Internet Of Things Communication Network Research Articles

Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection.

The proliferation of Internet-of-Things (IoT) technologies in modern smart society enables massive data exchange for offering intelligent services. It becomes essential to ensure secure communications while exchanging highly sensitive IoT data efficiently, which leads to high demands for lightweight models or algorithms with limited computation capability provided by individual IoT devices. In this study, a graph representation learning model, which seamlessly incorporates graph neural network (GNN) and knowledge distillation (KD) techniques, named reconstructed graph with global-local distillation (RG-GLD), is designed to realize the lightweight anomaly detection across IoT communication networks. In particular, a new graph network reconstruction strategy, which treats data communications as nodes in a directed graph while edges are then connected according to two specifically defined rules, is devised and applied to facilitate the graph representation learning in secure and efficient IoT communications. Both the structural and traffic features are then extracted from the graph data and flow data respectively, based on the graph attention network (GAT) and multilayer perceptron (MLP) techniques. These can benefit the GNN-based KD process in accordance with the more effective feature fusion and representation, considering both structural and data levels across the dynamic IoT networks. Furthermore, a lightweight local subgraph preservation mechanism improved by the graph attention mechanism and downsampling scheme to better utilize the topological information, and a so-called global information alignment defined based on the self-attention mechanism to effectively preserve the global information, are developed and incorporated in a refined graph attention based KD scheme. Compared with four different baseline methods, experiments and evaluations conducted based on two public datasets demonstrate the usefulness and effectiveness of our proposed model in improving the efficiency of knowledge transfer with higher classification accuracy but lower computational load, which can be deployed for lightweight anomaly detection in sustainable IoT computing environments.

Strengthening IoT Intrusion Detection through the HOPNET Model

The rapid growth of Internet of Things (IoT) applications has raised concerns about the security of IoT communication systems, particularly due to a surge in malicious attacks leading to network disruptions and system failures. This study introduces a novel solution, the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks. Validation using the Nsl-Kdd dataset involves meticulous data preprocessing for error rectification and feature extraction across diverse attack categories. Implemented on the Java platform, the HOPNET model undergoes comprehensive evaluation through comparative analysis with established intrusion detection methods. Results demonstrate the superiority of the HOPNET model, with improved attack prediction scores and significantly reduced processing times, highlighting the importance of advanced intrusion detection methods for enhancing IoT communication security. The HOPNET model contributes by establishing robust defense against evolving cyber threats, ensuring a safer IoT ecosystem, and paving the way for proactive security measures as the IoT landscape continues to evolve.

College English teaching reform and innovative methods under the new media platform based on the IoT

Hybrid teaching has emerged along with the growth of web technology. Teaching activities are combined with online and offline teaching. Some are online teaching and some are offline teaching. In addition to completing classroom teaching, teachers also need to help students answer questions and learn interactively online. The implementation of these links needs to be reflected in the teacher’s teaching evaluation. The article adopted the analytic hierarchy process, which enabled teachers (managers) to more objectively select and determine the evaluation factors, and decompose them at different levels to make quantitative evaluation, so as to provide appropriate information for teachers (managers) and reduce decision-making errors. In this paper, the effectiveness of the new media English teaching platform in colleges and universities in mainland China was evaluated by the method of final test, and all students signed the informed consent. According to different organizational structures, the new media English teaching platform in universities can be divided into user layer, terminal layer, application layer, platform layer and network layer. Each layer is connected with each other, which organizes all components of the new media English teaching platform of the whole university. The network layer is an essential part of college new media English teaching, including the Internet, Internet of Things (IoT), campus network, education network, etc. Through the Internet, educational network and campus network, various educational institutions and institutions were connected, and information exchange was carried out through the university’s new media English platform. The emergence of the IoT and mobile communication networks has greatly improved the network coverage and the utilization rate of the network. The average students' test score using the proposed platform (80.6) is higher than the average test score without the platform (69.1).The new media teaching platform proposed in the article has enabled teachers to effectively complete the teaching content and effectively enhance the teaching quality.

