Environmental hazards like water and air pollution, extreme weather, or chemical exposures can affect human health in a number of ways, and it is a persistent apprehension in communities surrounded by mining operations. The application of modern technologies in the environmental monitoring of these Human-made hazards is critical, because while not immediately health-threatening may turn out detrimental with unwanted negative effects. Enabling technologies needed to realise this concept is multifaceted and most especially involves deploying interconnected Internet of Things (IoT) sensors, existing legacy systems, enterprise networks, multi layered software architecture (middleware), and event processing engines, amongst others. Currently, the integration of several early warning systems has inherent challenges, mostly due to the heterogeneity of components. This paper proposes transversal microservice-based middleware aiming at increasing data integration, interoperability, scalability, high availability, and reusability of adopted systems using a container orchestration framework for a multi-hazard early warning system. Devised within the scope of the ICMHEWS project, the proposed platform aims at improving known challenges.
In recent years, the Internet of Things (IoT) has become a household name both in research and commercial domains. There are numerous practical applications of IoT, ranging from sophisticated solutions in big factories to simple smart homes devices, such as temperature monitors. Common to these solutions is the need to transmit data from a sensing source to a different location where the data is read, processed, analyzed, or simply stored. Numerous models and protocols have been developed to ferry IoT data, including queueing, client-server, and publish-subscribe models, all with their merits and demerits. In this paper, a comparison of two models for transmitting live/streaming IoT telemetry data is done. The Message Queue Telemetry Transport (MQTT), a publish-subscribe model, is compared with WebSocket, a client-server model, in terms of throughput, round trip time and system utilization. Obtained results reveal that MQTT is more suited for transmitting live IoT data than WebSocket, as it had better throughput, utilized less system resource, however it was slightly slower than WebSocket.
It is widely recognized that digital archiving represents many advantages compared to manually preserved documents. These include i) reduced risk of losing data, ii) eco-friendliness, iii) data security, iv) faster access to data, v) simple data management, vi) overall costs saving, and vii) potential for data recovery. However, in contrast to developed nations, which have experienced a steady maturity in the field, digital archiving is still in its infancy in developing countries, especially in Africa, where years of slavery, colonization, both economic and political issues, and wars have deprived nations of their history. Building around techniques of the fourth industrial revolution (4IR), this paper addresses this gap by proposing a digital archiving model called “Archiving 4.0”. The model curates a dataset of images of political figures in the Democratic Republic of Congo (DRC) from different sources and classifies these images using machine learning techniques to achieve facial recognition that will help recognize historical people in photographs and videos. To the best of our knowledge, the proposed dataset constitutes the first attempt at digital archiving in the political space of the DRC and provides an example that can be emulated to spin out related works on the African continent where digital archiving is needed for political research studies and also for the preservation of the history of nations. Through performance evaluation, accuracy, precision, recall, and loss; the paper reveals that Transfer learning outperforms traditional machine learning on different metrics of interest when using the generated dataset: 93
Small, Medium, and Micro Enterprises (SMMEs) in the informal sector are faced with a myriad of challenges, including access to loans. Because of its immutability, blockchain technology is said to be the solution to these types of challenges. Through blockchain-enabled Mobile Applications, informal SMMEs can record their financials, and give permission to external viewers such as banks to view when applying for a loan. However, there exists a possibility that these informal SMMEs may be digitally excluded from the use and benefits of Blockchain technology due to issues such as affordability, informality, and lack of skills. For this reason, this study aims to develop a model for the digital inclusion of SMMEs in the informal sector in South Africa, using Blockchain technology to solve their challenge of access to loans. This was achieved through a combination of the Digital Inclusion Model and the Theory of Planned Behaviour. A literature review was conducted, from which propositions were derived to develop the model. Based on the literature, we found that lack of access to and affordability of smartphones and the internet, lack of digital skills, and poor internet connection may have a negative impact on digital inclusion. We conclude that pre-conditions such as digital infrastructure need to be in place for digital inclusion of SMMEs in the informal sector. Furthermore, SMMEs should be educated on Blockchain technology and its benefits. This has the potential to greatly impact their view of the technology, which will in turn improve adoption and therefore digital inclusion.
Mobile learning (m-learning) offered opportunities for learning before COVID-19 disease (COVID-19) but experienced a significant surge in usage since learning moved to the homes. This systematic review collates studies published between 2020 and 2021 to understand the impact, challenges, opportunities, and gaps in m-learning during COVID-19. The key findings from the 26 papers reveal the extent to which many families, educators, and governments were caught off guard, some glaring family challenges, and social and digital divides inscribed in technology infrastructure, especially in developing countries. M-learning assumes adequate internet and digital technology infrastructure which means that developing countries continue to be affected the most. A research agenda is created for how m-learning can be better leveraged for future disruptions.
