Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches

With the success of wireless technologies and the number of wireless devices increasing, the wireless coverage footprint expands and removes the needs of wired networks for industrial applications. Wireless industrial sensor networks (WISNs) are a type of wireless sensor networks (WSNs), which dedicate to industrial applications. The WISNs bring several advantages over traditional wired industrial networks for detection and control, include flexibility, scalability, remote maintenance, and rapid deployment. However, some technical issues and routing design principles need to be noticed in terms of the stringent requirements of industrial applications. In this paper, we firstly introduce the characteristics and some routing issues of WISNs. Then we discuss existing routing protocols for WISNs and analysis their performances. Finally, we outline several future research directions.

Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N‐P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

Wireless industrial sensor networks are wireless sensor networks which have been adapted to industrial applications. Most techniques for wireless sensor networks can be applied to wireless industrial sensor networks. However, for industrial applications of wireless industrial sensor networks, new requirements such as real-time, reliable delivery need to be considered. In this paper, we propose EARQ, which is a novel routing protocol for wireless industrial sensor networks. It provides real-time, reliable delivery of a packet, while considering energy awareness. In EARQ, a node estimates the energy cost, delay and reliability of a path to the sink node, based only on information from neighboring nodes. Then, it calculates the probability of selecting a path, using the estimates. When packet forwarding is required, it randomly selects the next node. A path with lower energy cost is likely to be selected, because the probability is inversely proportional to the energy cost to the sink node. To achieve real-time delivery, only paths that may deliver a packet in time are selected. To achieve reliability, it may send a redundant packet via an alternate path, but only if it is a source of a packet. Experimental results show that EARQ is suitable for industrial applications, due to its capability for energy efficient, real-time, reliable communications.

The industrial wireless sensor network (IWSN) is a surface-type of wireless sensor network (WSN) that suffers from high levels of security breaches and energy consumption. In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this article, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data. The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy-efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.

In Industrial Wireless Sensor Networks (IWSNs), Sybil attacks compromise network topology and reduce data reliability by forging virtual nodes, leading to degraded network performance and significantly diminished monitoring accuracy. To address these issues, this study aims to propose a high-accuracy and highly robust Sybil attack detection method to overcome the limitations of traditional detection approaches, such as low precision and difficulty in handling ambiguous probability boundaries. The research designs a collaborative detection mechanism that integrates a CNN-BiLSTM-Attention (CBSA) deep learning module with the K-means clustering algorithm. By combining "multidimensional feature extraction via deep learning + clustering-based classification boundary optimization," an end-to-end Sybil attack detection model (CBSA-Kmeans) is constructed.The specific implementation includes four parts: 1. A Convolutional Neural Network (CNN) processes the raw sensor data matrix to extract spatial local patterns and capture abnormal correlation features among nodes. 2. A Bidirectional Long Short-Term Memory network (BiLSTM) processes the feature sequences output by the CNN. The forward LSTM learns the "past-present" temporal dependencies to identify the cumulative effects of attacks, while the backward LSTM models the "present-past" temporal correlations to trace attack origins. 3. An Attention mechanism is introduced to dynamically focus on key time steps corresponding to critical attack features, generating a weighted context vector and outputting attack probability predictions. 4. The K-means clustering algorithm is employed to perform secondary partitioning on the prediction probability space output by the CBSA module. By measuring Euclidean distances, high-density attack clusters and normal data clusters are constructed to form decision regions, thereby optimizing classification boundaries.Through a progressive approach of "spatial feature extraction → temporal dependency modeling and key feature enhancement → probability space clustering optimization," the model achieves attack detection: CNN first performs preliminary spatial feature screening, BiLSTM and Attention collaboratively mine temporal attack features and highlight critical information, and finally, K-means clusters the prediction probabilities to clarify the boundaries between attack and normal data. Experimental results demonstrate that the CBSA-Kmeans model excels in IWSN Sybil attack detection tasks: it achieves a detection accuracy of 98.2% and a recall rate of 96.7%, representing an improvement of over 12% compared to traditional detection methods. Additionally, the model has minimal negative impact on network performance, increasing IWSN network throughput by 23.5% and reducing data transmission latency by 31.8%, while effectively addressing the ambiguous probability boundary issue present in traditional methods. In conclusion, the CBSA-Kmeans model achieves high-precision and highly robust detection of Sybil attacks in IWSNs through the synergistic integration of deep learning and clustering algorithms, validating the effectiveness and superiority of this collaborative detection mechanism. This method provides a practical technical solution for IWSN security protection, ensuring network topology integrity and data transmission reliability while enhancing operational efficiency and monitoring accuracy. It holds significant practical application value for ensuring the secure and stable operation of wireless sensor networks in industrial settings.

