Industrial Wireless Research Articles

A Newborn Particle Swarm Optimization Algorithm for Charging-Scheduling Algorithm in Industrial Rechargeable Sensor Networks

The Industrial Wireless Rechargeable Sensor Network (IWRSN) is a sensor network used in industrial environments. In order to ensure a certain intensity of industrial monitoring and real-time industrial control, the network is equipped with mobile charger to supplement the energy for sensors according to the charging schedule. Because of the complexity of industrial environment, the monitoring area is firstly divided into grids and established a set of paths that can be driven by mobile chargers. On this basis, a newborn particle swarm optimization (NPSO) charging scheduling algorithm is proposed for the constraint of node working time window. The NPSO algorithm borrows the idea of fireworks algorithm to introduce newborn particles into the population, and improves the convergence speed of the algorithm, then applies it to the charging scheduling process. The NPSO charging algorithm firstly plans the initial scheduling path for the node that needs to be priority charging. The remaining nodes to be charged are then designed to search for the location of the initial path near their position and update the time window of the subsequent charging node. The simulation results show that the proposed newborn particle swarm optimization charging scheduling algorithm has superiority in energy utilization and node mortality compared with the existing charging scheduling algorithm.

Dragonfly approach for resource allocation in industrial wireless networks

This paper presents a novel nature inspired dragonfly based, optimization paradigm, named as dragonfly search convergence and coverage algorithm (DSCCA) for resource allocation in an industrial wireless network. This paradigm imitates the swarming behavior of the dragonfly for optimized and efficient resource allocation to fulfill multi-objective of better throughput, low error probability, and low latency. The robustness as well efficiency of the proposed DSCCA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon’s test and ANOVA on classical as well as modern IEEE CEC 2014 benchmark functions. For multi- as well conflicting objectives, DSCCA is evaluated using Pareto front. An extensive comparative analysis is carried out to exhibit the effectiveness of DSCCA over other well established optimization algorithms in terms of accuracy, convergence rate and efficiency. The results clearly shows that DSCCA provides more accurate solutions with high convergence rate as compared to other optimization algorithms. Along with this, DSCCA in compared with multi-objective resource management in terms fairness in the allocation of resources and convergence rate. Moreover, computational complexity for DSCCA is also evaluated and its adoption in realistic industrial wireless networks is also discussed. Comprehensive tests demonstrate that the methodology proposed contributes to fairer allocation of resources with lower complexity and the results are pragmatic, so it is easily extrapolated to real time execution in industrial wireless networks.

Advanced design of higher mode‐based dielectric resonator antenna array featuring high‐gain operation over a large frequency band

AbstractA higher mode‐based dielectric resonator antenna (DRA) featuring attractive characteristics has been explored and successfully demonstrated for use in wireless industry. In here, an improvised elevated feed platform has been conceived to exploit HEM12δ mode in a cylindrical DRA. An additional filtering mechanism by a selective loading has been introduced to achieve a clean and exclusive HEM12δ resonance. A single unit has been characterized at C‐band based on the available characterization facility and experimentally verified ensuring 22% wide matching bandwidth with as much as 11 dBi peak gain. A practical 2 × 2 array with a corporate feed has been successfully demonstrated. A consistent radiation pattern over about 10% matching bandwidth has been experimentally demonstrated. This ensures a flat peak gain (13‐14 dBi) over the full usable frequency band. This is novel in terms of its pure higher mode operation supporting a wireless system with uniform gain characteristics. Its improved features compared to the earlier designs have been clearly discussed.

Internet of things based hybrid cryptography for process data security

This manuscript addresses security issues, security mechanisms of Internet of Things (IoT) and implementation of hybrid cryptography for preserving the process information against unauthorized access and modification. Modern industrial devices use smart sensors, intelligent controllers, smart transmitters and wireless networks for process monitoring and control applications. The industrial networks are integrated with the information technology (IT) networks, which allow the process monitoring through the internet. Various software algorithms are available for data security, but hackers break the security and corrupt the industrial data. The corrupted data alter the industrial process activity. It is essential to have security algorithms with hardware to protect the data. In this proposed work, a novel hybrid secured wireless algorithm is developed and implemented in real-time using an embedded system. This proposed security algorithm uses 128-bit encryption and decryption as well as hash value generation along with an embedded system, which becomes transceiver for the industrial wireless network. This proposed security algorithm enables alerts and assures the security for monitoring of industrial processes through the internet.

A Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a <i>hierarchical soft margin support vector machine</i> training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ( <inline-formula><tex-math notation="LaTeX">$\approx 9\%$</tex-math></inline-formula> ).

Blockchain-based anomaly detection of electricity consumption in smart grids

Abstract The big data generated by Industry 4.0 is expected to increase 20-fold in the next ten years and it has raised various challenges in Industrial Wireless Sensor Networks (IWSNs). Among these challenges, detecting different types of anomalies of industrial electricity consumption in an accurate and timely manner is a priority. If not handled properly, these anomalies could lead to serious consequences, such as irregular fire and paralyzed power system components. While existing anomaly detection techniques may be efficient for old systems, they are now faced with big transmitted data. Therefore, it is important to design new methods that can detect the electricity consumption anomaly and carry out appropriate actions. In this article, we first review several existing work on anomaly detection schemes, and then introduce the system and monitoring models. Then, we present a new framework that aims to detect electricity consumption anomalies accurately and timely using sensor processing, smart meter readings, machine learning and blockchain.

Cooperative Jamming Secure Scheme for IWNs Random Mobile Users Aided by Edge Computing Intelligent Node Selection

The cooperative physical layer security scheme can enhance the communication security of industrial wireless nodes that are computing and energy limitation, which provides the lightweight security for future industrial wireless networks (IWNs). By aiding an edge computing device, the optimal cooperative jamming (CJ) node can be selected, and employed to collaboratively transmit jamming signals to maximumly weaken the quality of the wiretap channel. For overcoming the drawback of the existing work that only studies the physical layer security in static scenes, a practical scenario where legitimate user and eavesdropper move randomly is considered in this article. The ergodic secrecy capacity with CJ in random mobile scenes is derived. An edge computing device is employed to make intelligent selection of an optimal cooperative node. Test and verification are carried out in the industrial factory. The test results show that the novel scheme can improve the secrecy of random mobile users in the IWNs.

Experimental Assessments and Analysis of an SDN Framework to Integrate Mobility Management in Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSN) are expected to support in the near future a wide range of applications in which mobile nodes, for example, robots or humans, interoperate between them and with machinery. However, the real-time constraints imposed by the industrial processing make mobility support quite challenging. This article proposes an approach based on software-defined networking (SDN), herein named forwarding and time-slotted channel hopping scheduling over SDN (FTS-SDN), that handles transmission scheduling and node mobility in IWSN providing bounded end-to-end delays. The article presents the detailed design of the FTS-SDN and the implementation on commercial-off-the-shelf devices. In addition, the FTS-SDN performance are extensively discussed, providing simulation results and experimental measurements obtained in a real scenario.

EERS: Energy-Efficient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks.

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.

A Measurement-Based Frame-Level Error Model for Evaluation of Industrial Wireless Sensor Networks.

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.

Reliable uplink transmissions for NOMA-based Industrial Wireless Networks with guaranteed real-time performance

Reliability is vital for ultra-reliability low-latency transmission applications in Industrial Wireless Networks (IWNs). The power-domain Non-Orthogonal Multiple Access (NOMA) technology can support multiple parallel transmissions, and has been thought of as one of the most powerful candidate radio access technology for the next-generation IWNs. However, it suffers from low transmission reliability because of the high interferences caused by parallel transmissions in power-domain NOMA. In this paper, given the real-time performance requirements, we consider a single-hop network supporting 2-Successive Interference Cancellation, and study how to maximize the reliabilities of uplink transmissions by the joint user pairing and power allocation. We show that the problem is solvable in polynomial time by an optimal algorithm with complexity of O(nlogn), where n is the number of users. The performance evaluations reveal that the transmission reliabilities will increase exponentially with the linear degradation of the guaranteed real-time performance.

