Elastic Provisioning of Network and Computing Resources at the Edge for IoT Services

by Patrícia Cardoso, José Moura and Rui Neto Marinheiro

Sensors 2023, 23(5), 2762; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052762 - 02 Mar 2023

Cited by 1 | Viewed by 1236

Abstract

The fast growth of Internet-connected embedded devices demands new system capabilities at the network edge, such as provisioning local data services on both limited network and computational resources. The current contribution addresses the previous problem by enhancing the usage of scarce edge resources. It designs, deploys, and tests a new solution that incorporates the positive functional advantages offered by software-defined networking (SDN), network function virtualization (NFV), and fog computing (FC). Our proposal autonomously activates or deactivates embedded virtualized resources, in response to clients’ requests for edge services. Complementing existing literature, the obtained results from extensive tests on our programmable proposal show the superior performance of the proposed elastic edge resource provisioning algorithm, which also assumes an SDN controller with proactive OpenFlow behavior. According to our results, the maximum flow rate for the proactive controller is 15% higher; the maximum delay is 83% smaller; and the loss is 20% smaller compared to when the non-proactive controller is in operation. This improvement in flow quality is complemented by a reduction in control channel workload. The controller also records the time duration of each edge service session, which can enable the accounting of used resources per session. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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18 pages, 598 KiB

Open AccessArticle

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

by Kheireddine Choutri, Mohand Lagha, Souham Meshoul and Samiha Fadloun

Sensors 2022, 22(19), 7243; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197243 - 24 Sep 2022

Cited by 2 | Viewed by 1313

Abstract

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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21 pages, 2500 KiB

Open AccessArticle

Intelligent Task Dispatching and Scheduling Using a Deep Q-Network in a Cluster Edge Computing System

by Joosang Youn and Youn-Hee Han

Sensors 2022, 22(11), 4098; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114098 - 28 May 2022

Cited by 7 | Viewed by 1757

Abstract

Recently, intelligent IoT applications based on artificial intelligence (AI) have been deployed with mobile edge computing (MEC). Intelligent IoT applications demand more computing resources and lower service latencies for AI tasks in dynamic MEC environments. Thus, in this paper, considering the resource scalability and resource optimization of edge computing, an intelligent task dispatching model using a deep Q-network, which can efficiently use the computing resource of edge nodes is proposed to maximize the computation ability of the cluster edge system, which consists of multiple edge nodes. The cluster edge system can be implemented with the Kubernetes technology. The objective of the proposed model is to minimize the average response time of tasks offloaded to the edge computing system and optimize the resource allocation for computing the offloaded tasks. For this, we first formulate the optimization problem of resource allocation as a Markov decision process (MDP) and adopt a deep reinforcement learning technology to solve this problem. Thus, the proposed intelligent task dispatching model is designed based on a deep Q-network (DQN) algorithm to update the task dispatching policy. The simulation results show that the proposed model archives a better convergence performanc in terms of the average completion time of all offloaded tasks, than existing task dispatching methods, such as the Random Method, Least Load Method and Round-Robin Method, and has a better task completion rate than the existing task dispatching method when using the same resources as the cluster edge system. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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21 pages, 2602 KiB

Open AccessArticle

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

by Ioannis Tzanettis, Christina-Maria Androna, Anastasios Zafeiropoulos, Eleni Fotopoulou and Symeon Papavassiliou

Sensors 2022, 22(5), 2061; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052061 - 07 Mar 2022

Cited by 6 | Viewed by 2867

Abstract

Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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21 pages, 1174 KiB

Open AccessArticle

Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications

by Adrián Orive, Aitor Agirre, Hong-Linh Truong, Isabel Sarachaga and Marga Marcos

Sensors 2022, 22(5), 1755; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051755 - 23 Feb 2022

Cited by 13 | Viewed by 2518

Abstract

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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Review

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27 pages, 2109 KiB

Open AccessReview

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

by Raúl S-Julián, Ignacio Lacalle, Rafael Vaño, Fernando Boronat and Carlos E. Palau

Sensors 2023, 23(6), 2931; https://0-doi-org.brum.beds.ac.uk/10.3390/s23062931 - 08 Mar 2023

Cited by 1 | Viewed by 1813

Abstract

Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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31 pages, 1061 KiB

Open AccessReview

An Overview of Fog Data Analytics for IoT Applications

by Jitendra Bhatia, Kiran Italiya, Kuldeepsinh Jadeja, Malaram Kumhar, Uttam Chauhan, Sudeep Tanwar, Madhuri Bhavsar, Ravi Sharma, Daniela Lucia Manea, Marina Verdes and Maria Simona Raboaca

Sensors 2023, 23(1), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010199 - 24 Dec 2022

Cited by 15 | Viewed by 3900

Abstract

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends. Full article

(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)

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