Performance Of Internet Of Things Networks Research Articles

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Trustworthiness possesses numerous design challenges for the researchers and scientists in the global era of internet of things. The solution to deal with these challenges lies in trust management mechanisms (TMM). The integration of TMM on the Internet of things (IoT) adds a higher level of security and reliability for the resource utilization and management. On the other hand, the efficient and optimal implementation of TMM in IoT network requires significant efforts and innovations. In this paper, an innovative edge based trustworthy framework (EBTF) using novel cooperative centrality-based system (NCCBS) has been proposed and implemented for IoT networks. Trustworthiness has been computed using factors like direct trust, recommendation, global trust, and centralities. The NCCBS has been compared with existing mechanism namely: Distributive Reputation Management (DRM), Personalized Similarity Measure (PSM), and Reliable Trust Computation Mechanism (RTCM). The NCCBS outperformed the existing approaches with the adoption of dynamic weight computation scheme. The performance of IoT networks has been evaluated on parameters like recommendation weight, trust index, interaction success rate and malicious behaviour analysis. Simulations have been carried out to evaluate and validate the proposed NCCBS and it depicts optimal performance with reference to the existing schemes.

Methods of computer modeling of electromagnetic field propagation in urban scenarios for Internet of Things

Abstract The paper investigates software solutions to simulate radio waves propagation in urban environment, specifically for the context of the Internet of Things (IoT). The purpose of the study is to provide a comprehensive guide for utilizing the Altair Feko software to obtain a detailed representation of the IoT network, its associated parameters and how they relate to the modeling of electromagnetic fields. It is beneficial to simulate the network during the design stage to obtain valuable data and make necessary adjustments. The key features of a Sigfox or other low power IoT network can be obtained by simulation, enabling an evaluation of the network design prior to its actual implementation. Given the growing demand for IoT devices and networks, researching the optimal design and performance of such networks is of great importance. This underscores the need for continuous exploration of effective methods to achieve efficient design and performance of IoT networks.

Massive multi-player multi-armed bandits for IoT networks: An application on LoRa networks

More and more manufacturers, as part of the transition towards Industry 4.0, are using Internet of Things (IoT) networks for more efficient production. The wide and extensive expansion of IoT devices and the variety of applications generate different challenges, mainly in terms of reliability and energy efficiency. In this paper, we propose an approach to optimize the performance of IoT networks by making the IoT devices intelligent using machine learning techniques. We formulate the optimization problem as a massive multi-player multi-armed bandit and introduce two novel policies: Decreasing-Order-Reward-Greedy (DORG) focuses on the number of successful transmissions, while Decreasing-Order-Fair-Greedy (DOFG) also guarantees some measure of fairness between the devices. We then present an efficient way to manage the trade-off between transmission energy consumption and packet losses in Long-Range (LoRa) networks using our algorithms, by which LoRa nodes adjust their emission parameters (Spreading Factor and transmitting power). We implement our algorithms on a LoRa network simulator and show that such learning techniques largely outperform the Adaptive Data Rate (ADR) algorithm currently implemented in LoRa devices, in terms of both energy consumption and packet losses.

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

Analysis of the Performance of IoT Networks in Acoustic Environment by using LZW Data Compression Technique

The Internet of Things (IoT) has experienced phenomenal growth, opening up a wide range of applications in many settings. Due to the properties of sound propagation, IoT networks operating in acoustic environments in particular present special difficulties. Data compression techniques can be used to minimize overhead and maximize resource utilization in these networks to increase performance. The performance of IoT networks in acoustic environments is examined in this study, with a focus on routing overhead, throughput, and typical end-to-end delay. Lempel-Ziv-Welch (LZW) data compression is used to reduce data size and boost communication effectiveness. Three well-known protocols—MQTT, CoAP, and Machine-to-Machine (M2M)—are assessed in relation to acoustic Internet of Things networks. To mimic different acoustic conditions and collect performance metrics, a thorough experimental setup is used. Different network topologies, data speeds, and compression settings are used in the studies to determine how they affect the performance metrics. According to the analysis's findings, all three protocols' routing overhead is greatly decreased by the LZW data compression approach, which enhances network scalability and lowers energy usage. Additionally, the compressed data size has a positive impact on network throughput, allowing for effective data transmission in acoustic contexts with limited resources. Additionally, using LZW compression is seen to minimize the average end-to-end delay, improving real-time communication applications. This study advances knowledge of IoT networks operating in acoustic environments and the effects of data reduction methods on their functionality. The results offer useful information for network engineers and system designers to optimize the performance of IoT networks in similar situations. Additionally, a comparison of the MQTT, CoAP, and M2M protocols' suitability for acoustic IoT deployments is provided, assisting in the choice of protocol for particular application needs.

