Industrial Wireless Sensor Networks Research Articles

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks.

The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.

A lightweight smart contracts framework for blockchain‐based secure communication in smart grid applications

AbstractEnergy is a crucial need in today's world for powering homes, businesses, transportation, and industrial processes. Fossil fuels, such as oil, coal, and natural gas, have been the primary sources of energy for decades. However, there is growing recognition of the negative environmental impact of fossil fuels and the need to transition to cleaner and more sustainable sources of energy. Distributed Energy Resources , such as wind and solar offer several benefits including, reducing energy costs, increasing resiliency, and decreasing carbon emissions. However, the integration of () into the grid requires advanced communication and secure control strategies to ensure a stable and reliable grid operations. In this regard, a blockchain‐based industrial wireless sensor network can provide secure and resilience data transmission to facilitate intelligent integration, monitoring, and control of in the smart grid. In this research, a smart contracts framework in Solana called Advanced Solana Blockchain () is proposed for in the smart grid. The proposed scheme enables resilient and secure real‐time control and monitoring of in smart grids. The performance evaluations and security analysis illustrated that this scheme is secure, reliable, and suitable in terms of lightweight data sharing between in smart grids.

A Simple Event-Based Average Consensus Clock Synchronization Scheme in Industrial Wireless Sensor Networks Under Communication Delays

A Simple Event-Based Average Consensus Clock Synchronization Scheme in Industrial Wireless Sensor Networks Under Communication Delays

Integration of Cloud Based Scheme with Industrial Wireless Sensor Network for Data Publishing in Privacy of Point Source

Integration of Cloud Based Scheme with Industrial Wireless Sensor Network for Data Publishing in Privacy of Point Source

Integration of cloud-based scheme with industrial wireless sensor network for data publishing in privacy of point source

Integration of cloud-based scheme with industrial wireless sensor network for data publishing in privacy of point source

Enabling Centralized Scheduling Using Software Defined Networking in Industrial Wireless Sensor Networks

Enabling Centralized Scheduling Using Software Defined Networking in Industrial Wireless Sensor Networks

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio Irregularity

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches.

JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm

SummaryIndustrial Wireless Sensor Networks (IWSNs) have gained significant popularity for their ability to improve plant productivity and production efficiency through self‐organization and rapid deployment. However, the challenge of achieving reliable and sustainable data transmission remains due to the large amount of heterogeneous data generated by large‐scale IWSNs. In this paper, we present a systematic approach that addresses this challenge by focusing on data transmission clustering strategies, optimal cluster head selection, and routing design. We propose a novel Jointly Optimized Clustering Protocol (JOCP), which enhances cluster head selection by considering multiple critical factors that impact the IWSN life cycle. JOCP incorporates two key modules: the many‐objective clustering model and the double‐layer selection evolutionary algorithm. Specifically, the many‐objective clustering model considers cluster head selection from different perspectives, including maximum node survival cycle, minimum node distance, minimum network overall energy consumption, and balanced cluster energy consumption, with the aim of extending the network life cycle. Additionally, the double‐layer selection evolutionary algorithm optimizes the many‐objective clustering model to select appropriate cluster heads. Through performance verification, we demonstrate that the JOCP protocol effectively enhances the network life cycle and increases the number of surviving nodes compared to baseline clustering algorithms. Our research provides a comprehensive solution to the challenges associated with reliable and sustainable data transmission in large‐scale IWSNs, highlighting the potential for improved performance in industrial applications.

An AoTI-Driven Joint Sampling Frequency and Access Selection Optimization for Industrial Wireless Sensor Networks

The freshness of system information has attracted extensive attention and has become one of the most critical Quality of Service (QoS) indicators in industrial wireless sensor networks (IWSNs). For practical IWSNs composed of numerous components, the system freshness for a specific task is usually related to multiple and multitype sensing data. For example, in a chemical plant fire alarm system, the system controller (SC) cannot determine the fire notification until analyzing multiple types of ambient information, such as temperature, flame, carbon dioxide, and smoke. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. Therefore, to measure the system freshness accurately in IWSNs, we propose the Age of Task-oriented Information (AoTI) for industrial tasks. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to an industrial task. Considering the time-varying and wireless environments, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access selections and sampling frequencies for all sensors. We first formulate this problem as a Mixed Integer Nonlinear Programming (MINLP) problem and then transform it into a Constrained Markov Decision Process (CMDP), which is further relaxed as an MDP using the Lagrangian method. Finally, we develop a Learning-based Access selection and Sampling frequency Control (LASC) algorithm and verify its superiority through extensive simulations.

Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks

Security is an important factor that cannot be neglected in the design of time synchronization algorithms since industrial wireless sensor networks are prone to attacks against physical nodes and communication links. The Sybil attack is an intelligent attack with a high destructive capacity in pretending multiple identities and broadcasting illegitimate messages to destroy the network operation. Existing secure time synchronization algorithms mostly focus on distributed protocols; however, they pay less attention to Sybil attacks and centralized network time synchronization. In this paper, we propose a novel reference broadcast-based secure time synchronization (RSTS) for industrial wireless sensor networks with a time source against Sybil attacks. Different from previous protocols, in converging the network structure and the clock status, RSTS employs a public neighbor forwarding mechanism based on reference broadcast to filter the illegal time information automatically. Instead of establishing a table with timestamps of packet transmission and receipt, the least square linear regression is utilized to estimate the compensation relative to the source node with the recorded time and calculated time difference in receiving packets. The simulation results demonstrate that RSTS is resilient to Sybil attacks as well as message manipulation attacks in comparison with existing algorithms.

Codesign of Industrial Wireless Sensor Networks and Consensus-Based Sequential Estimation for Process Industries

Industrial wireless sensor networks (IWSNs) have been considered as promising technology to enhance target tracking and state monitoring in process industries with harsh environments. In this paper, the codesign of IWSNs and consensus-based sequential estimation (CSE) for typical long-belt process industries is proposed. Specifically, a group-based IWSNs deployment strategy is first designed to cover the transportation belt. Over the group-based IWSNs strategy, the CSE algorithm is then proposed to conduct target tracking and state estimation using distributed Kalman filter. To reveal the interrelation of IWSNs and CSE, the upper bound on the error violation probability of state estimation is first deduced from the sequential estimation process. Then, the algorithm is developed for the determination of IWSNs parameters (such as the number of groups of the IWSNs and communication design) and the CSE algorithm parameters (such as iterations of sequential estimation and estimation accuracy prediction). A case study of slab temperature monitoring over the hot strip milling process demonstrates the effectiveness of the proposed codesign of IWSNs and CSE.

Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

An Overview and Comparison of Wireless Technologies and Standards for Industrial Wireless Sensor Networks

The tremendous research and development has provided a number of wireless technologies and standards used for communication in Industrial Wireless Sensor Networks. The primary reason for writing this article is to study all available Wireless technologies and standards and to identify their area of usage, study different parameters like power consumption, functionality, communication range, transmission speed, data type etc. Another important and main reason of this article is to select best suitable technology for our next research. Various parameters of established technologies like Wireless HART, Zigbee, ISA 100.11a and Dash7 have been compared using a comparison table.

Analysis and Improvement of Authentication Schemes for Industrial Wireless Sensor Networks with Fog Computing

Cloud computing enables access to needed resources from a shared pool of configurable computing resources anytime, anywhere. Cloud computing offers many benefits, such as security and reliability of data and convenience of resource sharing. But as more and more devices are accessed, the demand for network bandwidth increases and it is because cloud computing centralizes all the resources that risks are also centralized. To overcome the shortcomings of cloud computing, the concept of fog computing has been introduced. Fog computing supports high mobility and has a wide geographical distribution, it also delivers data with very low latency. However, as an extension of cloud computing, fog computing inherits the security and privacy issues of cloud computing. Therefore, identity authentication in a fog computing environment is essential. In 2022, Sahoo et al. proposed an authentication scheme for industrial wireless sensor networks with fog computing. However, we analyze the security of Sahoo et al.’s scheme and find that many places in the data distribution phase are not clearly explained and their scheme is also not resistant to user impersonation attack, tracking attack, denial of service attack and replay attack. In order to overcome the weaknesses of Sahoo et al.’s scheme, this paper proposes an improved scheme by making full use of random numbers and timestamps. After security analysis and comparison with some similar schemes, it is shown that the improved scheme can resist various known attacks and has smaller computational cost.

