Industrial Internet Of Things Applications Research Articles

Cache-assisted computation offloading for workflow applications in industrial internet of things

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Improving Non-Line-of-Sight Identification in Cellular Positioning Systems Using a Deep Autoencoding and Generative Adversarial Network Model

Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.

An Industrial Internet-of-Things (IIoT) Open Architecture for Information and Decision Support Systems in Scientific Field Campaigns.

Information and decision support systems are essential to conducting scientific field campaigns in the atmospheric sciences. However, their development is costly and time-consuming since each field campaign has its own research goals, which result in using a unique set of sensors and various analysis procedures. To reduce development costs, we present a software framework that is based on the Industrial Internet of Things (IIoT) and an implementation using well-established and newly developed open-source components. This framework architecture and these components allow developers to customize the software to a campaign's specific needs while keeping the coding to a minimum. The framework's applicability was tested in two scientific field campaigns that dealt with questions regarding air quality by developing specialized IIoT applications for each one. Each application provided the online monitoring of the acquired data and an intuitive interface for the scientific team to perform the analysis. The framework presented in this study is sufficiently robust and adaptable to meet the diverse requirements of field campaigns.

Design and Implementation of Long Range Wide Area Networks for Future Industrial IoT Applications

Background: The evolution of Long Range Wide Area Networks (LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT). LoRaWAN is more suitable for transmitting smaller intermittent data but using smaller bandwidth over a long distance. Objective: This research paper proposes to design a Low Power Wide Area Network (LPWAN) to provide long-range data transfer for Industrial IoT applications, especially for industrial sensor data. Methods: This research work deploys the experimental setup of LoRaWAN devices using LoRa gateways and Internet of Things network server to evaluate its performance for IIOT applications. Results: The deployment of this LoRaWAN has demonstrated its long range, low power, stability and low deployment cost through extensive performance evaluation carried out. LoRaWAN has been analyzed for its coverage and throughput performance. Conclusion: Through performance analysis, potential enhancements have been identified to overcome its shortcomings. This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications.

Simultaneous Detection of the Orientation and Position of Moving Objects With Simple RFID Array for Industrial IoT Applications

Simultaneous Detection of the Orientation and Position of Moving Objects With Simple RFID Array for Industrial IoT Applications

Edge AI: Revolutionizing Real-Time Intelligence at the Network Periphery

Abstract: Edge AI, which transfers computation from centralized data centers to devices at the edge of the network, is a revolutionary development in artificial intelligence that offers advantages including real-time processing, decreased latency, increased privacy, and increased bandwidth efficiency. This paradigm is especially important for industrial IoT, smart city, and autonomous car applications where quick data processing and decision making are critical. Even with obstacles like scalability and hardware constraints, edge AI capabilities are being significantly expanded by continuing research and technical developments. Its revolutionary influence is demonstrated by current applications in a variety of domains, from improving industrial operations to urban infrastructure. Edge AI promises to revolutionize many industries, changing how we interact with technology and influencing the development of the next wave of AI applications as it develops further thanks to advancements in hardware and AI algorithms. With enormous potential for the future, edge AI represents a major advancement toward ubiquitous AI with broad ramifications for both technology and society

Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments.

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OF0 and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OF0 routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.

A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things

AbstractThe number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber‐attack detection approaches scrap to address and support the run‐time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber‐attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT‐IOT‐IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber‐attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F‐score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

Estimating vibration sources for industrial IoT using dilated CNN and deconvolution

To minimize data traffic in industrial IoT applications, vibration-based condition monitoring should be conducted on sensors and without requiring machine-specific information. The proposed method enables blind estimation of vibration sources, eliminating the need for information about the monitored equipment or external measurements. Vibrations in rotating machinery primarily originate from two sources: dominant gear-related vibrations and low-energy signals associated with bearing faults. Both sources are distorted by the machine's transfer function before reaching the sensor. This method estimates both sources in two stages: first, the gear signal is isolated using a dilated CNN; second, the bearing fault signal is estimated using the squared log envelope of the residual. The effect of the transfer function is removed from both sources using a novel whitening-based deconvolution method (WBD). Both simulation and experimental results demonstrate the method's ability to detect bearing failures early without additional information. This study considers both local and distributed bearing faults, assuming the vibrations are recorded under stable operating conditions.

I-Health: SDN-Based Fog Architecture for IIoT Applications in Healthcare.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

A DBDCP Antenna With a Helmet-Conformal AMC for Industrial IoT Applications Featuring LHCP and RHCP in the Low and High Bands, Respectively

A DBDCP Antenna With a Helmet-Conformal AMC for Industrial IoT Applications Featuring LHCP and RHCP in the Low and High Bands, Respectively

Overview of AI-Models and Tools in Embedded IIoT Applications

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

HCSMBO: A hybrid cat swarm and monarch butterfly optimization algorithm for energy consumption optimization in industrial internet of things

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.