Recently, industrial wireless sensor networks have attracted more attention, due to their large benefits in terms of faster installation, cost savings, and flexibility. Nevertheless, the acceptance of wireless sensor networks by the industrial community is not without its difficulties. In fact, several research efforts have been made in this field and a number of state-of-the-art reviews exist, presenting the current standards, the challenges, and the design principles. However, a comprehensive review on routing protocols appears to be missing. In this paper, we give a survey on routing protocols for industrial monitoring applications of the wireless sensor networks technology, and we present their limitations and weaknesses according to the industrial requirements.

Industrial Wireless Sensor Networks (IWSNs) have come to be widely used for collecting industrial information due to their low cost, rapid deployment, and high service quality. Clustering sensors together extends the lifetime of IWSNs is an essential part of improving their energy efficiency and network stability. In this paper, we propose a novel Multi-Objective Cluster Head Selection Optimization Model (MOCHSOM) to improve the selection of cluster heads by taking into account multiple factors critical to the IWSNs lifetime. Specifically, our model minimizes the total network energy consumption, balances the network cluster energy consumption, and maximizes the network node alive time. Moreover, we employ an Evolutionary Algorithm using Reference-point Based Non-dominated Sorting Approach (NSGA-III) to optimize the MOCHSOM model further. The experimental results show that our proposed algorithm significantly improves the network lifetime when compared with conventional clustering algorithms.

Industrial wireless sensor networks (IWSNs) are a key technology for smart manufacturing. To identify the performance bottlenecks in an IWSN before its real-world deployment, the IWSN must first be evaluated through simulations using an error model which accurately characterizes the wireless links in the industrial scenario within which it will be deployed. However, the traditional error models used in most IWSN simulators are not derived from the real traces observed in industrial environments. Accordingly, this study first measured the transmission quality of IEEE 802.15.4 in a one-day experiment in a manufacturing factory and then used the measurement records to construct a second-order Markov frame-level error model for simulating the performance of an IWSN. The proposed model was incorporated into the simulator of OpenWSN, which is an industrial WSN implementing the related IEEE and IETF standards. The simulation results showed that the proposed error model improved the accuracy of the estimated transmission reliability by up to 12% compared to the original error model. Moreover, the estimation accuracy improved with increasing burst losses.

With rapid adoption of advanced wireless sensors in last decades, industrial wireless sensor networks (IWSNs) are increasingly deployed in the industries for various applications. Sleep scheduling is a common approach in IWSNs to overcome network lifetime problem due to energy-constrained sensor nodes. However, in real-environment, node’s transmit power varies in different directions due to non-isotropic nature of electromagnetic transmission, path-loss, noise, and temperature. Thus, radio irregularity results in link asymmetry, thereafter, affects the performance of sleep scheduling in IWSNs. In this paper, we evaluate the impacts of radio irregularity on sleeping probability and lifetime performances of well-known connected k-neighborhood (CKN)-based sleep scheduling algorithms in duty-cycled IWSNs. We derive the upper-limit of sleep probability with radio irregularity variables. From the extensive simulations, we show that radio irregularity increases the number of awake nodes in duty-cycled sensor networks, therefore, network lifetime decreases with increasing values of link asymmetry parameters. Finally, an adverse impact of radio irregularity is observed in higher k-value in CKN-based algorithm due to more awake nodes to satisfy the k-connectivity in presence of link asymmetry.

