Search

In the majority of Internet of Things (IoT) applications, persistent and stable operation is a crucial requirement. While environmental energy-harvesting technologies can enhance IoT’s persistence, they do not guarantee stability. Therefore, we aim to address the stability challenges in solar-powered IoT (SP-IoT) by employing wireless power transmission (WPT) through unmanned aerial vehicles (UAVs). This study focuses on determining the optimal charging mobility of drones for WPT to enhance the stability of nodes operating in a wide area network (WAN)-based SP-IoT environment. The proposed scheme identifies nodes with insufficient solar energy harvesting and defines the optimal charging mobility parameters (hovering position, hovering time, and moving path) to efficiently transmit the drone’s energy to these nodes in a balanced manner. The experimental results confirm that the proposed scheme significantly improves the stability of solar-powered IoT nodes by optimally utilizing the limited energy of the drone.

This article presents novel research on the utilization of a neural-network-based time control system for microwave oven heating of food items within a solar-powered vending machine. The research aims to explore the control of heating time for various food products, considering multiple variables. The neural network controller is calibrated through extensive experimentation, allowing it to accurately predict optimal heating times based on input parameters such as food type, weight, initial temperature, water content, and desired doneness level. The results demonstrate that the neural-network-controlled microwave oven achieves precise and desirable heating durations, mitigating the risk of overheating and ensuring superior food quality and taste. Moreover, the solar-powered vending machine showcases a commitment to sustainable energy sources, effectively reducing dependence on non-renewable energy and minimizing greenhouse gas emissions. To maintain food quality and freshness, a food refrigeration unit is integrated into the vending machine, employing load-balancing technology to control the refrigeration chamber’s temperature effectively. Energy efficiency is prioritized in both the refrigeration unit and the microwave oven through intelligent algorithms and system optimization. The combination of a neural-network-controlled microwave oven, a solar-powered vending machine, and a food refrigeration unit introduces a novel and sustainable approach to food preparation and energy management.

Solar power is a popular option for powering Unmanned Aerial Vehicles (UAVs) due to its ability to provide power for long-endurance flight. However, solar-powered UAVs face challenges, including operational reliability problems. To address their operational reliability issues, this paper proposes an automatic solar tracker system and tests two implementation strategies for controlling a tandem-winged and solar-powered UAV, developed by the Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung. Due to its unconventional configuration, a mathematical model for the UAV is developed to evaluate the vehicle’s dynamic characteristics. Based on these, three feedback control systems are designed, i.e., roll attitude control, a heading tracker, and a solar tracker. Two implementation strategies are proposed to combine the control systems, i.e., the mode-switching and the simultaneous tracking strategies. A series of simulations are then conducted to check the vehicle’s overall flight performance, as well as the gathering of solar energy. The mode-switching strategy was able to gather up to 13% more solar energy than the simultaneous strategy, which could only reach slightly above 2%. Mode-switching, however, resulted in a shorter range compared to the latter, due to the time spent in the charging mode, flying in a circular pattern.

Having an exciting array of applications, the scope of unmanned aerial vehicle (UAV) application could be far wider one if its flight endurance can be prolonged. Solar-powered UAV, promising notable prolongation in flight endurance, is drawing increasing attention in the industries’ recent research and development. This work arose from a Bachelor’s degree capstone project at Hong Kong Polytechnic University. The project aims to modify a 2-metre wingspan remote-controlled (RC) UAV available in the consumer market to be powered by a combination of solar and battery-stored power. The major objective is to greatly increase the flight endurance of the UAV by the power generated from the solar panels. The power system is first designed by selecting the suitable system architecture and then by selecting suitable components related to solar power. The flight control system is configured to conduct flight tests and validate the power system performance. Under fair experimental conditions with desirable weather conditions, the solar power system on the aircraft results in 22.5% savings in the use of battery-stored capacity. The decrease rate of battery voltage during the stable level flight of the solar-powered UAV built is also much slower than the same configuration without a solar-power system.

In solar-powered wireless sensor networks (SP-WSNs), the best use of harvested energy is more important than minimizing energy consumption since energy can be supplied periodically. Meanwhile, as is well known, the reliability of the communication between sensor nodes is very limited due to the resource constraints of sensor nodes. In this paper, we propose an efficient forward error correction (FEC) scheme which can give solar-powered wireless sensor networks more reliable communication. First, the proposed scheme provides energy-adaptive operation for the best use of solar energy. It calculates the amount of surplus energy which can be used for extra operations and then determines the number of additional parity bits for FEC according to this amount of surplus energy. At the same time, it also provides a link quality model that is used to calculate the appropriate number of parity bits for error recovery required for the current data communication environment. Finally, by considering these two parity sizes, it is possible to determine the number of parity bits that can maximize the data reliability without affecting the blacking out of nodes. The evaluation of the performance of the approach was performed by comparing the amount of data collected at the sink node and the number of blackout nodes with other schemes.

By utilizing mobile sinks in wireless sensor networks (WSNs), WSNs can be deployed in more challenging environments that cannot connect with the Internet, such as those that are isolated or dangerous, and can also achieve a balanced energy consumption among sensors which leads to prolonging the network lifetime. However, an additional overhead is required to check the current location of the sink in order for a node to transmit data to the mobile sink, and the size of the overhead is proportional to that of the network. Meanwhile, WSNs composed of solar-powered nodes have recently been actively studied for the perpetual operation of a network. This study addresses both of these research topics simultaneously, and proposes a method to support an efficient location service for a mobile sink utilizing the surplus energy of a solar-powered WSN. In this scheme, nodes that have a sufficient energy budget can constitute rings, and the nodes belonging to these rings (which are called ring nodes) maintain up-to-date location information on the mobile sink node and serve this information to the other sensor nodes. Because each ring node only uses surplus energy to serve location information, this does not affect the performance of a node’s general operations (e.g., sensing, processing, and data delivery). Moreover, because multiple rings can exist simultaneously in the proposed scheme, the overhead for acquiring the position information of the sink can be significantly reduced, and also hardly increases even if the network size becomes larger.

In a wireless sensor network (WSN) environment with frequent errors, forward error correction (FEC) is usually employed at the link layer to achieve reliable transmission. In the FEC scheme, the error correction rate varies depending on the length of parity used for the recovery of broken data. The longer the parity length, the higher the possible error correction rate. However, this also means that the energy consumption increases. Meanwhile, in a solar-powered WSN, the energy of each node can be periodically collected, but the amount of collected energy varies drastically depending on the harvesting environment, including factors such as the weather, season and time of day. Therefore, each node must control energy consumption according to the energy harvesting rate. The scheme proposed in this study executes this control by adaptively adjusting the parity length of FEC according to the given energy budget of a node for the next period. This means that the error recovery rate can be increased as much as possible without adversely affecting the blackout time. Simulation results show that the proposed scheme improves the amount of data collected from the entire network for each environment compared with previous schemes.

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.