Industrial Wireless Research Articles

A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs

Routing requests in industrial wireless sensor networks (IWSNs) are always restricted by QoS. Therefore, finding a high‐quality routing path is a key problem. In this paper, a clone adaptive whale optimization algorithm (CAWOA) is designed for reducing the routing energy consumption of IWSNs with QoS constraints, and a novel clone operator is proposed. More importantly, CAWOA innovatively adopts a discrete binary‐based routing coding method, which provides strong support for optimal routing schemes. In addition, a novel routing model of IWSNs combined with QoS constraints has been designed, which involves comprehensive consideration of bandwidth, delay, delay jitter, and packet loss rate. Subsequently, in a series of simulations, the proposed algorithm is compared with other heuristic‐based routing algorithms, namely, whale optimization algorithm (WOA), simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA). The simulation results suggest that the CAWOA‐based routing algorithm outperforms other methods in terms of routing energy consumption, convergence speed, and optimization ability. Compared with GA, SA, PSO, and WOA under the conditions that the number of nodes is 120, the maximum delay is 120 ms, the maximum delay jitter is 25 ms, the maximum bandwidth is 9 Mbps, and the packet loss rate is 0.02; the energy consumption of CAWOA‐based routing is reduced by 12%, 17%, 19%, and 7%, respectively.

Coverage Evaluation for 5G Reduced Capability New Radio (NR-RedCap)

The fifth-generation (5G) wireless technology is primarily designed to address a wide range of use cases categorized into the enhanced mobile broadband (eMBB), ultra-reliable and low latency communication (URLLC), and massive machine-type communication (mMTC). Nevertheless, there are a few other use cases that are in-between these main use cases such as industrial wireless sensor networks, video surveillance, or wearables. In order to efficiently serve such use cases, in Release 17, the 3rd generation partnership project (3GPP) introduced the reduced capability NR devices (NR-RedCap) with lower cost and complexity, smaller form factor, and longer battery life compared to regular NR devices. However, one key potential consequence of device cost and complexity reduction is coverage loss. In this paper, we provide a comprehensive evaluation of NR RedCap coverage for different physical channels and initial access messages to identify the channels/messages that are potentially coverage limiting for RedCap UEs. We perform the coverage evaluations for RedCap UEs operating in three different scenarios, namely Rural, Urban and Indoor with carrier frequencies 0.7 GHz, 2.6 GHz and 28 GHz, respectively. Our results confirm that for all the considered scenarios, the amounts of required coverage recovery for RedCap channels are either less than 1 dB or can be compensated by considering smaller data rate targets for RedCap use cases.

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self‐organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP‐hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

Reformed timeslot allocation for data intensive clustered industrial wireless sensor networks using virtual grid structure with UWB

The data intensive industrial sensor networks produce excessive communication collision and introduce significant deficits. Further, a defined, appropriate scheduling methodology for transmission is desirable for mitigating the scarcity of such networks. With this declaration, here inclined a method for allocating conflict-free timeslots for transmitting the data in clustered sensor networks securing it from collision. This is lightened by partitioning the monitoring cluster regions into equal sized virtual grids and Latin square characteristics support scheduling in individual grids on ultra-wide band. This decentralised protocol follows an AODV routing and thereby each sensor is aware of the neighbouring nodes location and accepts the inherent topology changes. Moreover, the nodes that are participating in transmission remains in active state and the others become idle, thus conserving less energy. This distributed MAC scheduling method is particularly helpful in spatial usage of the communication channel for achieving scalability and efficiency and hence performance.

Multicast Scheduling in SDN WISE to Support Mobile Nodes in Industrial Wireless Sensor Networks

