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Edge Intelligence: Challenges and Opportunities
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Edge Intelligence: Challenges and Opportunities

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4198

Special Issue Editors

Dr. Bo Yi

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: SDN/NFV; intelligent routing; service computing; roboting; edge computing
Prof. Dr. Min Huang

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: logistics and supply chain system; manufacturing and service system modeling and optimization; data parsing and machine learning; computational intelligence
Prof. Dr. Xingwei Wang

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: future internet; cloud computing

Special Issue Information

Dear Colleagues,

Mobile edge computing (MEC) enables us to seamlessly and quickly access many kinds of novel services that appeared in the era of B5G/6G, for example, super-high-definition video on demand (e.g., 4k and 8k), ultra-low-delay live streaming (e.g., Internet live sport networks), and real-time immersive and interactive streaming (e.g., online cloud games, video conference, e-education), by pushing the content to servers closer to users at the edge. As such, MEC technology has rapidly gained a crucial role and become a popular topic in networking. As suggested by mobile network operators, the MEC and B5G/6G have greatly promoted the use of mobile devices, which then generate an ever-growing datastream in network. According to the statistics, the number of the mobile devices exceeded 50 billion in 2021, which poses a large amount of traffic demand for ubiquitous mobile network communications. It is also anticipated that we will witness an up to 10,000-fold growth in wireless data traffic by the year 2030. There mobile devices will request seamless connectivity and access to the demand contents with mobility supported. One of the most attractive solutions is the application of AI that helps to predict the trends of customer preferences based on big data, and then the predicted results can be used to address the challenges caused by such a large number of mobile devices and high amount of data traffic. Specifically, 1) the mature AI models can be well trained using such a tremendous amount of data traffic and deployed at the wireless edge network closer to users to increase the response time; 2) the well-trained AI models can be used to ease the wireless access network bottleneck by discovering the regularity of mobile user demands and avoid peak times; 3) distributing AI models in both cloud and edge wireless networks and establishing the collaboration between the cloud AI and edge AI can provide a smooth and seamless network environment. Therefore, this Special Issue will focus on AI for MEC modelling, analysis and design. This Special Issue calls for high-quality submissions so that the theoretical and practical frontiers can be moved forward for a deeper understanding from both academic and industrial viewpoints. The original papers are solicited on topics of interest that include but are not limited to the following:

  • AI for new mobile network architectures, protocols, resource allocation, synchronization, signaling, optimization in MEC
  • AI for mobile backhaul traffic management including the latency, bandwidth, jitter, power consumption, and routing optimization in MEC
  • AI for the mobility, handoff and interference management and control in MEC
  • AI for new wireless applications and technologies (e.g., smart city, drone control) in MEC
  • AI for load-balancing schemes and energy-saving techniques in MEC
  • AI for content caching, deployment, update, push, recommendation in MEC
  • AI for wireless network measurements, implementations, and demos in MEC
  • AI for intrusion and threat detection in MEC
  • AI for wireless sensing and data processing in MEC
  • AI-based open standard and application programming interface in MEC

Dr. Bo Yi
Prof. Dr. Min Huang
Prof. Dr. Xingwei Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • mobile edge computing
  • AI
  • model
  • wireless network
  • distributed network

Published Papers (3 papers)

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Research

20 pages, 4391 KiB  
Article
Edge IoT Prototyping Using Model-Driven Representations: A Use Case for Smart Agriculture
by Ivan Guevara, Stephen Ryan, Amandeep Singh, Colm Brandon and Tiziana Margaria
Sensors 2024, 24(2), 495; https://doi.org/10.3390/s24020495 - 12 Jan 2024
Viewed by 1024
Abstract
Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems [...] Read more.
Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem. Full article
(This article belongs to the Special Issue Edge Intelligence: Challenges and Opportunities)
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28 pages, 1679 KiB  
Article
Enhancing Resource Sharing and Access Control for VNF Instantiation with Blockchain
by Anwei Dong, Xingwei Wang, Bo Yi, Qiang He and Min Huang
Sensors 2023, 23(23), 9343; https://doi.org/10.3390/s23239343 - 23 Nov 2023
Viewed by 883
Abstract
In the realm of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) are crucial software entities that require execution on virtualized hardware infrastructure. Deploying a Service Function Chain (SFC) requires multiple steps for instantiating VNFs to analyze, request, deploy, and monitor resources. It [...] Read more.
In the realm of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) are crucial software entities that require execution on virtualized hardware infrastructure. Deploying a Service Function Chain (SFC) requires multiple steps for instantiating VNFs to analyze, request, deploy, and monitor resources. It is well recognized that the sharing of infrastructure resources among different VNFs will enhance resource utilization. However, conventional mechanisms for VNF sharing often neglect the interests of both VNF instances and infrastructure providers. In this context, this paper presents a blockchain-based framework that focuses on resource sharing and access control, with a particular emphasis on ensuring profitability during VNF instantiation. Additionally, a resource sharing game model and a novel greedy matching algorithm are introduced to optimize the benefits for both VNF instances and infrastructure resource providers. Furthermore, a blockchain-based access control mechanism is designed to securely store keys and provide fine-grained access control. The experimental results demonstrate that the proposed resource sharing game model and greedy matching algorithm promote healthy competition among resource owners and facilitate effective bargaining between resource owners and infrastructure providers. In comparison to the standard Stackelberg game solution, our proposed method achieves up to an 8.1 times performance improvement while sacrificing fewer optimal social utility values. Furthermore, compared to other CP-ABE methods, the proposed approach enhances security within a blockchain-based framework while maintaining an excellent encryption efficiency and a moderate decryption efficiency. Full article
(This article belongs to the Special Issue Edge Intelligence: Challenges and Opportunities)
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19 pages, 7571 KiB  
Article
Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection
by Khadija M. Abuali, Liyth Nissirat and Aida Al-Samawi
Sensors 2023, 23(21), 8959; https://doi.org/10.3390/s23218959 - 3 Nov 2023
Cited by 6 | Viewed by 1719
Abstract
With the rapid growth of social media networks and internet accessibility, most businesses are becoming vulnerable to a wide range of threats and attacks. Thus, intrusion detection systems (IDSs) are considered one of the most essential components for securing organizational networks. They are [...] Read more.
With the rapid growth of social media networks and internet accessibility, most businesses are becoming vulnerable to a wide range of threats and attacks. Thus, intrusion detection systems (IDSs) are considered one of the most essential components for securing organizational networks. They are the first line of defense against online threats and are responsible for quickly identifying potential network intrusions. Mainly, IDSs analyze the network traffic to detect any malicious activities in the network. Today, networks are expanding tremendously as the demand for network services is expanding. This expansion leads to diverse data types and complexities in the network, which may limit the applicability of the developed algorithms. Moreover, viruses and malicious attacks are changing in their quantity and quality. Therefore, recently, several security researchers have developed IDSs using several innovative techniques, including artificial intelligence methods. This work aims to propose a support vector machine (SVM)-based deep learning system that will classify the data extracted from servers to determine the intrusion incidents on social media. To implement deep learning-based IDSs for multiclass classification, the CSE-CIC-IDS 2018 dataset has been used for system evaluation. The CSE-CIC-IDS 2018 dataset was subjected to several preprocessing techniques to prepare it for the training phase. The proposed model has been implemented in 100,000 instances of a sample dataset. This study demonstrated that the accuracy, true-positive recall, precision, specificity, false-positive recall, and F-score of the proposed model were 100%, 100%, 100%, 100%, 0%, and 100%, respectively. Full article
(This article belongs to the Special Issue Edge Intelligence: Challenges and Opportunities)
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