Flow Measurement and Fluid Mechanics

Industry 4.0. The modern industrial environment focuses on providing a flexible factory floor that suits the needs of modern manufacturing through the reduction of downtimes, reconfiguration times, adoption of new technologies and the increase of its production capabilities and rates. Fog Computing through CPS aims to provide a flexible orchestration and management platform that can meet the needs of this emerging industry model.

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

Innovations in Big Data Analytics and advances in Internet of Things have changed how we collect, interpret and analyses data from Smart City and Transportation environments. Advances in Fog Computing that suggests the extension of the Cloud to the Edge of the network using resources available to reduce latencies and improve reliability fit perfectly with concepts from autonomous and connected vehicles which have increasingly powerful processing capabilities. The use of such systems for congestion management, route planning, infrastructure management and other advances orchestration techniques requires the use of varying platform and technologies together. The existing proposals and platform are mostly designed to perform a fixed set of tasks, either purpose built or not generic enough to satisfy requirements from multiple use-cases. This paper analyses existing Fog Computing and Processing platforms characteristic data types and their brokers as well as existing Big Data cluster-computing frameworks like Apache Spark and Hadoop based on capabilities, data types and preferred use-cases. Supported by this analysis we propose a conceptual polyglot Fog Computing platform for Smart Transportation scenarios that can satisfy the needs of future smart city environments and can connect to multiple platforms and perform real-time, off line and hybrid processing of data as well. We provide a high-level framework for the integration of such platforms into collaborative clustering scenarios.

The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical run-time parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with tailored optimization methods than can handle the heterogeneity and scale of the fog can improve the overall system run-time parameters and improve constraint satisfaction. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 90% accurate on load and delay predictions for application deployments in both cloud and edge.

Internet of Things (IoT), one of the key research topics in recent years, together with concepts from Fog Computing, brings rapid advancements in Smart City, Monitoring Systems, industrial control, transportation and other fields. These applications require a reconfigurable sensor architecture that can span multiple scenarios, devices and use cases that allow storage, networking and computational resources to be efficiently used on the edge of the network. There are a number of platforms and gateway architectures that have been proposed to manage these components and enable application deployment. These approaches lack horizontal integration between multiple providers as well as higher order functionalities like load balancing and clustering. This is partly due to the strongly coupled nature of the deployed applications, a lack of abstraction of device communication layers as well as a lock-in for communication protocols. This limitation is a major obstacle for the development of a protocol agnostic application environment that allows for single application to be migrated and to work with multiple peripheral devices with varying protocols from different local gateways. This research looks at existing platforms and their shortcomings as well as proposes a messaging based modular gateway platform that enables clustering of gateways and the abstraction of peripheral communication protocol details. These novelties allow applications to send and receive messages regardless of their deployment location and destination device protocol, creating a more uniform development environment. Furthermore, it results in a more streamlined application development and testing while providing more efficient use of the gateway's resources. Our evaluation of a prototype for the system shows the need for the migration of resources and the QoS advantages of such a system. The examined use case scenarios show that clustering proves to be an advantage in certain use cases as well as presenting the deployment of a larger testing and control environment through the platform.

Continuous integration of renewable energy sources into power networks is causing a paradigm shift in energy generation and distribution with regards to trading and control; the intermittent nature of renewable sources affects pricing of energy sold or purchased; the networks are subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, current bounds for distribution lines. These economic and technical constraints coupled with intermittent renewable injection may pose a threat to system stability and performance. We propose a novel holistic approach to energy trading composed of a distributed predictive control framework to handle physical interactions, i.e., voltage constraints and power dispatch, together with a negotiation framework to determine pricing policies for energy transactions. We study the effect of forecasting generation and consumption on the overall network’s performance and market behaviours. We provide a ri...

We have designed a two-stage, 10-step process to give organisations a method to analyse small local energy systems (SLES) projects based on their Cyber Physical System components in order to develop future-proof energy systems. SLES are often developed for a specific range of use cases and functions, and these match the specific requirements and needs of the community, location or site under consideration. During the design and commissioning, new and specific cyber physical architectures are developed. These are the control and data systems that are needed to bridge the gap between the physical assets, the captured data and the control signals. Often, the cyber physical architecture and infrastructure is focused on functionality and the delivery of the specific applications. But we find that technologies and approaches have arisen from other fields that, if used within SLES, could support the flexibility, scalability and reusability vital to their success. As these can improve the o...

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

Adding renewable energy sources and storage units to an electric grid has led to a change in the way energy is generated and billed. This shift cannot be managed without a unified view of energy systems and their components. This unified view is captured within the idea of a Smart Local Energy System (SLES). Currently, various isolated control and market elements are proposed to resolve network constraints, demand side response and utility optimisation. They rely on topology estimations, forecasting and fault detection methods to complete their tasks. This disjointed design has led to most systems being capable of fulfilling only a single role or being resistant to change and extensions in functionality. By allocating roles, functional responsibilities and technical requirements to bounded systems a more unified view of energy systems can be achieved. This is made possible by representing an energy system as a distributed peer-to-peer (P2P) environment where each individual demand e...

The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where

The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where

Phototherapy has shown its effect on cell stimulation and inhibition based on Arndt-Schulz model. Even though this therapeutic method has apparent effect, but it has limitations for epithelial application due to limitations on light penetration. Hence, with the ideology of fully overcoming this limitation, phosphorescent powder (strontium aluminate) is proposed as the potential light source that emitting photon from inside the body for phototherapy purposes. The strontium aluminate powder used in the experiment has the highest peak absorption at wavelength around 650 nm and lowest at around 350 nm. According to FESEM images, the powder has the particle size varies from 10 to 50 μm at cubic phase. The assessment is done by studying the effect on erythrocyte after blood plasma is irradiated by strontium aluminate powder’s photon. The powder luminesces with a maximum at 491.5 nm when pumped with 473 nm laser at 100 mW in fixed amount of 0.005±0.001 g. Later, it is mixed with  centrifug...

The quantification of causality is vital for understanding various important phenomena in nature and laboratories, such as brain networks, environmental dynamics, and pathologies. The two most widely used methods for measuring causality are Granger Causality (GC) and Transfer Entropy (TE), which rely on measuring the improvement in the prediction of one process based on the knowledge of another process at an earlier time. However, they have their own limitations, e.g., in applications to nonlinear, non-stationary data, or non-parametric models. In this study, we propose an alternative approach to quantify causality through information geometry that overcomes such limitations. Specifically, based on the information rate that measures the rate of change of the time-dependent distribution, we develop a model-free approach called information rate causality that captures the occurrence of the causality based on the change in the distribution of one process caused by another. This measure...