The growing use of mobile nodes in manufacturing environments is increasing the Quality of Service (QoS) requirements of the communications infrastructures that support them, as is the case with Industrial Wireless Sensor Networks (IWSN). Managing networks using Software Defined Networks (SDN) addresses the challenge of handling multiple sources in a satisfactory manner. The global vision of the topology that the SDN controller has allows a logical segmentation of the network, through the allocation of dedicated resources for each information flow, thus ensuring independent service levels and complete isolation. Mobile nodes produce constant changes in topology, which lead to instability for routing protocols. Traditional routing solutions with protocols such as Routing Protocol for Low-Power and Lossy Networks (RPL) require additional time to make the parent node change. This paper proposes Mobile Multicast Forwarding with Software Defined Network (MMF-SDN), an approach using Software Defined Networking solution for WIreless SEnsor Network (SDN WISE) protocol that exploits the advantages of SDN and Time Slotted Channel Hopping (TSCH) synchronism. The mobile nodes are managed as multicast sources, through a cumulative allocation of resources from the controller. This allows reception states to be synchronized at the parent nodes to provide network stability, avoiding subsequent recalculations in the routing protocol. Parent node changes are transparent and immediate. This proposal improves on other SDN based solutions, reducing energy consumption in reception by up to 50%, 70% end-to-end delay and improving scalability with a 30% reduction in slotframe occupancy.

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.

Mission-Critical Resource Allocation With Puncturing in Industrial Wireless Networks Under Mixed Services

As the manufacturing industry gradually expands and tends to be intelligent, the industrial networks have been greatly applied and are of paramount importance. Furthermore, taking the advantages of wireless networks into account, such as flexible deployment, low cost, and easy maintenance, etc., this paper is dedicated to the research of industrial wireless networks. Facing the problem of limited radio resource, in order to make full use of frequency band, this paper proposes a resource scheduling scheme with puncturing, which guarantees the reliability of subsequent ultra-reliable and low-latency communications (URLLC) services under the conditions of existing enhanced mobile broadband (eMBB) services. By optimizing the bandwidth pre-allocation for eMBB services, puncture weight and transmit power, this scheme can effectively meet the needs of the system. Specifically, the purpose of this paper is to minimize the decoding error rate of the devices carrying URLLC service while ensuring the demand for throughput of eMBB services. The problem proposed is a nonlinear stochastic optimization problem involving complex Q-function. The closed-form expression of the objective function is obtained through linear approximation and probability theory, which is further decoupled into three sub-problems. To this end, a block coordinate descent optimization (BCDO) algorithm is proposed to obtain the optimal bandwidth allocation, puncture weight and transmit power. The simulation results verify the rationality of the theoretical analysis and the effectiveness of the proposed algorithm, and also elaborate the influence of different parameters on the decoding error rate of the URLLC devices.

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.

Traffic-Adaptive CFP Extension for IEEE 802.15.4 DSME MAC in Industrial Wireless Sensor Networks

This paper presents a traffic-adaptive contention-free period (CFP) extension (TaCFPext) protocol for IEEE 802.15.4 deterministic and synchronous multichannel extension (DSME) medium access control (MAC), which aims to satisfy the traffic adaptability requirement of industrial wireless sensor networks (IWSNs). The legacy DSME standard has limitations in accommodating highly varying traffic load in IWSNs due to its fixed multi-superframe structure. TaCFPext enables a node to adaptively use a contention access period (CAP) as an extended CFP (extCFP) on the demands of traffic loads in a distributed manner. The node starts TaCFPext when it determines that the CFP of the current multi-superframe is insufficient to accommodate the traffic load. Then, the node selects a CAP to be used as an extCFP, on which it is allocated an extended guaranteed time slot (extGTS). When the traffic load decreases, the extGTS is deallocated before the GTS in CFP, and the extCFP returns to CAP again. An experimental simulation was performed to verify the superiority of TaCFPext. The results demonstrated that TaCFPext outperforms the legacy DSME for aggregate throughput and average delay under various traffic conditions.

A Hybrid Reliable Routing Algorithm Based on LQI and PRR in Industrial Wireless Networks

In Industrial Wireless Networks (IWNs), the communication through Machine‐to‐Machine (M2M) is often affected by the noise in the industrial environment, which leads to the decline of communication reliability. In this paper, we investigate how to improve route stability through M2M in an industrial environment. We first compare different link quality estimations, such as Signal‐Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Ratio (PRR), and Expected Transmission Count (ETX). We then propose a link quality estimation combining LQI and PRR. Finally, we propose a Hybrid Link Quality Estimation‐Based Reliable Routing (HLQEBRR) algorithm for IWNs, with the object of maximizing link stability. In addition, HLQEBRR provides a recovery mechanism to detect node failure, which improves the speed and accuracy of node recovery. OMNeT++‐based simulation results demonstrate that our HLQEBRR algorithm significantly outperforms the Collection Tree Protocol (CTP) algorithm in terms of end‐to‐end transmission delay and packet loss ratio, and the HLQEBRR algorithm achieves higher reliability at a small additional cost.

