The emergence of modern technologies, such as Wireless Sensor Networks (WSNs), the Internet-of-Things (IoT), and Machine-to-Machine (M2M) communications, involves the use of batteries, which pose a serious environmental risk, with billions of batteries disposed of every year. However, the combination of sensors and wireless communication devices is extremely power-hungry. Energy Harvesting (EH) is fundamental in enabling the use of low-power electronic devices that derive their energy from external sources, such as Microbial Fuel Cells (MFC), solar power, thermal and kinetic energy, among others. Plant Microbial Fuel Cell (PMFC) is a prominent clean energy source and a step towards the development of self-powered systems in indoor and outdoor environments. One of the main challenges with PMFCs is the dynamic power supply, dynamic charging rates and low-energy supply. In this paper, a PMFC-based energy harvester system is proposed for the implementation of autonomous self-powered sensor nodes with IoT and cloud-based service communication protocols. The PMFC design is specifically adapted with the proposed EH circuit for the implementation of IoT-WSN based applications. The PMFC-EH system has a maximum power point at 0.71 V, a current density of 5 mA cm ${}^{-2}$ , and a power density of 3.5 mW cm ${}^{-2}$ with a single plant. Considering a sensor node with a current consumption of 0.35 mA, the PMFC-EH green energy system allows a power autonomy for real-time data processing of IoT-based low-power WSN systems.

This paper presents the design of a wireless sensor network (WSN) system for smart estimation of soil conditions. The WSN is formed by very low-power autonomous sensor nodes and employs Internet-of-Things and cloud service communication protocols to generate spatial distribution maps of soil parameters at two different levels below ground. An artificial neural network is used to analyze the measured data and estimate the levels of phosphorus (P) in the soil. This feature eliminates the need to perform time-consuming laboratory analysis to continuously monitor the value of this nutrient. Considering the changing rate of soil phenomena throughout a day, a dynamic power management (DPM) strategy is applied, allowing the system to establish an adaptive balance between its energy consumption and the accuracy of phosphorus estimation. The proposed precision agriculture structure allows the implementation of a flexible methodology that can be adapted to different type of crops and agricultural regions. Experimental results obtained in the lab and on the field corroborate the system’s performance and reliability.

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals’ overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

Plant–Microbial Fuel Cell (P–MFC) is a renewable power source which generates bioelectricity through the plant-microbe interrelationship at the rhizosphere region of a plant. As a step toward sustainable wireless sensing, PMFC can harness the metabolism of microorganism as a catalyst and use organic matter to generate electrical energy. However, these energy sources tend to produce low power outputs (mW), and the P–MFC energy capacity can be affected by perturbations. The P–MFC needs to be analyzed for an accurate integration in harvester circuits and wireless technologies, such as LPWAN, to develop sustainable wireless communication applications. In this study, a P–MFC array is implemented as a promising self-sustainable green energy communication technology for Internet of Things (IoT)-based wireless sensor network (WSN). The serial-parallel configuration of the Dypsis lutescens plant type is characterized and adapted with an energy harvester (EH) circuit which manages the energy from P–MFC. An energy capacity model is proposed to analyze the supercapacitor’s charge response and the recovery effect after each Long-Range (LoRa) transmission of the sensor node. The integration of a power management strategy is used to improve the sustainable operation in each sensor node. Experimental results show an open circuit voltage of 1.75 V, and a short current circuit of 5.6 mA for the serial-parallel configuration of the P–MFC array. The test-scenarios demonstrate sustainable operation of the LoRa-WSNs after one month with a daily generation capacity of 80 J, which is sufficient for the WSNs node operation.

Protecting sensitive data, such as data collected from sensors, is crucial for ensuring the accurate assessment of sensing devices and preventing unauthorized access. In this regard, Internet of Things (IoT) devices offer a promising alternative for in situ monitoring. However, IoT sensing devices are often constrained by limited processing power and memory. Therefore, lightweight and efficient security algorithms are essential. This review paper assesses the implementation of lightweight cryptographic algorithms for power-constrained microcontrollers in IoT systems using symmetric cryptography. The implemented algorithms analyze the resource constraints of the IoT devices and compare their performance for the efficient implementation of secure monitoring systems. Experimental results demonstrate the performance of various lightweight encryption algorithms on low-power microcontrollers. The analysis reveals the comparative performance of these algorithms in terms of average power and energy consumption, memory usage, latency, and throughput.

Electric arc furnaces (EAFs) contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.