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.

To attain a flawless real-time data transfer over a communication channel of Industrial Wireless Sensor Networks (IWSNs) is a difficult issue.The reason is that data transmission reliability degrades remarkably due to the dynamically changing network topology.Although TDMA-based protocols are widely used for real-time data transmission in IWSNs, they are not suitable for dynamically changing network topology.Therefore, we analyze the communication behavior of real-time multimedia data transmission and try to find out some performance enhancing factors.Then, we propose a slotted-sense scheduling scheme in which a big time slot is shared among the nodes at same tree-level.A sharable slot not only assures the time constraint but also improves the reliability of multimedia data greatly.Moreover, the block data transmission technique with an adaptive acknowledgement is proposed to optimize the size of a sharable slot.Finally, the experimental results show that our approach outperforms other standard MAC protocols for real-time multimedia data transmission in IWSNs.

In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authentication method, the convolutional neural network (CNN)-based sensor nodes’ authentication method, and the convolution preprocessing neural network (CPNN)-based sensor nodes’ authentication method, have been adopted to implement the PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires few computing resources and has extremely low latency, which enable a lightweight multi-node PHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm and minibatch skill are used to accelerate the training of the neural networks. Simulations are performed to evaluate the performance of each algorithm and a brief analysis of the application scenarios for each algorithm is discussed. Moreover, the experiments have been performed with universal software radio peripherals (USRPs) to evaluate the authentication performance of the proposed algorithms. Due to the trainings being performed on the edge sides, the proposed method can implement a lightweight authentication for the sensor nodes under the edge computing (EC) system in IWSNs.

Industrial wireless sensor networks (IWSNs) have become a viable solution for diverse application. However, commercial products and real-world deployments of IWSNs are faced with harsh reliability, real time, and predictability issues. The problem is more challenging in the narrow process industry. A novel two-tier wireless network consisting of subnetworks (FNs) and a backbone (BN) in the field is proposed in this paper. Along exploring time and frequency diversity, we present an optional polling slots allocation method in the FN to maximize communication reliability and integrity. Since time slots are scarce in the communication network for narrow process, we design a slot-reuse strategy with time slots consumption of [Formula: see text] to construct a N-hop multipath BN without violating industrial standard. Furthermore, a multicycle scheduling strategy for polling and convergecast is presented to reduce the workload over the BN. Performance analysis and simulations show that our solution outperforms traditional ones in terms of communication reliability and integrity.

Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next process stage. To this end, a consensus-based sequential estimation (CSE) framework is proposed which utilizes the co-design of IWSN and parameter state estimation. First, a group-based network deployment strategy, together with a TDMA (Time division multiple access)-based scheduling scheme is provided to track and sample the moving workpiece. Then, by matching to the tailored IWSN, the sequential estimation algorithm, which is based on the consensus-based Kalman estimation, is developed, and the optimal estimator that minimizes the mean-square error (MSE) is derived under the uncertain wireless communications. Finally, a case study on temperature estimation during the hot milling process is provided. The results show that the estimation error can be reduced to less than within a limited time period, although the measurement error can be more than in existing systems with a single-point temperature sensor.

In this paper an investigation of the State of the Art of Industrial Wireless Sensor Networks (IWSNs) is performed. Therefore the given industrial requirements, different wireless communication protocols, the defined basic architecture and open research questions will be analysed. Furthermore, current challenges from the research domain will be discussed and a real use case of IWSNs from a research project in cooperation with a local industrial partner will be evaluated. To conclude this paper an outlook of further developments and possible future applications of IWSNs will be sketched.