Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

A Manufacturing Big Data Solution for Active Preventive Maintenance

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance

Energy efficiency is a significant problem in wireless sensor networks. Sensor networks are battery powered, therefore become a dead after certain period of time. In clustering technique, the cluster head selection is an essential issue. To moderate all above problems, this paper introduced adaptive fuzzy rule based energy efficient clustering and immune-inspired routing (FEEC-IIR) protocol for WSN assisted IoT system. For an optimal cluster head selection, adaptive fuzzy multi-criteria decision making approach (AF-MCDM) is used which is a combination of fuzzy AHP and TOPSIS method is introduced an energy efficient clustering algorithm. The criteria of energy status, QoS impact and node location are taken as the main factors that can influence the selection of cluster heads while each criteria contains some sub-criteria. For routing, immune-inspired optimization algorithm is used to enhance the data delivery reliability. The cluster based routing is an efficient manner to reduce the energy consumption. The experimental results show that our introduced approach improves the QoS parameters such as packet delivery ratio, packet loss ratio, throughput, network lifetime, end to end delay, channel load, jitter, bit error rate (BER), buffer occupancy and energy consumption compared with existing clustering and routing approach.

An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system

Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks.

A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks.

Unnamed aerial vehicles (UAVs) or drones have attracted growing interest in the last few years for multiple applications; thanks to their advantages in terms of mobility, easy movement, and flexible positioning. In UAV-based communications, mobility and higher line-of-sight probability represent opportunities for the flying UAVs while the limited battery capacity remains its major challenge. Thus, they can be employed for specific applications where their permanent presence is not mandatory. Data gathering from wireless sensor networks is one of these applications. This paper proposes an energy-efficient solution minimizing the UAV and/or sensors energy consumption while accomplishing a tour to collect data from the spatially distributed wireless sensors. The objective is to determine the positions of the UAV “stops” from which it can collect data from a subset of sensors located in the same neighborhood and find the path that the UAV should follow to complete its data gathering tour in an energy-efficient manner. A non-convex optimization problem is first formulated then, an efficient and low-complex technique is proposed to iteratively achieve a sub-optimal solution. The initial problem is decomposed into three sub-problems: The first sub-problem optimizes the positioning of the stops using linearization. The second one determines the sensors assignment to stops using clustering. Finally, the path among these stops is optimized using the travel salesman problem. Selected numerical results show the behavior of the UAV versus various system parameters and that the achieved energy is considerably reduced compared to the one of existing approaches.

Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering Using Unmanned Aerial Vehicles

For data collection in large-scale wireless sensor networks (WSNs), dynamic clustering provides a scalable and energy-efficient solution, which uses cluster head (CH) rotation and cluster range assignment algorithms to balance the energy consumption. Nevertheless, most existing works consider the clustering and routing as two isolated issues, which is harmful to the connectivity and energy efficiency of the network. In this paper, we provide a detailed analysis on the relations between clustering and routing, and then propose a joint clustering and routing (JCR) protocol for reliable and efficient data collection in large-scale WSN. JCR adopts the backoff timer and gradient routing to generate connected and efficient intercluster topology with the constraint of maximum transmission range. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analyses. The results show that the multihop routing in JCR may lead to the unbalanced CH selection. Then, the solution is provided to optimize the network lifetime by considering the gradient of one-hop neighbor nodes in the setting of backoff timer. Theoretical analysis and simulation results prove the connectivity and efficiency of the network topology generated by JCR.

Joint Clustering and Routing Design for Reliable and Efficient Data Collection in Large-Scale Wireless Sensor Networks

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in Sensors 2015, 15 26584 gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

https://www.mdpi.com/1424-8220/15/10/26583/pdf

Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

Due to the limited energy of wireless sensor nodes, the energy efficiency of data collection is a key issue in wireless sensor network (WSN). Dynamic clustering is a scalable and energy efficient solution for data collection in WSN. However, it suffers from the imbalanced energy consumption in the intercluster communication. To solve this problem, in this paper, we propose a distributed and adaptive routing protocol for cluster-based wireless sensor networks (DARC). In DARC, an adaptive energy threshold is proposed for the cluster head to control its intercluster routing mode, and a tunable cost function is designed for relay selection. Simulation results prove that DARC effectively balances the energy consumption in the intercluster communication and hence improves the energy efficiency and prolongs the network lifetime.

https://journals.sagepub.com/doi/pdf/10.1155/2015/627043

DARC: a distributed and adaptive routing protocol in cluster-based wireless sensor networks

For multi-hop data collection in wireless sensor network, a fundamental problem is to improve the energy efficiency with the constraint of network connectivity. The HCR protocol combines the gradient routing with dynamic clustering to provide connected and efficient inter-cluster topology with limited transmission range. However, it suffers from the imbalanced energy consumption which leads to the poor performance in network lifetime. In this paper, we analyze the energy consumption of HCR, and find that there is severe imbalanced energy consumption among the nodes in gradient k = 1, where the nodes can transmit their data to the sink directly. Based on this observation, we propose a new protocol termed as HCR-1, which uses the adaptive routing decision and the tunable cost function to balance the energy consumption of cluster heads. Simulation results show that HCR-1 protocol can effectively balance the energy consumption of nodes, and extend the network lifetime for over 30%.

A dynamic clustering and routing protocol for multi-hop data collection in wireless sensor networks

Dynamic clustering provides a scalable and energy efficient solution for data transmission in wireless sensor networks. Observing the fact that the cluster reorganization can not solve the problem of imbalanced energy consumption in the inter-cluster communication, we propose an adaptive routing strategy for cluster-based wireless sensor networks (ARC). The goal of ARC is to balance the energy consumption of nodes by adaptively controlling the modes of inter-cluster routing. In ARC, an adaptive energy threshold and a tunable cost function are designed for cluster heads to make routing decisions. Simulation results demonstrate that ARC effectively balances the energy consumption in the inter-cluster communication, and hence improves the energy efficiency and prolongs the network lifetime.

Liquan Chen

Papers

Joint Clustering and Routing Design for Reliable and Efficient Data Collection in Large-Scale Wireless Sensor Networks

Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

DARC: a distributed and adaptive routing protocol in cluster-based wireless sensor networks

A dynamic clustering and routing protocol for multi-hop data collection in wireless sensor networks

An adaptive routing strategy for cluster-based wireless sensor networks