Flora Health Wireless Monitoring with Plant-Microbial Fuel Cell

Abstract We propose a self-sustainable wireless sensor node capable to monitor both environmental data and flora health state, exploiting a Microbial Fuel Cell combined with a plant. This bio-electrochemical system is used both as a power generator to supply the wireless embedded electronics and as a biosensor for estimating the status of the plant. We demonstrate that the sub-milliwatt power provided by the fuel cell is enough for achieving an energy-neutral smart sensor that samples and sends data. Moreover, the rate of the harvested power is correlated with the health of the flora living in symbiosis with the bacteria colony. The proposed system has been conceived to address the needs of future smart agriculture applications, providing an unobtrusive and energy neutral monitoring system open to a broad range of applications, thanks to the bacteria species that populate almost any soil on Earth

Environmental Energy Harvesting Techniques to Power Standalone IoT-Equipped Sensor and Its Application in 5G Communication

In the recent few years, due to its significant deployment to meet global demand for smart cities, the Internet of Things (IoT) has gained a lot of attention. Environment energy harvesting devices, which use ambient energy to generate electricity, could be a viable option in near future for charging or powering stand-alone IoT sensors and electronic devices. The key advantages of such energy harvesting gadgets are that they are environmentally friendly, portable, wireless, cost-effective, and compact. It is significant to propos and fabricate an improved, high-quality, economical, and efficient energy harvesting systems to overcome power supply to tiny IoT devices at the remote locations. In this article, various types of mechanisms for harvesting renewable energies that can power sensor enabled IoT locally, as well as its associated wireless sensor networks (WSNs), are reviewed. These methods are discussed in terms of their advantages and applications, as well as their drawbacks and limitations. Furthermore, methodological performance analysis for the decade 2005 to 2020 is surveyed in order to identify the methods that delivered high output power for each device. Furthermore, the outstanding breakthrough performances of each of the aforementioned micro-power generators during this time period are emphasized. According to the research, thermoelectric modules can convert up to 2500×10^(-3) W/cm^2, thermo-photovoltaic 10.9%, piezoelectric 10,000 mW/cm^3 and microbial fuel cell 6.86 W/m^2 of energy. Doi: 10.28991/esj-2021-SP1-08 Full Text: PD

Internet of Things in Agricultural Innovation and Security

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

A “Plant-Wearable System” for Its Health Monitoring by Intra- and Interplant Communication

A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 μμ A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 μμ A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment

Energy Harvesting Systems for the Internet of Things with Applications to Smart Agriculture

The Internet of Things is the interconnection of everyday objects to the web, with the purpose of exchanging information to enable smarter actions and potentially make a process more efficient. However, how power is provided and stored in remote sensing applications is still one of the main modern electronics challenges of such technology and can become one of the main constraints to prevent its mass adoption. Energy Harvesting is an emerging technology that can transform energy in the environment into usable energy, among such environmental energy are electromagnetic waves, thermal, solar, kinesthetic transducers, fuel cells, to name a few. Because this technology makes use of the available ambient energy, it has the potential to increase the power readiness for battery-operated electronics and more importantly, it can become the technology that fully powers the next generation of internet-enabled agricultural solutions. This dissertation centers around the design and development of high-efficient power management systems for AC and DC energy harvesting sources. The proposed architectures not only consider circuits, systems and algorithms that make a more efficient power extraction but also focuses on providing inherent sensing functionalities at no extra system complexity, which in turn not only achieves the goal of extending the battery life of proposed smart sensor applications but also proposes new charge extraction methods to permanently power an electronic device. The work presented in this dissertation demonstrates that energy harvesting, and internet of things devices can be implemented in multiple smart agriculture scenarios by proposing algorithms, circuits and systems capable of performing energy harvesting operations while providing reliable data to the end user. The analysis of the design of such proof-of-concept prototypes are provided in this dissertation along with its implementation and testing. The first part of this dissertation proposes novel algorithms for maximum power extraction and new power measurement techniques. The second part focuses on front-end circuits for AC energy harvesting sources and circuits that can provide sensing capabilities along with energy harvesting operations

A Long-range Context-aware Platform Design For Rural Monitoring With IoT In Precision Agriculture

The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measurements of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed

A Wirelessly Controlled Robot-based Smart Irrigation System by Exploiting Arduino

In recent years, because of the limitations of fossil fuels and emissions resulting from the use of photovoltaic cells increase. Due to the changing state of the sun, solar cells must follow the sun's radiation to receive more energy. But, in this research, the modeling and analysis of the solar tracking system were carried out to obtain the optimal angle in photovoltaic systems for generating maximum power using genetic algorithm (GA). In this paper, the control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes. In this method, without the need for additional hardware, the optimal panel position angles are calculated by using the Matlab software to capture the most sun and maximize output energy. The main advantage is that the system operates discretely during operation and losses are reduced, as well as in the clouds, solar radiation is received and the output energy rises. The important results of this study can be the system is optimized, the output power of the photovoltaic system in a fixed array mode increases by 15.85%

Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression

This dissertation explores how to increase sensor density in the agricultural framework using low-cost sensors, while also managing major bottlenecks preventing their full commercial adoption for agriculture, accuracy and drift. It also investigated whether low-cost biodegradable printed sensor sheets can result in improved stability, accuracy or drift for use in precision agriculture. In this dissertation, multiple electrode systems were investigated with much of the work focused on printed carbon graphene electrodes (with and without nanoparticles). The sensors were used in two configurations: 1) in varying soil to understand sensor degradation and the effect of environment on sensors, and 2) in plant pod systems to understand growth. It was established that 3) the sensor drift can be controlled and predicted 2) the fabricated low-cost sensors work as well as commercial sensors, and 3) these sensors were then successfully validated in the pod platform. A standardized testing system was developed to investigate soil physicochemical effects on the modified nutrient sensors through a series of controlled experiments. The construct was theoretically modeled and the sensor data was matched to the models. Supervised machine learning algorithms were used to predict sensor responses. Further models produced actionable insight which allowed us to identify a) the minimal amounts of irrigation required and b) optimal time after applying irrigation or rainfall event before achieving accurate sensor readings, both with respect to sensor depth placement within the soil matrix. The pore-scale behavior of solute transport through different depths within the sandy soil matrix was further simulated using COMSOL Multi-physics. This work leads to promising disposable printed systems for precision agriculture

Developing Energy Harvest Efficient Strategies with Microbial Fuel Cells

Nowadays, thinking of energetic efficiency is to determine how to decrease consumption and to reuse resources. This is a major concern when addressing hydric resources. The consumption of drinking water is seeing an unaffordable growth and, although most of it is replenished to the environment, the water quality is affected by pollutants and impurities. As such, using wastewater, a by-product of our routine and way of life, as resource is an asset. Even more when thinking about the heightened energy costs of a wastewater treatment station. The hypotheses of this work show how to achieve this goal by using microbial fuel cells. The organic composition of this water increases its energy production potential, where the bacterial metabolism can be used to, simultaneously, produce energy and help to clean the water. This document is divided in 5 chapters. The strategic positioning of the theme happens in chapter 1. Chapter 2 explains how the main elements of microbial fuel cell technology can work and determine its operation. In chapter 3, the power management systems used with microbial fuel cells are presented and discussed, with the identification of optimization strategies. The second-to-last chapter corresponds to the experimental results discussion and validation, while focusing improved energy production efficiencies. The outputs of this chapter pilot the future work analysis on chapter 5, together with the main conclusions and research trends. The validity and usefulness of this work is cleared with an application example.Pensar em economia energética é, hoje, considerar soluções para a redução de consumo e reutilização de recursos. Esta preocupação é importante ao examinar a utilização dos recursos hídricos. O consumo de água potável está a crescer insustentavelmente e, apesar de grande parte desse consumo ser restituído ao meio ambiente, a qualidade da água é afetada por poluentes ou impurezas. A utilização de água residual, um produto da nossa rotina e qualidade de vida, como um recurso é, por isso, uma mais valia. É ainda mais evidente ao considerar os elevados consumos energéticos de uma estação de tratamento de água residual. As hipóteses abordadas neste trabalho mostram como é possível atingir este objetivo usando células microbianas de combustível. A composição orgânica desta água faz com que o seu potencial energético possa ser explorado, usando o metabolismo bacteriano para produzir energia e, simultaneamente, auxiliar na limpeza da água. Este documento está dividido em 5 capítulos. O posicionamento do tema ocorre no capítulo 1. O capítulo 2 observa os principais elementos da tecnologia das células microbianas de combustível, permitindo compreender o seu funcionamento e conhecer que variáveis afetam o seu funcionamento. No capítulo 3 são apresentadas as tipologias de abordagem à gestão energética para esta pilha bacteriológica, discutindo-se as vantagens e otimizações de cada sistema. O penúltimo capítulo corresponde à exploração de resultados experimentais e à validação de hipóteses, orientadas para a maior eficiência energética. Surgem assim recomendações que servirão para guiar os trabalhos futuros, discutidos no capítulo final. Este, o capítulo 5, conta ainda com a apresentação das principais conclusões e das tendências de pesquisa. O trabalho termina com um exemplo de aplicação que solidifica a validade e utilidade da aplicação desta tecnologia

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