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Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 24443

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Dr. Jehad Ali

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Guest Editor

Department of AI Convergence Network, Ajou University, Suwon 16499, Republic of Korea
Interests: software-defined networking; quality of service; WSN; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hsiao-Chun Wu

E-Mail Website
Guest Editor

Division of Electrical and Computer Engineering, School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: graph-based algorithms; topological analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

By using controller intelligence and programmability, software-defined networking (SDN) could dynamically and efficiently manage resources in order to supply a wide range of different services. SDN enables network systems to coordinate and estimate available resources, as well as dynamically adapt to the environment to optimize resource consumption. Artificial intelligence and machine learning are proving to be effective methods of increasing the intelligence of SDN controllers. Techniques such as machine learning and artificial intelligence (AI) are useful in network communication because they allow for rapid adaptability. It is possible to efficiently use this increased intelligence of the SDN controller in the context of the software-defined Internet of Things (SD-IoT). A controller trained with powerful AI and machine learning algorithms could improve the supply of end-to-end (E2E) services, security, and resource management in the SD-IoT environment. This Special Issue looks forward to providing the most up-to-date information on state-of-the-art SDN technologies based on machine learning and AI methods, as well as new research findings involving a broad variety of features within the intelligent SDN technology for SD-IoT.

The Special Issue topics include, but are not limited to:

Load balancing in SD-IoT;
Controller placement in SD-IoT;
Ultra-reliable low-latency-based SD-IoT;
Artificial intelligence-enabled SD-IoT;
Security and reliability in SD-IoT;
Leveraging NFV in SD-IoT;
MEC-based efficient solutions in SD-IoT;
Machine learning-based SD-IoT;
Energy-efficient SD-IoT-based solutions;
Massive machine type communication leveraging 5G in SD-IoT;
Network slicing in SD-IoT leveraging 5G;
Named data leveraging SD-IoT;
DDoS attack detection and prevention mechanisms in SD-IoT;
Reinforcement learning-based SD-IoT.

Dr. Jehad Ali
Prof. Dr. Hsiao-Chun Wu
Guest Editors

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Published Papers (11 papers)

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Research

25 pages, 863 KiB

Open AccessArticle

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

by Marika Kulmar, Ivo Müürsepp and Muhammad Mahtab Alam

Sensors 2023, 23(10), 4613; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104613 - 10 May 2023

Cited by 2 | Viewed by 1139

Abstract

In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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15 pages, 384 KiB

Open AccessArticle

Research on the Total Channel Capacities Pertaining to Two Coverage Layouts for Three-Dimensional, UAV-Assisted Ad Hoc Networks

by Xiao Yan, Shenglong Zhu, Qian Wang and Hsiao-Chun Wu

Sensors 2023, 23(7), 3504; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073504 - 27 Mar 2023

Viewed by 1056

Abstract

Unmanned aerial vehicles (UAVs) employed as airborne base stations (BSs) are considered the essential components in future sixth-generation wireless networks due to their mobility and line-of-sight communication links. For a UAV-assisted ad hoc network, its total channel capacity is greatly influenced by the deployment of UAV-BSs and the corresponding coverage layouts, where square and hexagonal cells are partitioned to divide the zones individual UAVs should serve. In this paper, the total channel capacities of these two kinds of coverage layouts are evaluated using our proposed novel computationally efficient channel capacity estimation scheme. The mean distance (MD) between a UAV-BS in the network and its served users as well as the MD from these users to the neighboring UAV-BSs are incorporated into the estimation of the achievable total channel capacity. We can significantly reduce the computational complexity by using a new polygon division strategy. The simulation results demonstrate that the square cell coverage layout can always lead to a superior channel capacity (with an average increase of

7.67 %

to be precise) to the hexagonal cell coverage layout for UAV-assisted ad hoc networks. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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21 pages, 6675 KiB

Open AccessArticle

Remote Sensing Image Characteristics and Typical Area Analysis of Taiyuan Xishan Ecological Restoration Area

by Wang Tao and Zhang Jin

Sensors 2023, 23(4), 2108; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042108 - 13 Feb 2023

Cited by 2 | Viewed by 1239

Abstract

The Taiyuan Xishan Ecological Restoration Zone is located in the west of Taiyuan City and belongs to the Xishan Coalfield. Due to the resource development activity of coal mining, which is caused by coal gangue accumulation, surface vegetation degradation, bare surfaces, and other phenomena, it is most common in this area. These have an impact on the surface ecology; however, after ecological restoration, the surface ecology has been greatly improved. There are many extraction models of vegetation coverage based on pixel dichotomology combined with multispectral vegetation index, but we believe that the combination of visible light vegetation index to construct models is relatively unexplored. The main problem of how to use the RGB image data in order to quickly and accurately extract vegetation coverage information is still under investigation and needs researchers’ attention. In this paper, through selecting the vegetation coverage as the evaluation index of ecological restoration effect, a new RGB vegetation coverage CIVE calculation model is innovatively proposed to solve the above problem, and on the basis of this model, the vegetation cover change analysis is carried out in the Xishan ecological restoration area of Taiyuan. According to the analysis of vegetation coverage change, relevant paper data, and the characteristics of multiple historical remote sensing images, the ecological restoration area of Taiyuan Xishan is divided into six typical areas. Through empirical evaluation, we summarize and analyze these six typical areas, which can provide typical demonstration roles for other ecological restoration areas. Our findings suggest that the proposed CIVE model realizes the extraction of vegetation cover information and long-term series dynamic monitoring of vegetation coverage. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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14 pages, 2546 KiB