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

AbstractIntegral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

Relay node placement for building wireless sensor networks with reconfigurability provision

Intelligent manufacturing needs the flexible and reconfigurable manufacturing systems to respond to dynamic market demands rapidly, which requires the underlying information systems (e.g., industrial wireless sensor networks) to provide reconfigurability. The Relay Node Placement (RNP) problem has aroused great attention due to its significant influence on network performance. However, existing works assume that the network configurations (e.g., transmit power, delay and reliability requirements) are fixed, and therefore cannot guarantee the reconfigurability of the deployed Wireless Sensor Networks (WSNs). To this end, this paper studies the RNP problem with reconfigurability provision. We propose an RNP framework with supporting network reconfiguration, and we also prove that this framework can ensure a deterministic approximation ratio and a tight time complexity. We also verify the efficiencies of the proposed algorithms through extensive simulations, and results show the superiorities of the proposed algorithms on deployment cost, end-to-end delay and reliability.

A Spatial Group-Based Multi-User Full-Duplex OFDMA MAC Protocol for the Next-Generation WLAN.

The Wireless Local Area Network (WLAN) has become a dominant piece of technology to carry wireless traffic for Internet of Things (IoT). The next-generation high-density WLAN scenario is very suitable for the development trend of the industrial wireless sensor network. However, in the high-density deployed WLAN scenarios, the access efficiency is low due to severe collisions, and the interference is diffused due to the scattered locations of the parallel access stations (STAs), which results in low area throughput, i.e., low spatial reuse gain. A spatial group-based multi-user full-duplex orthogonal frequency division multiple access (OFDMA) (GFDO) multiple access control (MAC) protocol is proposed. Firstly, the STAs in the network are divided into several spatial groups according to the neighbor channel sensing ability. Secondly, a two-level buffer state report (BSR) information collection mechanism based on P-probability is designed. Initially, intra-group STAs report their BSR information to the group header using low transmission power. After that, group headers report both their BSR information collected from their members and inter-group interference information to the access point (AP). Finally, AP schedules two spatial groups without mutual interference to carry on multi-user full duplex transmission on the subchannels in cascading mode. The closed-form formulas are theoretically derived, including the number of uplink STAs successfully collected by AP, the network throughput and area throughput under saturated traffic. The simulation results show that the theoretical analysis coincide with the simulation results. The system throughput of the GFDO protocol is 16.8% higher than that of EnFD-OMAX protocol.

W-SHARP: Implementation of a High-Performance Wireless Time-Sensitive Network for Low Latency and Ultra-low Cycle Time Industrial Applications

Real-time Industrial applications in the scope of Industry 4.0. present significant challenges from the communication perspective: low latency, ultra-reliability, and determinism. Given that wireless networks provide a significant cost reduction, lower deployment time, and free movement of the wireless nodes, wireless solutions have attracted the industry attention. However, industrial networks are mostly built by wired means because state-of-the-art wireless networks cannot cope with the industrial applications requirements. In this article, we present the hardware implementation of wireless SHARP (w-SHARP), a promising wireless technology for real-time industrial applications. w-SHARP follows the principles of time-sensitive networking and provides time synchronization, time-aware scheduling with bounded latency and high reliability. The implementation has been carried out on a field-programmable gate array-based software-defined radio platform. We demonstrate, through a hardware testbed, that w-SHARP is able to provide ultra-low control cycles, low latency, and high reliability. This implementation may open new perspectives in the implementation of high-performance industrial wireless networks, as both PHY and MAC layers are now subject to be optimized for specific industrial applications.

Real-time scheduling under heterogeneous routing for industrial Internet of Things

Industrial Internet of Things (IIoT) provide a promising opportunity for building efficient industrial wireless systems by leveraging the growing ubiquity of sensor nodes. However, IIoT exhibits strict reliability and has a high requirement for real-time communications. Furthermore, different criticality level flows are coexisted in many IIoT. The system must guarantee the reliability and real-time performance of high-criticality flows to avoid disasters. To address this issue, this paper first proposes a heterogeneous routing model in which both source and graph routing coexist. Then, we propose Relative-execution Deadline First (RDF) scheduling and prove it has a better performance than the fixed priority (FP) and earliest deadline first (EDF) methods. By extending RDF in mixed-criticality IIoT, a Mixed-Criticality Relative-execution Deadline scheduling algorithm (MCRD) has proposed, which can further improve system schedulability by making a trade-off between reliability and real-time performance. Simulation and experiment results show that our approach outperforms the existing methods.

