IoT応用向け環境RF信号RFエネルギーハーベスティングシステムの研究

電気通信大学201

Generic wireless sensor network for dynamic monitoring of a new generation of building material

Existing testing methods for building materials before deployment include a series of procedures as stipulated in British Standards, and most tests are performed in a controlled laboratory environment. Types of equipment used for measurements, data logging, and visualisation are commonly bulky, hard-wired, and consume a significant amount of power. Most of the off-the-shelf sensing nodes have been designed for a few specific applications and cannot be used for general purpose applications. This makes it difficult to modify or extend the sensing features when needed. This thesis takes the initiative of designing and implementing a low-powered, open-source, flexible, and small-sized Generic wireless sensor network (GWSN) that can continuously monitor the building materials and building environment, to address the limitations of the conventional measurement methods and the technological gap. The designed system is comprised of two custom-made sensor nodes and a gateway, as well as purpose designed firmware for data collection and processing. For the proof of concept and experimental studies, several measurement strategies were designed, to demonstrate, evaluate, and validate the effectiveness of the system. The data was collected from selected case study areas in the School of Energy, Geoscience, Infrastructure and Society (EGIS) laboratories by measuring and monitoring building structures and indoor environment quality parameters using the designed GWSN. The measured data includes heat flux through the material, surface and air temperatures on both sides of the material/structure, moisture variation, ambient temperature, relative humidity, carbon dioxide, volatile organic compounds, particulate matter, and sound/acoustic levels. The initial results show the potential of the designed system to become the new benchmark for tracking the variation of building materials with the environment and investigating the impact of variation of building materials on indoor environment quality. Based on the estimates of the thermal performance data, the sample used in the experiment had a typical U-value between 4.8 and 5.8 W/m2K and a thermal resistance value of 0.025m2 ·K/W[1][2]. Thermal resistance values from the GWSN real-time measurement were between 0.025 and 0.03 m2K/W, with an average of 0.025 m2K/W, and thermal transmission values varied between 4.55 and 5.11 W/m2K. Based on the data obtained, the results are within the range of typical values[3]. For thermal comfort measurements, the results of humidity and temperature from GWSN were compared to values in the Kambic climatic chamber in the EGIS laboratory, and the accuracies were 99 % and 98 % respectively. For the IAQ measurements, the values of CO2 and TVOCs were compared to the commercial off-the-shelf measuring system, and the accuracies were 98 %, and 97 %. Finally, the GWSN was tested for acoustic measurements in the range of 55 dB to 106 dB. The results were compared to class one Bruel & Kjaer SLM. The accuracy of GWSN was 97 %. The GWSN can be used for in lab and in-situ applications, to measure and analyse the thermal physical properties of building materials/building structures (thermal transmittance, thermal conductivity, and thermal resistance). The system can also measure indoor air quality, thermal comfort, and airborne sound insulation of the building envelope. The key point here is to establish a direct link between how building materials vary with the environment and how this impacts indoor environment quality. Such a link is essential for long-term analysis of building materials, which cannot be achieved using current methods. Regarding increasing the power efficient of the implemented GWSN as well as its performance and functionality, a new sensing platforms using backscatter technology have been introduced. The theory of modulation and spread spectrum technique used in backscattering has been explored. The trade-off between hardware complexity/power consumption and link performance has been investigated. Theoretical analysis and simulation validation of the new sensing technique, using backscatter communication, has been performed. A novel multicarrier backscatter tag compatible with Wireless Fidelity has been implemented and an IEEE 802.11g OFDM preamble was synthesized by simulation. The tag consists of only two transistors with current consumption no larger than 0.2 μA at voltage of less than 0.6 V. Novel harmonic suppression approaches for frequency-shifted backscatter communication has been proposed and demonstrated. The proposed approaches independently manipulate mirror harmonics and higher order harmonics whereby; specified higher order harmonics can be removed by carefully designing the real-valued (continuous and discrete) reflection coefficients-based backscatter tags. When successfully implemented, the backscatter system will reduce sensor node power consumption by shifting the power-consuming radio frequency carrier synthesis functions to carrier emitters.Engineering and Physical Sciences Research Council (EPSRC) Funding EP/H009612/

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Self Capacitance based Wireless Power Transfer for Wearable Electronics: Theory and Implementation

Wireless power transfer (WPT

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Low Power Circuit Design in Sustainable Self Powered Systems for IoT Applications

The Internet-of-Things (IoT) network is being vigorously pushed forward from many fronts in diverse research communities. Many problems are still there to be solved, and challenges are found among its many levels of abstraction. In this thesis we give an overview of recent developments in circuit design for ultra-low power transceivers and energy harvesting management units for the IoT. The first part of the dissertation conducts a study of energy harvesting interfaces and optimizing power extraction, followed by power management for energy storage and supply regulation. we give an overview of the recent developments in circuit design for ultra-low power management units, focusing mainly in the architectures and techniques required for energy harvesting from multiple heterogeneous sources. Three projects are presented in this area to reach a solution that provides reliable continuous operation for IoT sensor nodes in the presence of one or more natural energy sources to harvest from. The second part focuses on wireless transmission, To reduce the power consumption and boost the Tx energy efficiency, a novel delay cell exploiting current reuse is used in a ring-oscillator employed as the local oscillator generator scheme. In combination with an edge-combiner power amplifier, the Tx showed a measured energy efficiency of 0.2 nJ=bit and a normalized energy efficiency of 3.1 nJ=bit:mW when operating at output power levels up to -10 dBm and data rates of 3 Mbps