Deep Learning in Industrial Internet of Things: Potentials, Challenges, And

1 Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications Ruhul Amin Khalil Student Member, IEEE, Nasir Saeed Senior Member, IEEE, Yasaman Moradi Fard, Tareq Y. Al-Naffouri, Senior Member, IEEE, Mohamed-Slim Alouini, Fellow, IEEE Abstract—The recent advancements in the Internet of Things digital transformation in many industries is accelerating [12]– (IoT) are giving rise to the proliferation of interconnected [18]. With strong alliances between leading IIoT stakeholders devices, enabling various smart applications. These enormous and proven IIoT applications, inspire companies worldwide number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, to invest in the IIoT market, which is expected to grow up to such as Deep Learning (DL). Notably, the DL algorithms, $123.89 billion by 2021 (see Fig. 1). Throughout the literature, when applied in the Industrial Internet of Things (IIoT), can various terms are used to describe IoT for the industry, such enable various applications such as smart assembling, smart IIoT, smart manufacturing, and Industry 4.0 [19]–[22]. The manufacturing, efficient networking, and accident detection-and- step towards this smart production technology or IIoT is way prevention. Therefore, motivated by these numerous applications; in this paper, we present the key potentials of DL in IIoT. different from past technologies; therefore, it is also called First, we review various DL techniques, including convolutional the Industrial Revolution. The newly IIoT technology will neural networks, auto-encoders, and recurrent neural networks explicitly change the overall working conditions and day to and there use in different industries. Then, we outline numerous day lifestyles of people [23]–[25]. The revolution in smart use cases of DL for IIoT systems, including smart manufacturing, industry from Industry 1.0 to 4.0 is illustrated in Fig. 2. smart metering, smart agriculture, etc. Moreover, we categorize several research challenges regarding the effective design and Generally, IIoT networks consist of interconnected intelligent appropriate implementation of DL-IIoT. Finally, we present industrial components to accomplish high production with several future research directions to inspire and motivate further low cost. This is possible by real-time monitoring, precise research in this area. execution of tasks, and efficient controlling of the overall Index Terms—Industrial Internet of Things, deep Learning, industrial procedures [26]–[29]. smart industries, optimization, convolutional neural networks, auto-encoders, recurrent neural networks I. INTRODUCTION The recent technological advancements in hardware, soft- ware, and wireless communication have facilitated the emer- gence of a new concept termed as the Internet of Things (IoT), simplifying the human lifestyle by saving time, en- ergy, and money [1]–[6]. In IoT, the term “Things” repre- sents smart devices, such as sensors, machines, and vehicles, with intelligent processing capabilities. Many interesting IoT applications include smart cities, smart homes, healthcare arXiv:2008.06701v1 [eess.SP] 15 Aug 2020 monitoring, intelligent transportation, smart power generation, and agriculture [7]–[11]. Nevertheless, the IoT technology also plays a vital role in modern industries by introducing automation and reducing expenses. Adding IoT sensors to the industrial systems helps operators to monitor equipment in near real-time with high reliability. The Industrial IoT Fig. 1. Size and market impact of IIoT . (IIoT) market (without the consumer IoT) is growing fast as As the number of internet-connected devices grows expo- Ruhul Amin Khalil is with the Department of Electrical Engineer- nentially, a significant amount of data is generated. In the case ing, University of Engineering and Technology, Peshawar 25120, Pakistan. of IIoT, both volume of the data and its characteristics needs email:[email protected] proper consideration because the performance of IIoT applica- Nasir Saeed, Tareq Y. Al-Naffouri, and Mohamed-Slim Alouini are with Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) tions is strictly related to the intelligent processing of the big Division, King Abdullah University of Science and Technology, Thuwal data, which comes from different real-time resources [30]– 23955, Makkah, Kingdom of Saudi Arabia. email: [email protected], [37]. Therefore, big data analysis in IIoT networks requires Yasaman Moradi Fard is with the Department of Biomedical Engi- neering, Faculty of Engineering, University of Isfahan, Isfahan, Iran. email: intelligent modeling that can be achieved using deep learning [email protected] techniques. 