Localization of sensor nodes in the Internet of Things using fuzzy logic and learning automata

The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively.

Defective node detection by employing DS theory and RP model for secure internet of things networks

Internet of Things Networks (IOTN) are applied in all sectors as a result of their broad applicability. Even though the performance of IOTN is satisfactory, it still faces various obstacles, including energy constraints, dependability, and security. This work aims to improve the reliability of the network by identifying defective nodes. Defective nodes affect the normal functionality of the entire network; therefore, it is vital to detect the defective nodes, such that the Quality of Service (QoS) is improved. This work detects defective nodes by employing the Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster-Shafer (DS) theory. The effectiveness of the proposed work is found to be satisfactory in terms of defective node detection, precision, energy consumption, and network lifetime.

Honeynet Implementation in Cyber Security Attack Prevention with Data Monitoring System Using AI Technique and IoT 4G Networks

Cyber Physical Systems (CPS) comprises of the ubiquitous object concept those are connected with Internet to provide ability of data transmission and sensing over network. The smart appliances transmits the data through CPS devices with the implementation of Internet of Things (IoT) exhibits improved performance characteristics with significant advantages such as time savings, reduced cost, higher human comfort and efficient electricity utilization. In the minimal complexity sensor nodes cyber physical system is adopted for the heterogeneous environment for the wireless network connection between clients or hosts. However, the conventional security scheme uses the mechanisms for desktop devices with efficient utilization of resources in the minimal storage space environment, minimal power processing and limited energy backup. This paper proposed a Secure Honeynet key authentication (SHKA) model for security attack prevention through effective data monitoring with IoT 4G communication. The proposed SHKA model uses the lightweight key agreement scheme for authentication to provide security threats and confidentiality issues in CPS applications. With the implementation of SHKA HoneyNet model the data in IoT are monitored for security mechanism in IoT environment. The middleware module in SHKA scheme uses the Raspberry platform to establish internetworking between CPS device to achieve dynamic and scalability. The secure IoT infrastructure comprises of flexible evaluation of user-centric environment evaluation for the effectiveness. The developed SHKA model perform mutual authentication between CPS devices for minimal computation overhead and efficiency. The wireless channel uses the dynamic session key for the secure communication for cyber-attacks security with lightweight security in CPS system. The SHKA model demonstrate the effectiveness based on consideration of three constraints such as low power processing, reduced storage and minimal backup energy. Experimental analysis stated that proposed SHKA scheme provides lightweight end-to-end key establishment in every session. The CPS devices generates the session key of 128 bit long. The minimum key size is implemented to provide effective security in IoT 4G communication with minimal execution time. The simulation results demonstrated that SHKA model exhibits effective cyber-attacks for the constraint devices to improve performance of IoT network.

Multi-Controller Model for Improving the Performance of IoT Networks

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.

Performance evaluation of IoT networks: A product density approach

Mobile cellular-based Internet of Things (IoT) networking is set to be enhanced with the addition of two important pillars of 5G — massive Machine Type Communications and ultra-Reliable Low Latency Communications. Temporal traffic uncertainty from diverse applications in IoT networks mandates novel modeling approaches for performance evaluation. This paper introduces a novel stochastic point process approach that can be used to evaluate the time-dependent performance of IoT base stations. Special correlation functions called Product Densities are used to (a) evaluate time-dependent offered traffic, and (b) analyze delay performance. These performance measures are evaluated at IoT base station for Poisson as well as non-Homogeneous Poisson (Beta distributed) traffic arrival processes suggested by 3rd Generation Partnership Project(3GPP). The Product Density estimates of offered traffic are found to be more accurate than the point-wise stationary approximation (PSA) under non-stationary traffic arrival rates. Results from the proposed analytical model are compared with results from a simulation of two queuing models of the base station; infinite server model for offered traffic and multi-server model for delay performance. Analytical results from Product Density functions also correlate with the simulation outcomes suggesting that the proposed Product Density technique is effective when modeling the time-dependent performance of IoT networks subjected to non-stationary traffic conditions, which reflects the real-world scenario.

