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.

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.

In this paper, a Kalman framework is proposed for dynamic energy-saving in wireless sensor networks used to monitor urban noise pollution. The energy-saving framework implements a dynamic power management strategy (DPMS) with the Kalman algorithm that varies the sensor node’s sleep period according to the measured noise levels. An Internet-of-Things (IoT) edge-based self-sustaining Long Range (LoRa) network is developed and used for ubiquitous monitoring and analysis of urban noise pollution. The network consists of a star topology with six LoRa battery-free wireless sensing nodes deployed in Mérida city downtown, each node powered by a green facade structure consisting of an array of Plant-Microbial Fuel Cells (P-MFC). The sensor node’s prototype was implemented following Mexican regulations, transmitting the data packets with the open frequency band of 915 MHz and monitoring the LoRa network from a web page. Experimental results proof a sustainable operation with a green facade P–MFC array power generation of 112.1 mW with an open circuit voltage of 2.7 V and a short-circuit current of 180 mA. The sensor node’s average power consumption is 11.2 mW, therefore sufficient energy is generated for continuous monitoring. The efficient Kalman-DPMS is also tested with the urban noise measurement estimation and adjusting the sleep period only if the urban measurement state estimation is above the threshold normativity. The system’s low-power consumption allows to perform 70 continuous LoRa transmissions even when the energy harvester source is absent, and the power capacity of the green facade restores a supercapacitor full charge after only 4 minutes of a LoRa transmission. A 23.6% of energy was saved with Kalman-DPMS in comparison with a continuous measurement system of 10 min uniform-sleep period.

Two control strategies for power flow control in hybrid electric vehicles (HEVs) with parallel configuration and a planetary gear system as a power coupling device between the internal combustion engine and the electric machine are proposed in this paper. The aim of both strategies is to determine, for a given driving cycle, an appropriate mixture of the power provided by the two engines. Performance is measured not only in terms of fuel consumption; driving cycle tracking and preservation of energy in the bank of batteries are also considered. The first strategy, named the PGS strategy as it is designed around the planetary gear system, is heuristic, inspired by bang–bang optimal control formulations and has low computational load, while the second is an optimal one derived from Pontryagin’s minimum principle (PMP). It is shown that, under appropriate choice of the weighting parameters in the Hamiltonian of the PMP, both strategies give very similar results and, therefore, that the PGS strategy corresponds to a feasible solution to an optimization problem. Both strategies can be implemented in real time, however, the PGS strategy is easier to tune. Tuning of the strategies’ parameters is independent of the driving cycle. The power flow control laws are continuous and enforce the use of the internal combustion engine with the maximum possible efficiency. The strategies are tested with simulations of a power train of a hybrid diesel–electric bus subjected to the demands of four representative urban area driving cycles. Although optimization solutions are based on simplified dynamic models, simulation results are verified with more detailed dynamic models of the HEV main subsystems. This allows us to evaluate the accuracy of the results and to verify the hypothesis established in the optimization formulation. Simulation results indicate that both strategies attain good fuel consumption reduction levels.

In this paper, we investigate the interval observer problem for a class of discrete-time nonlinear systems, in absence or presence of external disturbances and parametric uncertainties. The interval observers depend on the design of two preserving order observers, providing lower and upper estimations of the state. The main objective is to apply the stability radii notions and cooperativity property in the estimation error systems in order to guarantee that the lower/upper estimation is always below/above the real state trajectory at each time instant from an appropriate initialization, and the estimation errors converge asymptotically towards zero when the disturbances and/or uncertainties are vanishing. For the disturbed case, the estimation errors practically converge to a vicinity of zero, while the lower/upper estimations preserve the partial ordering with respect to the state trajectory. The design conditions, that are valid for Lipschitz nonlinearities, can be expressed as Linear Matrix Inequalities (LMIs). A numerical simulation example is provided to verify the effectiveness of the proposed method.