Open AccessArticle

5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

by Ramin Firouzi and Rahim Rahmani

Sensors 2023, 23(1), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010133 - 23 Dec 2022

Cited by 4 | Viewed by 2031

Abstract

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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19 pages, 1561 KiB

Open AccessArticle

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)

by Irfan Ullah Khan, Nida Aslam, Rana AlShedayed, Dina AlFrayan, Rand AlEssa, Noura A. AlShuail and Alhawra Al Safwan

Sensors 2022, 22(23), 9235; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239235 - 27 Nov 2022

Cited by 12 | Viewed by 2426

Abstract

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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15 pages, 2719 KiB

Open AccessArticle

An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things

by Sasikumar Asaithambi, Logesh Ravi, Hossam Kotb, Ahmad H. Milyani, Abdullah Ahmed Azhari, Senthilkumar Nallusamy, Vijayakumar Varadarajan and Subramaniyaswamy Vairavasundaram

Sensors 2022, 22(20), 7917; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207917 - 18 Oct 2022

Cited by 12 | Viewed by 2634

Abstract

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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18 pages, 4171 KiB

Open AccessArticle

An Intelligent Model-Based Effective Approach for Glycemic Control in Type-1 Diabetes

by Ali Khaqan, Ali Nauman, Sana Shuja, Tahir Khurshaid and Ki-Chai Kim

Sensors 2022, 22(20), 7773; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207773 - 13 Oct 2022

Cited by 5 | Viewed by 2097

Abstract

Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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25 pages, 3218 KiB

Open AccessArticle

ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks

by Sagar Ramani and Rutvij H. Jhaveri

Sensors 2022, 22(18), 6958; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186958 - 14 Sep 2022

Cited by 6 | Viewed by 1399

Abstract

Traditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger scale, and the present routing algorithms fail to address this issue. In this paper, we present a real-time delay attack detection and isolation scheme for fault-tolerant software-defined industrial networks. The primary goal of the delay attack is to lower the resilience of our previously proposed scheme, SDN-resilience manager (SDN-RM). The attacker compromises the OpenFlow switch and launches an attack by delaying the link layer discovery protocol (LLDP) packets. As a result, the performance of SDN-RM is degraded and the success rate decreases significantly. In this work, we developed a machine learning (ML)-based attack detection and isolation mechanism, which extends our previous work, SDN-RM. Predicting and labeling malicious switches in an SDN-enabled network is a challenge that can be successfully addressed by integrating ML with network resilience solutions. Therefore, we propose a delay-based attack detection and isolation scheme (DA-DIS), which avoids malicious switches from entering the routes by combining an ML mechanism along with a route-handoff mechanism. DA-DIS increases network resilience by increasing success rate and network throughput. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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47 pages, 594 KiB

Open AccessArticle

Software-Defined Networking: Categories, Analysis, and Future Directions

by Mudassar Hussain, Nadir Shah, Rashid Amin, Sultan S. Alshamrani, Aziz Alotaibi and Syed Mohsan Raza

Sensors 2022, 22(15), 5551; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155551 - 25 Jul 2022

Cited by 20 | Viewed by 3647

Abstract

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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19 pages, 3797 KiB

Open AccessArticle

Satellite Network Task Deployment Method Based on SDN and ICN

by Zhiguo Liu, Xiaoqi Dong, Lin Wang, Jianxin Feng, Chengsheng Pan and Yunqi Li

Sensors 2022, 22(14), 5439; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145439 - 21 Jul 2022

Cited by 7 | Viewed by 2109

Abstract

With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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18 pages, 2568 KiB

Open AccessArticle

A Novel Scheme for Controller Selection in Software-Defined Internet-of-Things (SD-IoT)

by Jehad Ali and Byeong-hee Roh

Sensors 2022, 22(9), 3591; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093591 - 09 May 2022

Cited by 9 | Viewed by 1959

Abstract

The software-defined networking (SDN) standard decouples the data and control planes. SDN is used in the Internet of Things (IoT) due to its programmability, central view and deployment of innovative protocols, and is known as SD-IoT. However, in SD-IoT, controller selection has never been studied. Controllers control the network and react to dynamic changes in SD-IoT. As sensors communicate frequently with the controller in SD-IoT, there is a degradation in performance with scalability and an increase in flow requests. Hence, the controller performance and selection are critical for SD-IoT. However, one controller’s support for certain functions is high while another’s is poor. There are various SD-IoT controllers, and choosing the best one might be a multi-criteria choice. An analytical network decision making process- (ANDP) based technique is employed here to identify feature-based optimal controllers in SD-IoT. The experimental analysis quantifies the high-weight controller from the feature-based comparison. An ANDP-based feature-based controller selection strategy is suggested, which selects the controller with the best feature set first, before comparing performance. This paper’s main contribution is to evaluate the ANDP for SD-IoT controller selection based on its features and performance validation in the SD-IoT environment. The simulation results suggest that the proposed controller outperforms the controller selected with previous schemes. Choosing an optimal controller in SD-IoT reduces the delay in both normal and heavy traffic scenarios. The suggested controller also increases throughput while using the central processing unit (CPU) efficiently and reduces the recovery latency in case of failures in the network. Full article

(This article belongs to the Special Issue Effective Software-Defined Internet-of-Things (SD-IoT) Leveraging AI, 5G and NFV)

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