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.

Healthcare monitoring systems in hospitals and other health facilities have grown significantly, and portable healthcare monitoring systems using emerging technologies such as Internet of Things (IoT) technologies have aided the advancement of healthcare monitoring systems. Many studies have focused on intelligent healthcare systems in an IoT context to improve components, including wearable sensors and hardware devices, intelligent data collecting and processing, and network connections. Even while these applications are necessary and helpful for enhancing wireless healthcare settings related to monitoring, detection, and diagnostics, it might be challenging to fully understand how IoT characteristics are currently intertwined with its architecture. Accordingly, this work adds to the academic literature by thoroughly reviewing all significant areas of wireless healthcare monitoring system advancements. This research also examines a state-of-the-art healthcare monitoring system component under IoT. One hundred and ninety-four related articles were collected and filtered based on the system components defined. This study includes a thorough review and a list of genuine concerns with novel critical solutions. The study can facilitate academics and practitioners by giving them direction and vital information for future research.

Ocean waves that impact along the coast are among the most promising renewable energy sources that can be extracted from the environment. The ocean’s energy can be extracted by a few methods and converted from kinetic energy into electrical energy using turbine blades and power generators installed in areas with strong waves. The best and most widely established wave energy converters use an Oscillating Water Column (OWC) conversion concept. During the past few years, numerous research projects have been conducted to ensure a continuous development of OWC technologies that could provide more systematic, consistent, and environmentally friendly electrical supply sources to the nearby communities and electric-powered facilities. OWC has distinctive techniques for every section of the energy converting process, differing from other methods. Several things need to be considered to ensure that a maximum amount of energy can be generated from the established system. This paper discusses the OWC system design characteristics and functions. This paper also summarizes the current technology developments for the OWC and their achievements.

Malaysia has a great potential to harness energy in water due to its long coastline within the South China Sea and the Straits of Malacca. Malaysia's energy mix could be improved using marine current energy devices (MCEDs) to replace fossil fuel and it is predictable energy compare to hydropower, solar photovoltaic (PV), and biomass. However, MCEDs is not been fully developed in Malaysia. The objectives of this paper are to provide a useful background for policymakers or researchers in the types of MCEDs and potential sites location of MCEDs that are applicable in Malaysia. This review also discusses the issues and challenges of MCEDs in Malaysia. Five types of MCEDs were discussed including tidal range device, tidal stream turbine, wave energy converter, ocean thermal energy conversion, and salinity gradient energy. These MCEDs are compared for their suitability of application in Malaysia. Among all MCEDs, tidal stream turbine is identified as high potential and commercially viable in Malaysia. However, ocean characteristics in Malaysia are low kinetic energy-flux density, low current speed, low tide, and shallow water; only fulfill the minimum requirement of tidal stream turbine making the tidal stream energy resources not significant enough to contribute to the nation's energy mix. Therefore, using diffuser augmented tidal stream turbines to increase the flow velocity should be studied thoroughly.

Device-to-Device (D2D) communication underlaying cellular network is a capable system for advancing the spectrum’s efficiency. However, in this condition, D2D generates cross-channel and co-channel interference for cellular and other D2D users, which creates an excessive technical challenge for allocating the spectrum. Despite this, massive connectivity is another issue in the 5G and beyond networks that need to be addressed. To overcome this problem, non-orthogonal multiple access (NOMA) is integrated with the D2D groups (DGs). In this paper, our target is to maximize the sum throughput of the overall network while maintaining the signal-to-interference noise ratio (SINR) of the cellular and D2D users. To achieve the target, a discriminated spectrum distribution framework dependent on multi-agent deep reinforcement learning (MADRL), termed a deep deterministic policy gradient (DDPG) is proposed. Here, it shares the global historical states, actions, and policies using the duration of central training. Furthermore, the proximal online policy scheme (POPS) is used to decrease the computation complexity of training. It utilized the clipping substitute technique for the modification and reduction of complexity at the training stage. The simulation results demonstrated that the proposed scheme POPS attains 16.67%, 24.98%, and 59.09% higher performance than the DDPG, Deep Dueling and deep Q-network (DQN).