ABSTRACT
This qualitative study aimed to explore the current status, practices, and challenges of Internet of Things (IoT) implementation and to develop an IoT framework for Industry 4.0 in Malaysia. Industry 4.0 enhances a company's manufacturing competitiveness and efficiency. However, the implementation of Industry 4.0 in Malaysian small- and medium-sized enterprises (SMEs) is still in its early stages. Five participants from three different SMEs were selected for online interviews and a focus group. Due to the COVID-19 pandemic, the interviews were conducted online and lasted about 30 to 45 min. The data collected from the interviews were analyzed through thematic analysis and used to validate the literature review and to identify gaps in existing frameworks. The IoT framework was developed through a focus group of experts. This study found that the implementation of Industry 4.0 is relatively low in Malaysian manufacturing SMEs. SMEs are facing various challenges, including the need for education and training, budget constraints, and a lack of experience and knowledge among workers. This study found that the positive impact of IoT implementation included improved internal communication, reduced errors, and enhanced product quality and safety. In addition, this study resulted in the development of an IoT framework for SMEs in Malaysia. © 2023 by the authors.
ABSTRACT
Nowadays, smart health has been developing in the healthcare system by implementing the Internet of Things. One of the implementations of smart health is remote monitoring systems for rehabilitating patients such as stroke. Today, with the rising Covid-19 pandemic, patients undergoing rehabilitation at home have difficulties meeting with their doctors due to the moving restrictions. The healthcare facilities are focused on treating Covid-19 patients. These restrictions have caused doctors and patients not to meet regularly to collect their data on the rehabilitation progress. This research suggests building a prototype to monitor a post-stroke patient’s lower limb strength rehabilitation process by using embedded sensors and microcontrollers. The prototype will measure key components of the rehabilitation process and will be discussed in the later section of this paper.
ABSTRACT
Internet of Things (IoT) platforms are desired for realizing an early warning system of COVID-19 detection. The key challenge is to provide solution including sensors to meet the application requirements. We manipulate the diarrhea symptom to assess the COVID-19 case where the virus indirectly can be detected in the wastewater. We proposed COVID-19 early warning detection system architecture from the wastewater comprises of five layers, namely perception layer, connectivity layer, middleware layer, application layer and business layer. Our architecture integrates IoT and machine learning (ML) components to build the total solution. Benefits on the stakeholders can be anticipated including the local authority and hospitals. Key challenges are also discussed in developing the COVID-19 early warning detection system based on the wastewater circumstances. © 2020 IEEE.