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified information model for production line devices and improve the connectivity of heterogeneous networks. However, in industrial wireless network application scenarios, there are various types of sensing information and large amounts of data to be transmitted. Each type of data has different requirements for real-time. The traditional OPC UA communication method is difficult to achieve real-time and reliable transmission in intelligent production lines and can’t meet the transmission requirements of time-sensitive data. In this paper, we propose a real-time communication model of the OPC UA wireless network for intelligent production lines (UAMPDS). IEEE 802.15.4e TSCH is used as the wireless communication infrastructure, and OPC UA protocols are fused to this model. Meanwhile, we design a load-aware time-slot scheduling algorithm to dynamically allocate and schedule time slots according to the network topology and traffic load of each node. We also implement a dynamic preemptive resource scheduling strategy based on service differentiation to ensure real-time transmission of time-sensitive data. The experimental results show that this model achieves differentiated services based on data with different latency tolerance and effectively reduces the transmission latency of real-time service data under the constrained network resources. It can avoid the starvation phenomenon of low priority queues, and improves the overall quality of service of intelligent production line communication networks.

A Robust Receiver Based on Chaos Modulation for the Industrial Internet of Things

Industrial wireless channels feature rich multipath components and strong noise. Massively deployed nodes in an industrial network are often cheap devices. Under such circumstances, the received packets are prone to errors. The conventional method for guaranteeing data quality relies on the MAC layer approach, such as retransmissions. However, this approach creates a data misalignment problem that degrades the performance of multidevice cooperation. Therefore, we propose a robust quadrature ergodic chaotic parameter modulation (QECPM)-based receiver to avoid retransmission. The proposed method does not require timing synchronization. This method eliminates the possibility of cycle slipping, which has a major effect on performance. The bit error rate (BER) performance of the proposed receiver in the Nakagami-m channel is derived and verified by simulation. Using the proposed receiver, the multipath effect can be mitigated using a single scalar. We use software-defined radios (SDRs) to show that the proposed method is robust against timing synchronization errors in practice. Furthermore, we show that as long as there are retransmissions, misaligned packets are to be expected; however, when using the proposed receiver, the error bits are sparse enough to utilize the non-retransmission mode to maintain stable link rates. Our results show that the proposed receiver is robust to multipath, timing synchronization errors and data misalignment.

Adaptive Secure Transmission Strategy for Industrial Wireless Edge-Enabled CPS

For real-time industrial wireless edge-enabled cyber-physical systems, this paper proposes an adaptive secure transmission mechanism under the large scale path loss to fight against randomly distributed illegal sensing devices. According to changes in the communication environment, the proposed scheme adaptively obtains the corresponding optimal power allocations and wiretap code rates. Specifically, by injecting artificial noise, an edge controller provides confidential control information for a legitimate wireless sensing device and achieves non-confidential data service for a service subscriber, simultaneously. Considering whether the secrecy and non-confidential service rate constraints are met, a dynamic secrecy transmission mechanism is introduced to minimize secrecy outage probability (SOP) based on random channel realizations. The optimal power allocation for minimizing the SOP is derived through the efficient alternating optimization algorithm with numerical methods. Numerical results show that the proposed adaptive secure transmission strategy can efficiently utilize transmit power to achieve non-confidential information traffic without compromising the secrecy performance.

Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

In this paper, we performed a series of measurement campaigns on the wireless electromagnetic noise for two typical industrial welding scenarios. On this basis, we investigate the characterization of the impulsive noise mainly from two aspects, that is, frequency and time domains. To start with, a novel denoising method based on the dynamic threshold is proposed to identify the desirable impulse noise from the background noise. Then, in the frequency domain, we focus on the power distribution of impulsive noise at different frequency bands. Results exhibit a shadow effect with regard to different frequency bands and we characterize it by using a linear function with a Gaussian distribution. Besides, analyses on the power spectrum correlation for different polarization modes and scenarios are also provided. In the time domain, we performed a series of statistical analyses from aspects of pulse amplitude, duration, and elapse interval to characterize the impulsive noise. Furthermore, three empirical distributions are employed to depict the parameters' variation tendency, that is, Cauchy distribution for amplitude, Gamma distribution for pulse duration, and exponential distribution for pulse interval. Finally, a first-order two-states Markov method is proposed to model the industrial noise. Simulation results are proved to be consistent with the actual measured results in terms of the amplitude distribution.