Predicting Internet of Things (IOT) Security and Privacy Risks – A Proposal Model

The Internet of things (IOT) users lack awareness of IOT security infrastructure to handle the risks including Threats, attack and penetration associated with its use. IOT devices are main targets for cyber-attacks due to variable personally identifiable information (PII) stored and transmit in the cyber centers. The security risks of the Internet of Things aimed to damage user's security and privacy. All information about users can be collected from their related objects which are stored in the system or transferred through mediums among diverse smart objects and may exposed to exposed dangerous of attacks and threats if it lack authentication so there are essential need to make IOT security requirements as important part of its efficient implementation. These requirements include; availability, accountability, authentication, authorization, privacy and confidentiality, Integrity and Non-repudiation. The study design is a survey research to investigate the visibility of the proposed model of security management for IOT uses, the security risks of IOT devices, and the changes IOT technology on the IT infrastructure of IOT users through answering of the research questionnaires. This work proposes a model of security management for IOT to predict IOT security and privacy threats, protect IOT users from any unforeseen dangers, and determine the right security mechanisms and protocols for IOT security layers, as well as give the most convenient security mechanisms. Moreover, for enhancing the performance of IOT networks by selecting suitable security mechanisms for IOT layers to increase IOT user's security satisfaction.

Impairments-aware time slot allocation model for energy-constrained multi-hop clustered IoT nodes considering TDMA and DSSS MAC protocols

The possibility of Internet of Things (IoT) is that a large number of live items (e.g., apparatuses) in the network are available, detected, and interconnected. In any case, the performance of IoT networks may be limited due to several impairments, most importantly, the energy-constrained IoT nodes. In fact, this limitation opens the door to many researchers where using network clustering has proven to be efficient energy saving mechanism. To the best of our knowledge, there are no prior research, in the field of multi-hop clustered IoT networks, which addressed the energy-constrained IoT nodes problem along with the delay in receiving data messages from devices, located far away from the remote users (or control center), in timely basis. Practically, it is significantly challenging to contemplate and clarify this issue. Consequently, in this paper, we make major amendments on the prior work, Improved Energy and Distance Based CH Selection with Balanced Objective Function (IEDB-CHS-BOF), and particularly propose a multi-hop routing (MR) protocol in which the IoT network is divided into a number of virtual sections with lengths held below the cross-over communication distance, thereby ensuring minimal energy depletion. Over those virtual sections, efficient forwarder and cluster head selection algorithms are further proposed. Interestingly, we propose a novel delay-, power-, and interference-aware time slot allocation model in which Time Division Multiple Access (TDMA) and Direct Sequence Spread Spectrum (DSSS) MAC protocols are operated. Furthermore, it is a good representation of the above-mentioned proposals. As a result, our protocols are referred to as MR-IEDB-CHS-TDMA and MR-IEDB-CHS-DSSS, respectively. To this end, numerous simulations have been conducted for evaluating the performance of the proposed protocols in the perspective of IoT network lifetime. In addition, sufficient comparisons have been conducted with the existing state-of-the-art protocols. In overall, the MR-IEDB-CHS-TDMA protocol keeps the IoT network functioning the longest.

DDoS detection in 5G-enabled IoT networks using deep Kalman backpropagation neural network

The fifth-generation (5G) wireless communication systems associating with the high achievable data-transfer speeds will significantly affect the performance of IoT networks. On one hand, the internet goes through a dramatic transaction period that shapes every aspect of our lives, industry, and business where cloud computing, smart cities, and the Internet of Things (IoT) play a significant role in the advancement of data transfer, storing, and processing. On the other hand, it plays a significant role in emerging advanced versions of different types of cybersecurity attacks especially that are novel, hard-to-detect, and that of distributive never cease-fire characteristics. To mitigate these concerns, we present a distributed denial-of-service (DDoS) intrusion detection model that can be implemented in IoT dynamic environments, providing an intelligent intrusion detection mechanism against the second biggest threat to data traffic and transfer on IoT networks. Kalman backpropagation neural network-based DDoS intrusion detection is proposed in this work. The framework is validated through various simulations via the most up to date CICDDoS2019 dataset to demonstrate the effectiveness of the solution in terms of intrusion detection. the proposed solution achieved an average detection accuracy of 94% with 0.0952 false alarm rate and 97.49%, 91.22% for detection rate, and precision respectively.

Spectrum Based Power Management for Congested IoT Networks.

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

A Deep Learning method for effective channel allotment for SDN based IOT

Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed. Doing proper assignment of appropriate channels to the generated traffic in SDN based IoT can affect transmission speed enormously. Software Defined Networking has been evolved as a supporting technology to improve the performance of IoT networks and to increase transmission quality. Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment. Hence, in this paper CNNs based network traffic prediction and allocation of channel technique is proposed. This technique significantly improves the network performance.