Supervised learning for more accurate state estimation fusion in IoT-based power systems

Concerned with deploying zero-emission energy sources, reducing energy wasted through transmission lines, and managing power supply and demand, monitoring and controlling microgrids have found utter importance. Accordingly, this paper aims to investigate the efficacy of state estimation fusion for a synchronous generator as well as an induction motor in order to ameliorate system monitoring. A third-order nonlinear state-space model, that operates based on actual input data taken from the Smart Microgrid Laboratory, is assumed for each of the electrical machines. The model parameters are set according to the parameters of the electrical machines. A fusion structure based on the internet of things communication network, which is modified to increase uncertainty, is presented for fusing the state estimates. The data fusion topology is distributed and relies on two data fusion models. The first model is a set of state estimators, referred to as data input-feature output model. The second one fuses the estimators’ outputs based on supervised machine learning methods, referred to as feature input-feature output model. The simulation results in MATLAB and Python show the efficiency of linear regression methods compared with other leveraged methods for data fusion. By comparing the results obtained from both simple and complex estimation filters, it can be deduced that combining simple filters, extended Kalman filter in this case, with simple data fusion methods, linear regression in this case, can produce much more accurate results in a short period of time. Besides, this study shows that the averaging operators are unsuitable for estimation fusion by referring to their convexity condition.

An adversarial domain adaptation approach combining dual domain pairing strategy for IoT intrusion detection under few-shot samples

The security of the Internet of Things (IoT) is an integral research topic to support its applicability. With the rapid development of IoT technology, security threats have become increasingly serious. Attackers turn their attention to large IoT devices with many security vulnerabilities, and many infected IoT devices are used to form botnets to launch intrusions. Furthermore, IoT communication networks poses security risks. Therefore, it is necessary to design a model for the IoT intrusion detection. However, the current models are trained using big data samples. Moreover, IoT devices can only intercept a few-shot samples in special scenarios, resulting in lower efficiency and accuracy. In this study, we propose an adversarial domain adaptation (ADDA) approach with dual domain pairing strategy for IoT intrusion detection under few-shot samples. First, we design two data augmentation algorithms to construct a sample transfer model that conforms to the joint dual domain distribution. We then combine the dual domain pairing strategy and ADDA model to perform supervised domain adaptation. In addition, a lightweight network is designed to extract features. Finally, a new loss function and domain discriminator model are constructed to achieve IoT intrusion detection under few-shot samples. Different results demonstrate the effectiveness of our model.

Network selection based on chi-square distance and reputation for internet of things

The internet of things (IoT) has become one of the most important technologies of the 21<sup>st</sup> century. The IoT environment is composed of heterogeneous IoT communication networks. These technologies are complementary and need to be integrated to meet the requirements of different types of IoT applications that require the mobility of the IoT device under different IoT communication networks. In this paper, the vertical handover decision method is considered to select the appropriate network among different IoT technologies. So, IoT devices, equipped with several radio technologies, can select the most suitable network based on several criteria like quality of service (QoS), cost, power, and security. In this work, a multi-attribute decision-making algorithm (MADM) based on techniques for order preference by similarity to an ideal solution (TOPSIS) that uses chi-square distance instead of Euclidean distance is proposed. The network reputation is added to reduce the average number of handoffs. The proposed algorithm was implemented to select the best technology depending on the requirements of the different IoT traffic classes. The obtained results showed that our proposition outperforms the traditional MADM algorithms.

Enhancement of Online Education in Engineering College Based on Mobile Wireless Communication Networks and IOT

The field of Engineering is that which needs a high level of analytical thinking, intuitive knowledge, and technical know-how. The area of communication engineering deals with different components including, wireless mobile services, radio, broadband, web and satellites. There is a rapid decline in the quality of students produced by engineering faculties as a result of sufficient and quality methods and frameworks of student assessment. The production of high-potential engineers is limited by the utilization of old and traditional education methodology and frameworks. The student presentation estimation system in engineering institution is a motionless manual. Usually, the assessment of student’s performance using the traditional system is limited to the use of students’ performance scores, while failing to evaluate their performance based on activities or practical applications. In addition, such systems do not take cognizance of individual knowledge of students that connects to different activities within the learning environment. Recently, engineering institutions have started paying attention to evaluation solutions that are based on wireless networks and Internet of Things (IoT). Therefore, in this study, an automated system has been proposed for the assessment of engineering students. The proposed system is designed based on IoT and wireless communication networks with the aim of improving the process of virtual education. The data used in this study has been collected through the use of different IoT sensors within the premises of the college, and pre-processed using normalization. After the data was pre-processed, it was stored in cloud. In order to enable the classification of student’s activity, an Adaptive Layered Bayesian Belief Network (AL-BBN) classifier is proposed in this work. The student’s scores have been calculated using fuzzy logic, while Multi-Gradient Boosting Decision Tree (MGBDT) was proposed for decision making. The use of python simulation tool is employed in the implementation of the proposed system, and the evaluation of the performance benchmarks was done as well. Based on the findings of the study, the proposed conceptual model outperformed the existing ones in terms of improving the process of online learning.