Learning-Based Autonomous Scheduling for AoI-Aware Industrial Wireless Networks

Due to the ever-increasing time-sensitive industrial applications, critical-machine type communication (C-MTC) is a promising technique for timely delivery services in industrial wireless networks (IWNs), where vicinal devices can benefit from device-to-device (D2D) communication for low power consumption and latency. For real-time applications, Age of Information (AoI) is an essential metric that represents the freshness of data from the perspective of destinations. Thus, an AoI orchestration agent for link scheduling in D2D-enabled IWNs is needed. Most existing works on AoI deal with this scheduling in a centralized manner, which cannot afford timely packet delivery requirements for numerous D2D devices. Different from the existing works, a learning-based autonomous AoI and power orchestration agent, namely, L-AoI, is proposed for D2D-enabled IWNs in this article, where D2D devices adaptively compete for wireless resources in a distributed manner. As a result, the global channel state information as well as the actions of other D2D devices are unknown. Hence, D2D devices deal with this uncertainty under the guidance of L-AoI so that AoI constraints can be respected. By leveraging from the belief-based Bayesian reinforcement learning, L-AoI learns the scheduling action profile with strategies of other D2D devices considered so that the spectrum sharing coalitions can be intelligently formed. Both theoretical analysis and simulation are provided to validate the performance of L-AoI in terms of AoI stability and violation ratio.

Industrial wireless channel measurements in a 2.4 GHz ISM radio band using a low-cost SDR-based channel sounder

Industrial wireless channel is a challenge for the design of communication systems, due to non-Line-of-Sight transmission, caused by the presence of many highly reflective obstacles, and machines in operation, which are a source of the increased noise level. The main effect, which must be analyzed, is multipath propagation. In this article, a low-cost sounding system is proposed, based on Software Defined Radio (SDR) equipment, with the intention of making sounding devices more accessible to a larger group of researchers. Likewise, the mathematical foundations and the software/hardware implementation of the wireless channel sounding system are presented, and the solutions to mitigate the synchronization issues and SDR limitations are also introduced. The performance of the proposed sounder is validated through a measurement campaign in an industrial workshop, considering the 2.4 GHz Industrial, Scientific, Medical (ISM) band. Channel sounding measurements corroborate the accuracy of the results, which converge with the channel mathematical models proposed for several industrial environments and reported in the state-of-the-art literature. In this sense, the proposed channel sounder can be used to investigate the wireless propagation environments.

A High-Accuracy Least-Time-Domain Mixture Features Machine-Fault Diagnosis Based on Wireless Sensor Network

Fault diagnosis of rolling bearing plays a vital role in identifying incipient failures and ensuring the reliable operation of the mechanical system. To improve the performance of the whole machine-fault-diagnosis system and meet the requirements of low cost, low consumption, high-reliability in industrial wireless sensor networks (IWSNs), a high-accuracy least-time-domain features fault diagnosis algorithm based on the BP neural network (BPNN) for IWSNs is proposed in this article. First, the hardware of wireless multifeatures extraction sensor node is designed, which performs local-processing features extraction of four-dimensional parameters and five dimensionless features of the vibration signal. Then, the bearing-fault classification based on mentioned characteristics is investigated in the proposed BPNN with different hidden layer nodes. Furthermore, we make the comparisons of bearing-fault classification accuracy in terms of varying number of dimensional features, dimensionless features, and the combination features, searching a least-time-domain mixture features selection strategy for ensuring high-fault classification accuracy and proving the effectiveness and feasibility of the proposed method by experiments on drivetrain diagnostics simulator system. This article is conducted to provide new insights into how to select the least time-domain features for high-accuracy fault diagnosis and further giving references to more IWSNs scenarios.