Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Does blockchain technology have anything to say in the oil and gas industry: the study of opportunities, challenges, and future trends

This research focuses on the utilization of blockchain as an emerging technology in the oil and gas industry with the aim to advance the understanding of blockchain technology adoption in this industry. Through conducting qualitative research based on semi-structured interviews with managers and decision makers in the oil and gas industry and by overviewing the literature, the current opportunities, challenges, and adoption barriers of this technology were investigated. Findings indicated that despite the rapid progress of digital technologies, the adoption of blockchain technology in the oil and gas industry is a slow process due to the conservative and inherently resistant nature of the industry. Although there is evidence proving that the technology is capable of increasing the efficiency and operational transparency in the industry, there are still challenges mainly due to lack of business and managerial support, awareness, and expertise about blockchain technology within the oil and gas industry or immaturity and complexity of the technology. Different categories of blockchain adoption barriers were detected together with the presentation of some solutions to overcoming these barriers. Additionally, the drivers and influential factors in the adoption of the technology were discussed. The future of blockchain technology in the oil and gas industry is considered to be promising based on the participants’ opinions and the evidence from successful use cases

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

A survey on industry 4.0 for the oil and gas industry: upstream sector

The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored

A modern teaching environment for process automation

Emergence of the new technological trends such as Open Platform Communications Unified Architecture (OPC UA), Industrial Ethernet, cloud computing and the 5th wireless network (5G) enabled the implementation of Cyber-physical System (CPS) with flexible, configurable, scalable and interoperable business models. This provides new opportunities for the process automation systems. On the other hand, the constant urge of industries for cost and material efficient processes demands a new automation paradigm with the latest tools and technologies which should be taken into account while teaching future automation engineers. In this thesis, the modern teaching environment for process automation is designed, implemented and described. This work explains the connections, configurations and the test of three mini plants including the Multiple Heat Exchanger, the Three-tank system and the Mixing Tank. In addition, OPC UA communication between the server and its clients has been tested. The plants are a part of the state of the art of the architecture that provides the access of ABB 800xA to the cloud services via OPC UA over the 5G test wireless network. This new paradigm changes the old automation hierarchy and enables the cross layered communication in the old architecture. This modern teaching environment prepares the students for the future automation challenges with the latest tools and merges data analytics, cloud computing and wireless network studies with process automation. It also provides the unique chance of testing the future trends together in this unique process automation setup

Security and privacy analysis based on Internet of Things in the fourth industrial generation (Industry 4.0)

The connection of smart devices using the Internet has dramatically changed the way people live, and this concept has also been extended to the industrial sector. This practice not only provides more stable, faster, and safer communications but also makes it possible to realize the concept of the smart factory in the fourth industrial revolution. The Internet of Things uses a unique Internet Protocol to identify, control, and transmit data to individuals as well as databases. Data is collected through the Internet of Things, stored in cloud storage, and managed and calculated through analytical tools. Internet of Things security is a field of technology that focuses on protecting connected devices and networks in the Internet of Things (IoT). Ensuring the safety of networks with connected IoT devices is critical. Security in the Internet of Things includes a wide range of techniques, strategies, protocols, and measures aimed at mitigating the ever-increasing vulnerabilities of the Internet of Things in modern businesses. The simultaneous connection of objects also brings privacy concerns. For this reason, in this research, an effort has been made to examine and analyze the most important privacy requirements in the Internet of Things in digital businesses in Industry 4.0. In this regard, by using experts' opinions and literature review, privacy requirements were extracted and evaluated using fuzzy non-linear decision-making methodology. The results showed that acquired and intrinsic information has the highest importance

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

Blockchain, parameterisation and automated arbitrage applied to the chemical industry

This thesis considers three scenarios related to chemical industry where the concepts of eco-industrial parks (EIPs), Industry 4.0, parameterisation, blockchain and arbitrage are brought together to explore the issues of simulation speed and accuracy, machine-to-machine (M2M) communication and automated participation in financial markets. In the first scenario, a biodiesel plant flow sheet model is analysed and parameterised. The relations between 11 inputs typical to a biodiesel plant and its energy requirements are approximated using surrogate models, of which accuracy is assessed. Additionally, the effects of dimensionality, domain size and surrogate type on the accuracy are investigated and global sensitivities of the outputs are computed using High Dimensional Model Representation (HDMR). Most surrogate models achieved at least a reasonable fit regardless of the domain size and number of dimensions. It was observed that in all cases only 4 or fewer inputs have significant influence on any of the outputs and that the interaction terms have only minor effect on any one output. In the second scenario, applications of blockchain technology related to Industry 4.0 are explored and an example where blockchain is employed to facilitate M2M interactions and establish a M2M electricity market in the context of the chemical industry is presented. Successful implementation of two electricity producers and one electricity consumer trading with each other over a blockchain-based network is presented. In the third scenario, an automated arbitrage spotter is developed and applied to two cases: conversion of natural gas to methanol and crude palm oil to biodiesel. The spotter is designed to search for opportunities to make additional profit by analysing the futures market prices for both the reagent and the product. It considers cost of storage and conversion (other feedstock, steam, electricity and other utilities) derived from physical simulations of the chemical process. In a profitable scenario up to 345.17 USD per tonne of biodiesel can be earned by buying contracts for delivery of crude palm oil in September 2018 and selling contracts for delivery of biodiesel in December 2018 in a ratio of 4 to 1.Department of Chemical Engineering and Biotechnology in the University of Cambridge and Cambridge CARES Ltd