Industrial Wireless Sensor Networks Research Articles

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

A Multi-Layered Assessment System for Trustworthiness Enhancement and Reliability for Industrial Wireless Sensor Networks

A Multi-Layered Assessment System for Trustworthiness Enhancement and Reliability for Industrial Wireless Sensor Networks

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

Integrating Battery-Less Energy Harvesting Devices in Multi-Hop Industrial Wireless Sensor Networks

Integrating Battery-Less Energy Harvesting Devices in Multi-Hop Industrial Wireless Sensor Networks

Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures. The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG. This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.

Enhanced Smart Industry Wireless Sensor Networks for Smart Factory

Industrial Wireless Sensor Networks are increased in recent years due to provide real-time monitoring, decision-making and controlling. Industrial Wireless Sensor Networks used in many factories such as: food production, aircraft factory and pharmaceutical factory. The aim of this research is to focus on the Industrial Wireless Sensor Networks in smart factory and the challenges that need to be considered in use such as: its location, energy consumptions and security. The research discuss different issues are highlighted the importance of using real-time decision-making to improve the performance of the process and provide service monitoring for industrial process improvement.

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.

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

Supporting Ultra-Low-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

Supporting Ultra-Low-Power Nodes in 6TiSCH Industrial Wireless Sensor Networks

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.