Performance Limit: Fuzzy Logic Based Anti-Collision Algorithm for Industrial Internet of Things Applications

Passive RFID has the advantages of rapid identification of multi-target objects and low implementation cost. It is the most critical technology in the Industrial Internet of Things information-gathering layer and is extensively applied in various industries, such as smart production, asset management, and monitoring. The signal collision caused by the communication between the reader/writer and tags sharing the same wireless channel has caused a series of problems, such as the reduction of the identification efficiency of the reader/writer and the increment of the missed reading rate, thus restricting the further development of RFID. At present, many hybrid anti-collision algorithms integrate the advantages of Aloha and TS algorithms to optimize RFID system performance, but these solutions also suffer from performance bottlenecks. In order to break through such performance bottleneck, based on the ISE-BS algorithm, we combined the sub-frame observation mechanism and the Q value adjustment strategy and proposed two hybrid anti-collision algorithms. The experimental results show that the two algorithms proposed in this paper have obvious advantages in system throughput, time efficiency and other metrics, surpassing existing UHF RFID anti-collision algorithms.

Stateless Blockchain-Based Lightweight Identity Management Architecture for Industrial IoT Applications

Stateless Blockchain-Based Lightweight Identity Management Architecture for Industrial IoT Applications

Integral Cryptanalysis of Reduced-Round IIoTBC-A and Full IIoTBC-B

This paper delves into the realm of cryptographic analysis by employing mixed-integer linear programming (MILP), a powerful tool for automated cryptanalysis. Building on this foundation, we apply the division property method alongside MILP to conduct a comprehensive cryptanalysis of the IIoTBC (industrial Internet of Things block cipher) algorithm, a critical cipher in the security landscape of industrial IoT systems. Our investigation into IIoTBC System A has led to identifying a 14-round integral distinguisher, further extended to a 22-round key recovery. This significant finding underscores the cipher’s susceptibility to sophisticated cryptanalytic attacks and demonstrates the profound impact of combining the division property method with MILP in revealing hidden cipher weaknesses. In the case of IIoTBC System B, our innovative approach has uncovered a full-round distinguisher. We provide theoretical validation for this distinguisher and uncover a pivotal structural issue in the System B algorithm, specifically the non-diffusion of its third branch. This discovery sheds light on inherent security challenges within System B and points to areas for potential enhancement in its design. Our research, through its methodical examination and analysis of the IIoTBC algorithm, contributes substantially to the field of cryptographic security, especially concerning industrial IoT applications. By uncovering and analyzing the vulnerabilities within IIoTBC, we enhance the understanding of cipher robustness and pave the way for advancements in securing industrial IoT communications.

Dynamic trust security approach for edge computing-based mobile IoT devices using artificial intelligence

The Industrial Internet of Things (IIoT) encompasses various applications such as smart cities, healthcare, and agriculture, which rely on a multitude of interconnected devices. These applications demand specific performance requirements, including low latency and sufficient bandwidth. Edge computing has emerged as a promising solution to address the challenges associated with data flow processing. Particularly, in scenarios like smart cities, where a massive number of IIoT devices are deployed, edge computing serves as the primary data processing layer. However, the limited resources of edge computing, compared to cloud computing, pose significant security concerns for IIoT applications. In fact, this paper proposes a novel security system to secure the relation between the IIoT devices and edge computing. Dynamic Trust Security Approach (DTSA) leverages Artificial Intelligence to analyze, evaluate and enhance the security and privacy aspects of the IIoT devices’ interaction with edge computing. DTSA improve the security level of the IIoT system and improve the performance with more than 45% compare to the traditional method.

Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks

LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology.

Vibration Energy Harvesting with Printed P(VDF:TrFE) Transducers to Power Condition Monitoring Sensors for Industrial and Manufacturing Equipment

A vibration energy harvesting system based on fully printed piezoelectric transducers is realized. The transducers have a butterfly‐like architecture based on two single‐optimized cantilevers with a resonance frequency tuned to 49.5 Hz and can be easily mounted to an industrial engine. By comparing single‐ and multistack configurations of the piezoelectric layers combined with full‐wave or voltage doubler rectifiers, the power transfer characteristics and impedance can be matched to the electrical requirements of the sensing circuitry. A single stack with 21 μm thickness results in the maximum power output of 14.4 μW at a vibration velocity of 11.5 mm s−1, typical for industrial engines. The system is used to power a wireless sensor node on a 1 kW rotary pump in normal operation. The system can harvest up to 138 mJ within 24 h, sufficient for daily remote monitoring of the engine's vibration spectrum and temperature state. The system is thus suitable as a low‐cost, ecofriendly power source for industrial IoT applications.

PrioMQTT: A prioritized version of the MQTT protocol

MQTT is an application layer protocol that, thanks to its simplicity and low overhead, is widely used in the Internet of Things (IoT) devices typically found in home automation and consumer applications. The MQTT properties make it an interesting option also for Industrial IoT (IIoT) applications. However, MQTT is not specifically devised for IIoT applications requiring low-latency and the support for time-constrained transmissions. For this reason, this paper proposes an IIoT-enabled version of MQTT called a Prioritized MQTT (PrioMQTT) that is able to provide low latencies to time-critical messages. Unlike the standard MQTT, PrioMQTT adopts the UDP/IP stack, which is more suitable than TCP/IP for low-latency communications. Moreover, PrioMQTT introduces a mechanism to prioritize the time-critical messages over the non-time-critical ones. The combination of the UDP/IP stack and priority support in the PrioMQTT protocol is achieved while maintaining the compliance with the MQTT standard message format. As a result, PrioMQTT can be implemented on commercial-off-the-shelves (COTS) devices without hardware modifications. The paper describes the PrioMQTT protocol and investigates its performance through an assessment in a realistic industrial scenario and in comparison with the standard MQTT protocol.