A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Towards Flexible and Cognitive Production—Addressing the Production Challenges

Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity

Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment

Smart Grids (SGs) constitute the evolution of the traditional electrical grid towards a new paradigm, which should increase the reliability, the security and, at the same time, reduce the costs of energy generation, distribution and consumption. Electrical microgrids (MGs) can be considered the first stage of this evolution of the grid, because of the intelligent management techniques that must be applied to assure their correct operation. To accomplish this task, sensors and actuators will be necessary, along with wireless communication technologies to transmit the measured data and the command messages. Wireless Sensor and Actuator Networks (WSANs) are therefore a promising solution to achieve an intelligent management of MGs and, by extension, the SG. In this frame, this paper surveys several aspects concerning the application of WSANs to manage MGs and the electrical grid, as well as the communication protocols that could be applied. The main concerns regarding the SG deployment are also presented, including future scenarios where the interoperability of different generation technologies must be assured

The design and evaluation of Wireless Sensor Networks for applications in industrial locations

In manufacturing industries, there exist many applications where Wireless Sensor Networks (WSN\u27s) are integrated to provide wireless solution for the automated manufacturing processes. It is well known that industrial environments characterized by extreme conditions such as high temperature, pressure, and electromagnetic (EM) interference that can affect the performance of the WSN\u27s. The key solution to overcome this performance issue is by monitoring the received Signal Strength Index (RSSI) at the received sensor of the WSN device and track frame error rate of wireless packets. ZigBee is a wireless sensor network (WSN) standard designed for specific needs of the remote monitoring sensor system. Zigbee networks can be established by three different topologies: start, hybrid, and mesh. In this research project, the interest in analyzing the characteristics of the Zigbee performance was completed using a star topology network. Three performance parameters were obtained: the RSSI signal to monitor the received wireless packets from the sending node, path-lost exponent to determine the effect of industrial environment on wireless signals, and the frame error rate to know the discontinue time. The study was in three phases and took place in two settings: The first was at the manufacturing laboratory at the University of Northern Iowa, the second and the third were at the facility of a Midwestern manufacturing company. The study aimed to provide an analytical tool to evaluate the performances of Zigbee networks in industrial environments and compare the results to show that harsh environments do affect its performance. The study also involved testing the performance of WSN. This was done by simulating input/output Line passing with digital and analog data. Packets were sent from one node and counted at the receiving side to measure the packet error rate of WSN in industrial environment. In conclusion, investigating the WSN\u27s systems performance in industrial environment provides is crucial to identify the effects of the harsh conditions. It is necessary to run similar investigation to prevent the malfunction of the manufacturing applications. Testing a simple WSN in industrial environment can be capable of predicting the performance of the network. It is also recommended to have an embedded approach to WSN applications that can self-monitor its performance

Design and Empirical Validation of a Bluetooth 5 Fog Computing Based Industrial CPS Architecture for Intelligent Industry 4.0 Shipyard Workshops

[Abstract] Navantia, one of largest European shipbuilders, is creating a fog computing based Industrial Cyber-Physical System (ICPS) for monitoring in real-time its pipe workshops in order to track pipes and keep their traceability. The deployment of the ICPS is a unique industrial challenge in terms of communications, since in a pipe workshop there is a significant number of metallic objects with heterogeneous typologies. There are multiple technologies that can be used to track pipes, but this article focuses on Bluetooth 5, which is a relatively new technology that represents a cost-effective solution to cope with harsh environments, since it has been significantly enhanced in terms of low power consumption, range, speed and broadcasting capacity. Thus, it is proposed a Bluetooth 5 fog computing based ICPS architecture that is designed to support physically-distributed and low-latency Industry 4.0 applications that off-load network traffic and computational resources from the cloud. In order to validate the proposed ICPS design, one of the Navantia’s pipe workshops was modeled through an in-house developed 3D-ray launching radio planning simulator that allows for estimating the coverage provided by the deployed Bluetooth 5 fog computing nodes and Bluetooth 5 tags. The experiments described in this article show that the radio propagation results obtained by the simulation tool are really close to the ones obtained through empirical measurements. As a consequence, the simulation tool is able to reduce ICPS design and deployment time and provide guidelines to future developers when deploying Bluetooth 5 fog computing nodes and tags in complex industrial scenarios.Auto-ID for Intelligent Products research line of the Navantia-UDC Joint Research Unit (Grant Number: IN853B-2018/02) 10.13039/100014440-Ministerio de Ciencia, Innovaci??n y Universidades (Grant Number: RTI2018-095499-B-C31