2 several advantages in IIoT [43], [44]. Recently, IIoT solu- tions are growing significantly, especially for sensor-based data consisting of various structures, formats, and semantics [45], [46]. Data released from IIoT technologies is derivative from various resources across manufacturing that includes product line, equipment-and-processes, labor operation, and environmental conditions. Therefore, data modeling, labeling, and analysis play an important role in making manufacturing much smarter [47], [48]. They are essential parts of handling high-volume data and supporting real-time data processing. Deep Learning (DL) is one of the powerful methods of machine learning (ML) which can upgrade manufacturing into highly optimized smart facilities by processing a bunch of data with its multi-layered structure. The DL approaches can provide computing intelligence from unclear sensory data, re- Fig. 2. Revolution of smart industries. sulting in smart manufacturing [49]. DL techniques are useful due to their automatic data learning behavior, identifying the underlying patterns, and making smart decisions. One of the As we discussed above, the most critical factor in automa- advantages of DL over traditional machine learning methods tion and intelligence for IIoT is data modeling, analyzing, and is that feature learning is done automatically, and there is no support real-time processing. Among data analysis schemes, need to design a separate algorithm to do this part [50], [51]. Deep Learning (DL) used in regression, classification, and The comparison of DL with traditional techniques in IIoT can forecasting has shown the most potential for IIoT systems be analyzed in Fig. 3. [37]–[39]. DL approaches have benefits to learn from the given data automatically, identify the patterns, and make some precise decisions. With the advancements provided by DL methods, IIoT will transform into highly optimized facilities [40]. The advantages of using DL include lower operation costs, no change with variations in consumer demands, en- hancing productivity, downtime reduction, obtaining a better perspective of the market, and extracting valuables from the operations [41], [42]. With all these advantages of DL in IIoT, this paper aims to present a state-of-the-art review for DL techniques, such as Convolutional Neural Networks (CNNs), Auto Encoder (AE), Recurrent Neural Network (RNN), Restricted Boltzmann Ma- chine (RBM) and its variants, and their usage in IIoT. Notably, the DL-enabled advanced analytics framework can realize the Fig. 3. Comparison of DL with traditional algorithms for IIoT . opportunistic need for smart manufacturing. First, we review the basic DL techniques and their applications in IIoT. Then, we introduce various use cases of IIoT, where DL can sig- Different data analysis methods can be utilized to interpret nificantly improve the performance of predictive maintenance, the IIoT data, such as predictive analytics, descriptive ana- assets tracking, smart metering, remote healthcare, power-and- lytics, diagnostic analytics, and prescriptive analytics [52]– smart grid industry, telecommunications, and human resource [56]. After capturing and analyzing the product’s condition, management. Finally, we provide different research challenges environment, and operational parameters, we can arrive at a and future trends for DL-based IIoT. summary of what happens; this is called descriptive analytics. The rest of the paper is organized as follows. Key DL Predictive analytics utilizes statistical models and provides the concepts in the context of smart industries are illustrated in prospect of making future equipment fabrication or degrada- Section II. In Section III, we present numerous use cases tion based on the provided historical data. We use diagnostic of DL in different smart industries. Section IV provides analytics to figure out the foundation cause and report the various challenges faced by DL-based IIoT, followed by future reason for failure or reducing product performance. In contrast, research directions in Section V. Finally, conclusions are drawn prescriptive analytics recommends one or more courses of in Section VI. action and whatever lies beyond it. Measures out of data analysis techniques are classified for developing the production outcomes or improve the problems, depicting probable result II. DEEP LEARNING FOR IIOT of each assessment. Fig.4 shows the DL-based architecture and It is crucial to use smart manufacturing, making both its impact on IIoT. In the following, we discuss various DL manufacturing and production intelligent, which can have techniques for IIoT networks. 3 Fig. 4. Deep learning in future Industrial Internet of Things. A. Convolutional Neural Network layers and

Load more