Industrial Wireless Sensor Networks Research Articles

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.

Investigation of IoT-Integrated Smart Homes

The growth of internet-connected services, known as the Internet of Things (IoT), has led to a proliferation of new applications. One such application is the smart home, where household appliances and devices can be remotely monitored and controlled. To achieve this, appropriate network architecture and standard protocols are used to connect various devices to the internet, resulting in an "IoT-based smart home." However, managing and regulating the entire system, as well as ensuring the security of servers and smart homes, present challenges. This paper presents an IoT architecture and discusses the issues and difficulties faced by IoT-enabled smart home systems while also proposing potential solutions. Smart homes simplify home automation tasks and offer greater convenience to users. The Industrial Wireless Sensor Network (WSN) has already demonstrated the potential of IoT, and the integration of IoT into smart homes is a logical next step. The article explores various aspects of IoT-based smart homes and highlights the need for proper management and security protocols. In conclusion, the study investigates the integration of IoT into smart homes, highlighting the challenges and solutions associated with the development of an IoT-based smart home system. The objective is to provide a framework for the development and management of IoT-based smart homes that will enhance the quality of life for users.

Design and Analysis of 1x4 and 1x8 Circular Patch Microstrip Antenna Array for IWSN Application

A 1x4 and 1x8 circular micro strip patch antenna array operating at 2.4GHz are simulated for Industrial Wireless Sensor Network (IWSN) applications using CST studio suite and MATLAB based simulator software. The circular patch antenna designed on a FR4 substrate with dielectric constant of 4.4 and a substrate height of 1.6mm is fed using inset-fed technique. Several antenna characteristics such as return loss, radiation pattern, bandwidth, directivity, antenna gain, radiation efficiency isanalyzed. The compact size and higher directivity help to improve the network performance by alleviating contention, increasing communication range, and mitigating interference of the system. Hence, the advantages gained, make the system more useful for IWSN applications as achieved in this research

AoI-Bounded Scheduling for Industrial Wireless Sensor Networks

Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of the entire system. However, in practical industrial applications, optimizing the average AoI does not guarantee that the peak AoI of each data packet is within a bounded interval. If the AoI of certain packets exceeds the predetermined threshold, it can have a significant impact on the stability of the industrial control system. Therefore, this paper studies the scheduling problem subject to a hard AoI performance requirement in IWSNs. First, we propose a low-complexity AoI-bounded scheduling algorithm for IWSNs that guarantees that the AoI of each packet is within a bounded interval. Then, we analyze the schedulability conditions of the algorithm and propose a method to decrease the peak AoI of nodes with higher AoI requirements. Finally, we present a numerical example that illustrates the proposed algorithm step by step. The results demonstrate the effectiveness of our algorithm, which can guarantee bounded AoI intervals (BAIs) for all nodes.

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme.

RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks

Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers’ industrial applications to implement timecoordinated actions.

Accurate Phase Detection for ZigBee Using Artificial Neural Network

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK’s phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging machine learning and clustering algorithms, sensing and computing technologies, and image processing tools, and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual perception and environment mapping algorithms. A Shiny app was harnessed for Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to configure the flow diagram integrating evidence-based gathered and processed data (the search outcomes and screening procedures). A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out throughout June and October 2022, with search terms including “Internet of Robotic Things” + “remote big data management tools”, “sensing and computing technologies”, and “visual perception and environment mapping algorithms”. Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have been deployed as data extraction tools for literature collection, screening, and evaluation, for document flow monitoring, for inspecting qualitative and mixed methods research, and for establishing robust outcomes and correlations. For bibliometric mapping by use of data visualization, Dimensions AI was leveraged and with regards to layout algorithms, VOSviewer was harnessed.

Timely Communications With and Without Relaying and Buffering

In this article, we consider the timeliness of information transmissions in a three-node industrial wireless sensor network (IWSN) in terms of Age of Information (AoI). In this network, a sensor monitors the ambient environment and transmits the sensed information to a remote monitor directly or through a relay node. In particular, we are interested in how the timeliness of the system is changed by decomposing the long-distance transmission with a relay and by enabling parallel transmissions over the two hops with a packet buffer. To this end, we derive the average AoIs of the transmissions over the direct-link, the relay-links with and without a buffer in a closed form. The obtained results show that the relay-link with a buffer outperforms the other two links, while the relay-link without a buffer outperforms the direct-link only if the relay is properly placed and the sensor–monitor distance is relatively large. On the condition that the average transmission times over the direct-link and the relay-link without a buffer are equal, we further evaluate how fast the average AoI can be reduced by using a relay or a packet buffer, as the packet rate approaches the maximum feasible rate over the links. It is shown that, although the sensor–monitor distance dominates the average AoIs of the links, the gains of using the relay and the buffer do not change much with the distance and are approximately constant.

QEGWO: Energy-Efficient Clustering Approach for Industrial Wireless Sensor Networks Using Quantum-Related Bioinspired Optimization

Compared with conventional wireless sensor networks (WSNs), industrial WSNs (IWSNs) have stricter requirements in real-time data transmission, energy consumption, and energy uniformity. To fulfill these requirements, a new energy-efficient clustering approach using quantum-related bioinspired optimization, i.e., quantum elite gray wolf optimization (QEGWO), is proposed to improve the performance of IWSNs. Innovatively, a new quantum operator, including quantum probability amplitude, quantum rotation gate, and quantum NOT gate, is designed in QEGWO to enhance its global search capability. This quantum operator need not query the quantum rotation angle table in updating quantum probability amplitude with the quantum rotation gate, which reduces the computational complexity of introducing quantum optimization into the clustering problem of IWSNs. Moreover, to enhance the convergence performance of the clustering algorithm, a multielite strategy is proposed to preserve the historical optimal individuals generated in the iterative process by establishing a dynamic elite pool. Compared with the state-of-the-art clustering approaches, extensive simulations in four different scenarios are carried out, and the results demonstrate that the proposed QEGWO outperforms other comparison approaches in delay, energy consumption, and energy uniformity.

Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing

Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN).

Industrial functional safety assessment for WSN using QoS metrics

Wireless Sensor Networks are increasingly getting deployed for the safety use cases in industrial applications. While several research papers discuss about the Quality & Reliability improvement techniques in WSN systems to achieve minimal delay, higher node life, optimal routing etc., very limited work is witnessed on assessment of safety integrity levels of WSN systems. In this paper we tried to bridge this gap by bringing out a QoS metric-based safety integrity assessment for the end-to-end industrial Wireless Sensor Network (WSN) system. To identify relevant QoS metrics for monitoring the safety integrity levels, we also bring out a 4-step mapping methodology to link the QoS metrics and communication defenses/safety mechanisms. This mapping approach is expected to serve the network safety design engineers. Finally, a simulation case example is discussed to illustrate safety integrity assessment and we conclude by bringing out future research opportunities to improve safety integrity levels of industrial WSN systems.