Network Value Drivers in a 5G World

5G upends many assumptions about the wireless industry. From the consumer perspective, speed, latency and number of connections will far surpass current expectations. From network operators’ perspective, integrating previously unusable mmW (mmW) and upper mid-band frequencies and significantly increased demand for backhaul will rearrange the relative value contribution of these network components. Traditional valuation relationships will change and will require updating in a 5G world, such as the economic relationships that drive spectrum and backhaul value. Although the rules of value creation have not changed, the realization of value creation is changing. In particular, based on FCC auction data, this paper empirically evaluates the factors that influenced value of traditional wireless spectrum and evaluates whether the same models can be applied to make value inferences for mmW spectrum bands, as well as the change in relative value between urban and rural geographies. We find that the factors that explain spectrum value for lower and mid-band spectrum are not the value drivers for the mmW spectrum, and do not perform well in explaining the geographic variation in value either. Based on this finding we investigate whether the strategic relationship between wired networks and wireless networks had greater complementarity for mmW than the traditional spectrum bands

Visible Light Communications for Industrial Applications—Challenges and Potentials

Visible Light Communication (VLC) is a short-range optical wireless communication technology that has been gaining attention due to its potential to offload heavy data traffic from the congested radio wireless spectrum. At the same time, wireless communications are becoming crucial to smart manufacturing within the scope of Industry 4.0. Industry 4.0 is a developing trend of high-speed data exchange in automation for manufacturing technologies and is referred to as the fourth industrial revolution. This trend requires fast, reliable, low-latency, and cost-effective data transmissions with fast synchronizations to ensure smooth operations for various processes. VLC is capable of providing reliable, low-latency, and secure connections that do not penetrate walls and is immune to electromagnetic interference. As such, this paper aims to show the potential of VLC for industrial wireless applications by examining the latest research work in VLC systems. This work also highlights and classifies challenges that might arise with the applicability of VLC and visible light positioning (VLP) systems in these settings. Given the previous work performed in these areas, and the major ongoing experimental projects looking into the use of VLC systems for industrial applications, the use of VLC and VLP systems for industrial applications shows promising potential.

Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks

Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.

Slow Surface Acoustic Waves via Lattice Optimization of a Phononic Crystal on a Chip

Strategically reducing the speed of waves, which greatly improves both the energy density and information capacity of carrier signals in space, is a key enabling factor for signal-processing devices. Among these devices, especially in the prosperous wireless communication industry, surface acoustic wave (SAW) devices based on interdigital transducers (IDTs) currently hold an essential status. However, velocity reduction in traditional IDT-based SAW devices can be achieved only by using specific substrate materials that are generally of lower hardness, which inevitably leads to an increase in device size and less-optimal electromechanical coupling coefficients. Here, we demonstrate a technological means of realizing slow on-chip SAWs that is relevant for practical rf signal processing, gyrometers, sensing, and transduction. This method takes advantage of the gradual flattening of a Rayleigh-type dispersion band due to the spatial lattice evolution of a surface phononic crystal. In our experiment, the speed of an ultraslow SAW is measured to be approximately 200 m/s, which is even slower than the speed of sound in air and equivalent to 1/17.4 of the speed of the original Rayleigh waves in ${\mathrm{Li}\mathrm{Nb}\mathrm{O}}_{3}$. Such ultraslow SAWs may have promising applications in time-dependent SAW modulation, high-sensitivity SAW sensors, and SAW nonlinear even quantum-dynamic systems. Additionally, our technique can be similarly applied to a broad range of other two-dimensional or quasi-two-dimensional wave structures, e.g., in electronic, optical, acoustic, and thermal systems.

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

SummaryReal‐time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO‐SVM) as a binary classifier with optimally pruned extreme learning machine (OP‐ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance‐based lightweight K‐Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.