Under Pressure: 3-D Indoor Airflow Monitoring Based on Differential Pressure Measurements

In industry, large amounts of energy are used for Heating, Ventilation and Air Conditioning (HVAC) systems to ensure good working conditions for human workers and machines alike. To decrease the energy used by existing HVAC systems while maintaining the predefined working conditions, knowledge about the actual conditions inside factories and other buildings is required. In this paper, we propose a low-cost 3D airflow sensor based on three off-the-shelf differential pressure sensors that can be integrated into an Industrial Wireless Sensor Network (IWSN) to collect data about the airflow inside factories and other buildings. The sensor enables the monitoring of emission propagation through buildings and, therefore, provides data to control HVAC systems based on the actual demand. The low-cost and simple design enables the deployment in high quantities to provide comprehensive data with a high spatial resolution. To demonstrate and evaluate the sensor, we present a custom-made wind tunnel as well as a real-world evaluation.

Fast and Low-Overhead Time Synchronization for Industrial Wireless Sensor Networks with Mesh-Star Architecture.

Low-overhead, robust, and fast-convergent time synchronization is important for resource-constrained large-scale industrial wireless sensor networks (IWSNs). The consensus-based time synchronization method with strong robustness has been paid more attention in wireless sensor networks. However, high communication overhead and slow convergence speed are inherent drawbacks for consensus time synchronization due to inefficient frequent iterations. In this paper, a novel time synchronization algorithm for IWSNs with a mesh-star architecture is proposed, namely, fast and low-overhead time synchronization (FLTS). The proposed FLTS divides the synchronization phase into two layers: mesh layer and star layer. A few resourceful routing nodes in the upper mesh layer undertake the low-efficiency average iteration, and the massive low-power sensing nodes in the star layer synchronize with the mesh layer in a passive monitoring manner. Therefore, a faster convergence and lower communication overhead time synchronization is achieved. The theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm in comparison with the state-of-the-art algorithms, i.e., ATS, GTSP, and CCTS.

Enhancing energy efficiency of IEEE 802.15.4- based industrial wireless sensor networks

The IEEE 802.15.4 standard protocol has been widely used in many industrial wireless sensor networks (IWSN), due to its low energy, optimal data rate and relative long-distance characteristics. The protocol operation, however, should be optimized to maximize efficiency and minimize interference between wireless sensor network (WSN) nodes. The protocol supports two media access modes: carrier sense multiple access (CSMA) and time division multiple access (TDMA). In this paper, we use the enhancing energy efficiency of wireless sensor networks (EEE-WSN) protocol that alternates between these two modes. The objective is to accommodate for traffic load and to produce better efficiency. EEE-WSN utilizes CSMA in the case of having lightweight traffic, which is normally the case when having normal and non-critical events in the area of interest (AoI). It switches operation in case of heavy traffic load conditions, which often happen when a critical event is detected. Furthermore, a modified selective activation (SA) technique is used to minimize the energy consumption and to reduce the interference between redundant nodes. The protocol has been implemented using Qualnet simulator and its performance has been evaluated for different simulation time, packet rate ratios, and sent packets. The simulation results showed that the EEE-WSN outperformed the IEEE 802.15.4 while operated in either CSMA or TDMA by decreasing the energy consumption by around 1%. The amount of energy saving is exponentially increasing as a function of the simulation time. The EEE-WSN has lower end-to-end delay by 0.4, 0.1 s under light traffic conditions when compared with IEEE 802.15.4- TDMA, and IEEE 802.15.4-CSMA modes, respectively. At heavy traffic conditions, the EEE- WSN has similar end-to-end delay to IEEE 802.15.4- TDMA, and less end-to-end delay values by 0.3 s when compared to IEEE 802.15.4- CSMA. Although IEEE 802.15.4- TDAM has lower packet loss values by 5% when compared to EEE-WSN under light traffic conditions and similar loss at heavy traffic conditions, EEE-WSN still has lower packet loss values by 5% and 25% when compared to IEEE 802.15. 4- CSMA, under light and heavy traffic conditions, respectively.