The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

The role of big data analytics in industrial internet of things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

The Internet of Things: A Main Source of Big Data Analytics

IoT is regarded as a platform or a framework for the objects/devices to interact with one another in an electronically manner with the world around. Not only the humans can communicate with one another using system, but rather IoT had made possible for the system to communicate with each other too. It is being enabled by the presence of other independent technologies which make fundamental components of IoT. One of such technology is Big Data Analytics. Big data has emerged as a connecting point between the objects on the internet. Massive data is generated in day to day life and data mining has played a vital role in converting the data to information useful for the end users. But as the amount of data kept on increasing, it became difficult to extract useful information from it. It is when Big Data Analytics came into picture. In today’s world, various sensors interact over a wireless network by exchanging huge amount of data with one another. It is with the help of IoT, designing of a good infrastructure for storing and managing such huge amount of sensor data became possible. It resulted in an easy search and utilization of sensor data by the users. This paper deals with the so called relationship between IOT and Big data. In particular, the focus of the paper will be in how the things in IOT generate massive data (Big Data) and if both combined then how it can lead to wonders in real world. Keywords: Internet of things, Big Data Analytics, Hadoop, Big Data, Unstructured Dat

Big data analytics as a management tool: An overview, trends and challenges

Innovative digital technologies and ever-changing business environment have and will continue to transform businesses and industries around the world. This transformation will be even more evident in view of forthcoming technological breakthroughs, and advances in big data analytics, machine learning algorithms, cloud-computing solutions, artificial intelligence, internet of things, and the like. As we live in a data-driven world, technologies are altering work and work-related activities, and everyday activities and interactions. This paper is focused on big data and big data analytics (BDA), which are viewed in the paper from organisational perspective, as a means of improving firm performance and competitiveness. Based on a review of selected literature and researches, the paper aims to explore the extent to which big data analytics is utilized in companies, and to highlight the valuable role big data analytics may play in achieving better business outcomes. Furthermore, the paper briefly presents main challenges that accompany the adoption of big data analytics in companies

Big Data Network Optimization for Mobile Cellular Networks in 5G

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing

Big Data Classification and Internet of Things in Healthcare

[EN] Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive analysis of the various techniques involved in mining big data. This article provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future works. Therefore, in this article, the use of machine learning techniques is proposed for real-time diabetic patient data analysis from IoT devices and gatewaysRghioui, A.; Lloret, J.; Oumnad, A. (2020). Big Data Classification and Internet of Things in Healthcare. International Journal of E-Health and Medical Communications. 11(2):20-37. https://doi.org/10.4018/IJEHMC.2020040102203711

The impact of organisation culture on effective exploitation of building information modelling, big data analytics and internet of things (BBI) for competitive advantage in construction organisations

The purpose of this paper is to analyse the impact of organisational culture on the exploitation of three technological innovations: Building Information Modelling, Big Data Analytics and Internet of Things (BBI) considering the role of organisational culture as a determinant of organisational competitive advantage. After reviewing the literature on organisational culture and its relationship with competitiveness, this paper further analyses the critical culture constructs that impact specifically on exploitation of Building Information Modelling, Big Data Analytics and Internet of Things which leads to maximise organisational competitive advantage. Findings reveal that organisational culture can be both positively and negatively associated with aforementioned technological innovations depending on its key attributes for exploitation. Hence, culture of an organisation has the potential of fostering innovative technologies, but can also act as a barrier depending on how they are operationalised. The findings additionally show that in order to enhance innovation, neither a flexibility focus (which is rooted in collaboration and shared commonalities) nor an external focus (built upon the dynamics of competition and achieving concrete results) alone would suffice- both are equally critical in characterising organisational culture. The paper focuses on a context, where there is a lack of studies on the impact of cultural constructs that are specifically relevant to BBI, which lays the basis for the originality of this paper. Findings can guide managers’ efforts in organisational culture developments which foster exploitation of these technologies towards maximising the competitive edge