An Exact Analytical Model for an IoT Network with MMPP Arrivals

Analytical modeling of the Internet of Things (IoT) networks is challenging. This is due to the presence of a large number of devices in these networks and the complexity of the priorities between different types of traffic. Taking these aspects into account, the objective of this paper is to analyze the performance of an IoT network where the IoT devices work independently of one another. To this end, we developed a novel multi-dimensional Continuous-Time Markov Chain (CTMC) model with threshold-based preemption. In this model, each IoT device is modeled as a Markov Modulated Poisson Process (MMPP) that can transmit regular and alarm packets. Alarm packets have higher priority over regular packets. To measure access to the channel between alarm and regular packets, we introduced a threshold parameter where the threshold is the number of packets in the alarm queue that indicates when preemption starts. The performance measures include blocking probability, the average delay of regular packets and alarm packets, discard rate , and success probability of regular packets. Comprehensive numerical analysis was conducted. Our results indicate that impact of the threshold on performance measures is higher on the boundary values of the threshold. The model was proven to be efficient in analyzing the performance of IoT networks on a wide range of parameter values. These results may be used in the future to develop and assess a protocol that utilizes a scheduling algorithm with a dynamic preemption threshold to optimize the performance of the IoT network.

Using priority queuing for congestion control in IoT‐based technologies for IoT applications

SummaryThe Internet of Things (IoT) connect millions of devices in diverse areas such as smart cities, e‐health, transportation and defense to meet a wide range of human needs. To provide these services, a large amount of data needs to be transmitted to the IoT network servers. However, the IoT networks suffer from limited resources such as buffer size, node processing capabilities, and server capacities adversely affecting throughputs, latency, and energy consumption. Additionally, the ensuing heavy network traffic due to large amount of data transmitted results in congestion which degrades IoT network performance. Therefore, innovative congestion control techniques, e.g., queue management approach needs to be developed to overcome congestion problems in IoT networks. In this paper, a novel priority queuing technique (Npqt++) is developed to control congestion in IoT networks. The Npqt++ implements a preemptive/nonpreemptive discipline with a discretion rule to classify network traffic based on their real‐time requirement into priority groups. If the discretion rule for low priority packets is satisfied, high priority packets are pushed to the front of the queue; otherwise, they wait in the queue. Our approach significantly outperforms existing techniques in terms of throughput, delay, and energy consumption.

Design and Operation of a Lightweight Educational Testbed for Internet-of-Things Applications

In this article, we present the design and operation process of the AssIuT-IoT testbed; an educational and remotely accessible testbed for Internet-of-Things (IoT) applications. The testbed adopts the experiment as a service (EaaS) model in which users are able to access the testbeds and reserve/use the available resources remotely over the Internet. Also, the testbed is federated with other ones distributed among different locations to allow the sharing of resources, user accounts, and policies. The federated testbeds form an educational consortium that facilitates students' access to hardware resources available at different locations. AssIuT-IoT testbed consists of a control server and a set of IoT nodes (hardware resources) supported with different wireless communication capabilities. We present the design, operation, and performance details of the testbed in terms of the hardware and software components. We describe the steps needed to complete experiments using our testbed and we provide some examples. Moreover, we evaluate the testbed and the IoT network's performance. Finally, we present the IoT students' competition as one of the activities where the testbed has been used.

Adaptive delay-constrained resource allocation in mobile edge computing for Internet of Things communications networks

The traffic burden at each node in Internet-of-Things (IoT) communication networks becomes prohibitively high especially when involving exhaustive computation. Mobile edge computing (MEC) makes this complicated computation feasible while alleviates the traffic burden by providing the corresponding node with powerful computing resources through wireless transmission between the node and the MEC for offloading computation. However, the transmission via the varying wireless channel requires considerable energy consumption and imposes delay. In this paper, we study the trade-off between the energy consumption and the delay performance in IoT network due to the offloading computation and the wireless communication. An optimization problem involving the offloading ratio for MEC as unknown parameter is established by minimizing the total energy consumption subject to a delay constraint. The problem is then solved by analyzing the convexity of the cost function and the constraint. Moreover, the scaling law of both energy cost and delay performance of IoT networks is investigated with respect to the number of nodes employing the MEC. It is discovered that the delay performance decreases in the logarithm with increasing the number of nodes while the energy cost grows linearly with the increase of the number of nodes. Numerical simulations verifying the performances of the proposed method in the studied IoT networks with MEC are provided.

Toward Edge-Based Deep Learning in Industrial Internet of Things

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach. 1 Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes. 1 Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.