Data-centric UML profile for agroecology applications: Agricultural autonomous robots monitoring case study

The conceptual design of information systems is mandatory in several application domains. The advent of the Internet of Things (IoT) technologies pushes conceptual design tools and methodologies to consider the complexity of IoT data, architectures, and communication networks. In agroecology applications, the usage of IoT is quite promising, but it raises several methodological and technical issues. These issues are related to the complexity and heterogeneity of data (social, economic, environmental, and agricultural) needed by agroecology practices. Motivated by the lack of a conceptual model for IoT data, in this work, we present a UML profile taking into account different kinds of data (e.g., sensors, stream, or transactional) and non-functional Requirements. We show how the UML profile integrates with classical UML diagrams to support the design of complex systems. Moreover, We prove the feasibility of our conceptual framework through a theoretical quality assessment and its implementation in the agroecology case study concerning the monitoring of autonomous agricultural robots.

Smart health analysis system using regression analysis with iterative hashing for IoT communication networks

Wireless communication systems offer a dynamic infrastructure with efficient data sensing and forwarding services using digital networks and the Internet of Things (IoT). Many schemes have been proposed to cope with a smart communication system by integrating medical devices. However, lowering the processing overheads with efficient utilization of network services are challenging tasks. Thus, this paper presents a smart health analysis system using machine learning techniques for IoT network, which aims to handle big data with balancing the communication load for green technologies. Firstly, using regression prediction, the proposed system offers quality-aware services, secondly, by exploring intelligent methods it provides a delay-tolerant scheme to give the least overhead communication paradigm using mobile agents. Finally, the big data is secured using cryptographic techniques and collaborative devices to maintain its trustworthiness with the cloud. The proposed system has revealed a noteworthy performance in terms of network parameters against existing studies.

Priority based dynamic spectrum management using Virtual Utility Functions in Cognitive Radio enabled Internet of Things

Fast growth of wireless devices in IoT (Internet of Things), industry 5.0 and 6G cellular networks, is dealing with the challenge of spectrum scarcity and uneven utilization of available resources. To achieve Ambient Intelligence (AmI) in IoT communication network, dynamic utilization of available network resources, is a challenge faced by resource constraint IoT devices. Cognitive radio technology enables these devices to intelligently sense the free spectrum holes and smartly manage available channels opportunistically. To jointly deal with the challenge of spectrum scarcity and application specific network service provision, we propose a novel multi-attribute based two-tier framework called Cognitive Radio Efficient Spectrum Utilization (CR-ESU) which integrates the concept of Network Function Virtualization (NFV) as Virtual Utility Functions (VUFs) with cognitive radio enabled IoT devices to achieve the dynamic and flexible network services as well as spectral efficiency. The proposed framework is one of the pioneer works that incorporate the two ground breaking technologies, NFV and CRN to create a smart and flexible CR-enabled IoT network. CR-ESU also ensures the best channel selection using Dynamic Channel Reservation (DCR) scheme to decrease the channel switching rate. Continuous Time Markov Chain (CTMC) model is used for network modeling and mathematical expression formation. Promising results show the potential of CR-ESU framework to form a reconfigurable and efficient network.

A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks

In this paper, an antenna with a multiple-input, multiple-output (MIMO) configuration is demonstrated for mm-wave 5G-based Internet of Things (IoT) applications. The two antenna elements are arranged next to each other to form a two-port antenna system such that significant field decorrelation is achieved. Moreover, a dielectric layer is backed by an eventual multiport system to amend and analyze the radiation characteristics. The overall size of the MIMO configuration is 14 mm × 20 mm, and the operation bandwidth achieves ranges from 16.7 to 25.4 GHz, considering the −10 dB criterion with a maximum isolation of more than −30 dB within the operating band. The peak gain offered by the antenna system is nearly 5.48 dB, and incorporating a dielectric layer provides an increase in the gain value to 8.47 dB. Within the operating band, more than 80% total efficiency is observed, and analysis shows several MIMO performance metrics with favorable characteristics. The compactness of the proposed design with high isolation, improved gain, and wideband features make it a suitable candidate for mm-wave-based 5G applications.

Contextualized Design of IoT (Internet of Things) Finance for Edge Artificial Intelligence Computing.

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively.

ASC Performance Prediction for Medical IoT Communication Networks

Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communication issue is very important for medical IoT communication networks. This paper investigates the secrecy performance of medical IoT communication networks. To improve the secrecy performance, we adopt a cooperative communication strategy. We also use the average secrecy capacity (ASC) as a metric, and the expressions are first derived. Then, a secrecy performance intelligent prediction algorithm is proposed. The extensive simulations are used to verify the proposed method. Compared with other methods, the proposed algorithm realizes a better prediction precision.

