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.

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.

Long power wide area networks (LPWAN) systems play an important role in monitoring environmental conditions for smart cities applications. With the development of Internet of Things (IoT), wireless sensor networks (WSN), and energy harvesting devices, ultra-low power sensor nodes (SNs) are able to collect and monitor the information for environmental protection, urban planning, and risk prevention. This paper presents a WSN of self-powered IoT SNs energetically autonomous using Plant Microbial Fuel Cells (PMFCs). An energy harvesting device has been adapted with the PMFC to enable a batteryless operation of the SN providing power supply to the sensor network. The low-power communication feature of the SN network is used to monitor the environmental data with a dynamic power management strategy successfully designed for the PMFC-based LoRa sensor node. Environmental data of ozone (O₃) and carbon dioxide (CO₂) are monitored in real time through a web application providing IoT cloud services with security and privacy protocols.