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.

Cobalt-SiC Schottky barrier diodes have been fabricated and their characteristics were investigated when exposed to alpha radiation. Two new defects with energies, EC – 0.40 eV and EC – 0.65 eV were induced after exposure to alpha radiation. Characteristics such as ideality factor, barrier height, series resistance and carrier concentration change with increasing alpha radiation fluency were determined. DLTS analysis was done and analysis on the plots is included. Increased fluency gave rise to an increase in the peaks to the induced peaks and the inherent peak E0.67 while the other three inherent peaks were suppressed. Isochronal annealing was carried out at 20 minute intervals and 100 oC steps to study the thermal stability of the induced defects. The induced defects were suppressed after annealing at 400 oC showing that the defects are not thermally stable.

The rapid urbanization coupled with the aging infrastructure and lack of resources makes drainage management of urban centres in Africa a critical issue. This paper provides the design, implementation, and high-quality assessment of a Smart Integrated Drainage and Sewage Monitoring and Control System (SIDSMCS) customized to the Zimbabwean urban environments. Based on Design Science Research approach, a low-cost IoT solution was built, which combines ESP32 microcontrollers, hybrid communication (Wi-Fi/LoRa/GSM), solar power resiliency, and predictive analytics. The prototype was implemented in 3 high-density locations in Harare in 2023-2024 rainy season. Findings show that there were substantial improvements: response time dropped by 5.2 to 1.5 days (71 percent improvement), 70 percent of possible overflows avoided, 85percent prediction accuracy of blockages, and 73 percent reduction in cost per intervention. Technical evaluation was done thoroughly, consisting of sensor calibration statistics, uncertainty quantification, fault tolerance mechanisms, and economic sensitivity analysis. The proposed system incorporates predictive algorithms and delivers results that match, and in certain respects may surpass, those of smart drainage solutions deployed globally, while operating with drastically reduced resource requirements. This provides empirical evidence that supports the viability of context-specific smart infrastructure in African urban centres.