Performance Analysis and Prediction for Mobile Internet-of-Things (IoT) Networks: A CNN Approach

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.

Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications

Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.

Context Information Aggregation Mechanism Based on Bloom Filters (CIA-BF) for High Performance Monitoring Applications of Internet of Things

Internet of Things (IoT) has become a popular technology in recent years. Different IoT applications such as traffic control, environment monitoring, etc. contain many sensor devices, routers, actuators, edge routers, and Base Stations (BS) which communicate with each other and send millions of data packets that need to be delivered to their destination nodes successfully to ensure the High-performance communication networks. IoT devices connect to the Internet using wired or wireless communication channels where most of the devices are wearable, which means people slowly move from one point to another or fast-moving using vehicles. How to ensure high performance of IoT data networks is an important research challenge while considering the limitation of some IoT devices that may have limited power resources or limited coverage areas. Many Kinds of research focus on how to customize routing protocols to be efficient for IoT devices. The traditional routing mechanisms utilized specific IP addresses to identify users while in IoT it is more beneficial to identify a group of users (things) based on any contexts, status, or values of their resources such as the level of their batteries (e.g., low, medium or high). While IoT devices have different characteristics, a multicasting mechanism to send one message to various groups of devices will not be efficient in IoT communication networks since the aggregation of packets is very difficult. Thus, it is useful to propose a mechanism that able to filter data packets that need to be sent to a specific group of devices. In this paper, we propose efficient context-aware addressing mechanism, which is based on bloom filters to increase the performance of IoT communication networks. A routing architecture is built based on bloom filters which store routing information. In our works, we reduce the size of routing information using a proposed aggregation mechanism which is based on connecting each group of IoT devices with an edge router which is hierarchically connected to an upper router after operating its bloom filter. Our simulation results show a significant improvement in the IoT performance metrics such as packets transmission delay, jitter the throughput, packets dropping ratio, and the energy consumption in comparison with well-known routing protocols of IoT such as Destination Sequenced Distance Vector routing protocol (DSDV), and Ad hoc On-demand Distance Vector routing protocol (AODV).

An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.

Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

An efficient group signcryption scheme supporting batch verification for securing transmitted data in the Internet of Things

This paper presents a novel secure and privacy-preserving scheme for enhancing the security of transmitted data in an Internet of Things (IoT) environment. The proposed scheme combines a short group signature scheme with a signcryption method to represent an efficient short group signcryption (SGSC) technique with added security features. The proposed scheme supports aggregation and batch verification to eliminate the issue of increasing the computation delay from which the current IoT system suffers, particularly when the number of connected devices are increased. Detailed security analysis has shown that the proposed SGSC scheme is robust against many security and privacy threats in the IoT communication network, and can achieve more security features compared with currently available group signature schemes. The proposed scheme shows high efficiency in terms of computational cost in verifying a large number of messages within a short time frame.

A Privacy-Preserving Authentication, Authorization, and Key Agreement Scheme for Wireless Sensor Networks in 5G-Integrated Internet of Things

Wireless sensor networks (WSNs) have played an important role in the Internet of Things (IoT), and the 5G network is being considered as a major candidate for IoT's communication network with the advent of 5G commercialization. The potential of integrating WSNs and 5G in the IoT is expected to allow IoT to penetrate deeply into our daily lives and to provide various services that are convenient, but at the same time, it also brings new security threats. From this aspect, user authentication and key agreement are essential for secure end-to-end communication. As IoT devices, including sensors, collect and process more and more personal information, both anonymous authentication and authorization are also required to protect the privacy and to prevent anyone without privileges from accessing private data. Recently, Adavoudi-Jolfaei et al. proposed an anonymous three-factor authentication and access control scheme for real-time applications in WSNs. However, we found that this scheme does not provide sensor-node anonymity and suffers from user collusion and desynchronization attacks. In this paper, we introduce a system architecture by considering the integration of WSNs and 5G for IoT. Based on a cryptanalysis of Adavoudi-Jolfaei et al.'s scheme and the system architecture, we propose an elliptic curve cryptography (ECC)-based privacy-preserving authentication, authorization, and key agreement scheme for WSNs in 5G-integrated IoT. We conduct a formal and informal security analysis in order to demonstrate that the proposed scheme withstands various security attacks and guarantees all desired security features, overcoming the drawbacks of Adavoudi-Jolfaei et al.'s scheme. Finally, a performance and comparative analysis with the related schemes indicate that the proposed scheme is both efficient and more secure.