Bootstrap–CURE: A novel clustering approach for sensor data: an application to 3D printing industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government.Peer ReviewedPostprint (published version

Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories

Prescriptive Analytics deals with the task of prescribing actionable decisions. These decision-making processes are usually based on expert knowledge and existing Data Analytics solutions in a specific manufacturing environment. Prescriptive Analytics is seen as the next big thing for smart factories. Most Use Cases still focus on the descriptive, diagnostic, or predictive level. This is partly due to the complexity of Prescriptive Analytics algorithms. Another major challenge for practitioners is the lack of transparency when planning Use Cases as they are mostly seen as standalone initiatives. This paper presents a novel approach to developing Use Cases based on a fit-gap analysis for existing data objects for Prescriptive Production Management in the Smart Factory. First, a Prescriptive Analytics Data Canvas is developed to structure the input data for Prescriptive Analytics in the Smart Factory. Then, promising Use Cases are selected based on the Data Canvas and existing data collection methods. Synergies between different Use Cases in the same factory are derived. We demonstrate the functionality and usability of the developed artifact and method in a real-world IoT-Factory scenario

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

A review of the 'smart technology' currently being explored globally and its potential impact upon the construction industry on a micro level

The following paper will review literature that covers the use of ‘Smart Technology’ and ‘Big Data’ in the context of Smart Cities currently being explored globally. By investigating into the perceived benefits of implementing the digital economy in to essential infrastructure the paper will look at how the construction industry can benefit. The literature covered found that through the adoption of Smart Technology within a Smart City framework there are benefits available for all industries; such as greater efficiencies and forecasting ability, resulting in savings. However the integration of real time data on-site could possess great potential for construction managers as they look to make more informed and accurate decisions. However the extents of the benefits are unclear as many pieces of literature state that the potential use of Big Data is almost unimaginable currently. Urbanisation is forcing city authorities to adopt more strategic approaches to their decision making processes which has resulted in the emergence of ‘Smart Cities’. Case studies around the globe have shown promising and innovative potential for a range of stakeholders. These are promising signs for the industry as it still seeks considerable investment and testing before it can be scaled up. However further work should look to investigate first-hand how construction managers could benefit from open source ‘Big Data’ collected by city authorities. This would add evidence to the many theoretical benefits that are possible

Climate Change Adaptation in Agriculture: IoT-Enabled Weather Monitoring

Smart farming is a new technology concept that uses advanced electronic sensors to collect data from a variety of agricultural landscapes. This data is used to make predictions about weather patterns, soil fertility, crop quality, and water requirements. The information can be used by experts and local farmers to make better decisions about short- and long-term planning. One of the key aspects of smart farming is the automation of some farming processes, such as smart irrigation and water management. This can be done using predictive algorithms on SoC or microcontrollers to determine how much water is needed right now for a particular agricultural sector. The use of the Internet of Things (IoT) eliminates the need for manual labor to collect this crucial agricultural data. This is because IoT systems can collect data automatically and in real time, which canlead to more accurate and timely insights. This paper will present a research study on the development of a smart farming system. The study will investigate the use of different types of sensors, data collection and analysis techniques, and communication protocols. The results of the study will be used to design and implement a prototype system. The prototype system will be evaluated in a field trial to assess its effectiveness in improving agricultural productivity. The paper will conclude with a discussion of the potential benefits of smart farming systems for agriculture. The paper will also discuss the challenges that need to be addressed in order to make these systems more widely available and affordable

A review of the 'smart technology' currently being explored globally and its potential impact upon the construction industry on a micro level

The following paper will review literature that covers the use of ‘Smart Technology’ and ‘Big Data’ in the context of Smart Cities currently being explored globally. By investigating into the perceived benefits of implementing the digital economy in to essential infrastructure the paper will look at how the construction industry can benefit. The literature covered found that through the adoption of Smart Technology within a Smart City framework there are benefits available for all industries; such as greater efficiencies and forecasting ability, resulting in savings. However the integration of real time data on-site could possess great potential for construction managers as they look to make more informed and accurate decisions. However the extents of the benefits are unclear as many pieces of literature state that the potential use of Big Data is almost unimaginable currently. Urbanisation is forcing city authorities to adopt more strategic approaches to their decision making processes which has resulted in the emergence of ‘Smart Cities’. Case studies around the globe have shown promising and innovative potential for a range of stakeholders. These are promising signs for the industry as it still seeks considerable investment and testing before it can be scaled up. However further work should look to investigate first-hand how construction managers could benefit from open source ‘Big Data’ collected by city authorities. This would add evidence to the many theoretical benefits that are possible

Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework

© 2017 by the authors. This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data

Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered -- as opposed to classic time-triggered -- paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building

Cloud-computing strategies for sustainable ICT utilization : a decision-making framework for non-expert Smart Building managers

Virtualization of processing power, storage, and networking applications via cloud-computing allows Smart Buildings to operate heavy demand computing resources off-premises. While this approach reduces in-house costs and energy use, recent case-studies have highlighted complexities in decision-making processes associated with implementing the concept of cloud-computing. This complexity is due to the rapid evolution of these technologies without standardization of approach by those organizations offering cloud-computing provision as a commercial concern. This study defines the term Smart Building as an ICT environment where a degree of system integration is accomplished. Non-expert managers are highlighted as key users of the outcomes from this project given the diverse nature of Smart Buildings’ operational objectives. This research evaluates different ICT management methods to effectively support decisions made by non-expert clients to deploy different models of cloud-computing services in their Smart Buildings ICT environments. The objective of this study is to reduce the need for costly 3rd party ICT consultancy providers, so non-experts can focus more on their Smart Buildings’ core competencies rather than the complex, expensive, and energy consuming processes of ICT management. The gap identified by this research represents vulnerability for non-expert managers to make effective decisions regarding cloud-computing cost estimation, deployment assessment, associated power consumption, and management flexibility in their Smart Buildings ICT environments. The project analyses cloud-computing decision-making concepts with reference to different Smart Building ICT attributes. In particular, it focuses on a structured programme of data collection which is achieved through semi-structured interviews, cost simulations and risk-analysis surveys. The main output is a theoretical management framework for non-expert decision-makers across variously-operated Smart Buildings. Furthermore, a decision-support tool is designed to enable non-expert managers to identify the extent of virtualization potential by evaluating different implementation options. This is presented to correlate with contract limitations, security challenges, system integration levels, sustainability, and long-term costs. These requirements are explored in contrast to cloud demand changes observed across specified periods. Dependencies were identified to greatly vary depending on numerous organizational aspects such as performance, size, and workload. The study argues that constructing long-term, sustainable, and cost-efficient strategies for any cloud deployment, depends on the thorough identification of required services off and on-premises. It points out that most of today’s heavy-burdened Smart Buildings are outsourcing these services to costly independent suppliers, which causes unnecessary management complexities, additional cost, and system incompatibility. The main conclusions argue that cloud-computing cost can differ depending on the Smart Building attributes and ICT requirements, and although in most cases cloud services are more convenient and cost effective at the early stages of the deployment and migration process, it can become costly in the future if not planned carefully using cost estimation service patterns. The results of the study can be exploited to enhance core competencies within Smart Buildings in order to maximize growth and attract new business opportunities

Overview of the Smart Network Element Architecture and Recent Innovations

In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM