The recent advances in ultralow power device integration, communication electronics, and microelectromechanical systems (MEMS) technology have fuelled the emerging technology of wireless sensor networks (WSNs). The spatial distributed nature of WSNs often requires that batteries power the individual sensor nodes. One of the major limitations on performance and lifetime of WSNs is the limited capacity of these finite power sources, which must be manually replaced when they are depleted. Moreover, the embedded nature of some of the sensors and hazardous sensing environment make battery replacement very difficult and costly. The process of harnessing and converting ambient energy sources into usable electrical energy is called energy harvesting. Energy harvesting raises the possibility of self-powered systems which are ubiquitous and truly autonomous, and without human intervention for energy replenishment. Among the ambient energy sources such as solar energy, heat, and wind, mechanical vibrations are an attractive ambient source mainly because they are widely available and are ideal for the use of piezoelectric materials, which have the ability to convert mechanical strain energy into electrical energy. This paper presents a concise review of piezoelectric microgenerators and nanogenerators as a renewable energy resource to power wireless sensors.

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined.

Convolutional neural networks (CNNs) are the gold standard in the machine learning (ML) community. As a result, most of the recent studies have relied on CNNs, which have achieved higher accuracies compared with traditional machine learning approaches. From prior research, we learned that multi-class image classification models can solve leaf disease identification problems, and multi-label image classification models can solve leaf disease quantification problems (severity analysis). Historically, maize leaf disease severity analysis or quantification has always relied on domain knowledge—that is, experts evaluate the images and train the CNN models based on their knowledge. Here, we propose a unique system that achieves the same objective while excluding input from specialists. This avoids bias and does not rely on a “human in the loop model” for disease quantification. The advantages of the proposed system are many. Notably, the conventional system of maize leaf disease quantification is labor intensive, time-consuming and prone to errors since it lacks standardized diagnosis guidelines. In this work, we present an approach to quantify maize leaf disease based on adaptive thresholding. The experimental work of our study is in three parts. First, we train a wide variety of well-known deep learning models for maize leaf disease classification, then we compare the performance of the deep learning models and finally extract the class activation heatmaps from the prediction layers of the CNN models. Second, we develop an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge. Lastly, we use these regions of interest to estimate image leaf disease severity. Experimental results show that transfer learning approaches can classify maize leaf diseases with up to 99% accuracy. With a high quantification accuracy, our proposed adaptive thresholding method for CNN class activation maps can be a valuable contribution to quantifying maize leaf diseases without relying on domain knowledge.

A comparative study of the traditional PZT ceramics and new single crystals is critical in selecting the best material and optimization of transducer design for applications such as conversion of ambient vibrations into useful electrical energy. However, due to material and fabrication costs and the need for rapid prototyping while optimizing transducer design, primary comparisons can be based on simulation. In this paper, the COMSOL Multiphysics finite element package was used to study the direct piezoelectric effect when an external load is applied at the free end of a piezoelectric composite beam. The primary output parameters such as electric potential and electric field were studied as a function of the input strain and stress. The modeling is presented for the relatively new single crystal lead magnesium niobate-lead titanate (PMN32) and three different lead zirconate titanate ceramics (PZT-5A, PZT-5H, and PZT-4). Material performance was assessed by using a common geometry and identical excitation conditions for the different piezoelectric materials. For each material, there are three analyses performed, namely, static, eigenfrequency, and transient/time-dependent analysis. Comparative results clearly suggest that the new crystal material PMN32 is capable of outperforming presently useing piezoelectric ceramics for voltage generation.

This paper presents the experimental design and characterization of an optimized radio-frequency energy-harvesting prototype device to operate in the GSM 900 band. The energy harvesting device is based on an off-the-shelf GSM antenna, Schottky diode rectifier, and was implemented on a standard FR4 substrate. The experimental results show that the harvesting device is capable of generating 0.3 V DC voltage at an input power of −17 dBm at 956 MHz frequency. The measured efficiency of the developed energy harvesting system was 68%, at R_L = 1.6 kΩ, which is a very significant performance relative to other prototypes of the same class reported in literature.