AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

Advanced Sensing, Fault Diagnostics, and Structural Health Management

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

A multi-modal smart sensing network for marine environmental monitoring

There is an imperative need for long-term, large-scale marine monitoring systems that will allow decisions to be made based on the analysis of collected data to avoid or limit negative impacts on the ecosystem. Modern marine environmental sensing technologies, such as autonomous wireless sensor networks (WSNs), provide the capability to meet the challenges of high spatial and temporal scales. However, the significant amount of data generated from WSNs is a significant challenge for manual analysis. These multitudinous data need to be automatically processed, indexed and catalogued in a smarter way that can be more easily understood, accessed and managed by operators, scientists and policy makers. Moreover, current research works show that WSNs have their own limitations, for example, reliability issues and the fact that they are passive systems and provide context-less data. Thus, it is becoming increasingly clear that in order to adequately monitor marine environments, they need to be characterised from multiple perspectives. Combining multiple technologies and sensing modalities in environmental monitoring programmes can provide not only advantages of reliability and robustness for sensing systems, but also enhanced understanding of environmental processes. In addition, considerable advances can be made if robust sensing technology can be combined with sophisticated methods of data analysis, classification and cataloguing. The aim of this work is to bridge the gap between current aquatic monitoring systems and futuristic ideal large scale multi-modality smart sensing networks for marine environmental monitoring. To illustrate this, a smart sensing system is proposed and two case studies are used to show data processing from in-situ measurements and from camera based visual sensing data automatically using machine learning techniques. Abnormal events detection results from an in-situ sensor and shipping traffic detection results from visual sensor are combined to illustrate the benefit of coupling multiple sensing modalities

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

BLE-based Indoor Localization and Contact Tracing Approaches

Internet of Things (IoT) has penetrated different aspects of modern life with smart sensors being prevalent within our surrounding indoor environments. Furthermore, dependence on IoT-based Contact Tracing (CT) models has significantly increased mainly due to the COVID-19 pandemic. There is, therefore, an urgent quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions leveraging accurate indoor localization/tracking approaches. In this context, the first objective of this Ph.D. thesis is to enhance accuracy of Bluetooth Low Energy (BLE)-based indoor localization. BLE-based localization is typically performed based on the Received Signal Strength Indicator (RSSI). Extreme fluctuations of the RSSI occurring due to different factors such as multi-path effects and noise, however, prevent the BLE technology to be a reliable solution with acceptable accuracy for dynamic tracking/localization in indoor environments. In this regard, first, an IoT dataset is constructed based on multiple thoroughly separated indoor environments to incorporate the effects of various interferences faced in different spaces. The constructed dataset is then used to develop a Reinforcement Learning (RL)-based information fusion strategy to form a multiple-model implementation consisting of RSSI, Pedestrian dead reckoning (PDR), and Angle-of-Arrival (AoA)-based models. In the second part of the thesis, the focus is devoted to application of multi-agent Deep Neural Networks (DNN) models for indoor tracking. DNN-based approaches are, however, prone to overfitting and high sensitivity to parameter selection, which results in sample inefficiency. Moreover, data labelling is a time-consuming and costly procedure. To address these issues, we leverage Successor Representations (SR)-based techniques, which can learn the expected discounted future state occupancy, and the immediate reward of each state. A Deep Multi-Agent Successor Representation framework is proposed that can adapt quickly to the changes in a multi-agent environment faster than the Model-Free (MF) RL methods and with a lower computational cost compared to Model-Based (MB) RL algorithms. In the third part of the thesis, the developed indoor localization techniques are utilized to design a novel indoor CT solution, referred to as the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) approach coupled with a Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

Internet of Things 2.0: Concepts, Applications, and Future Directions

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments