The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.

Biomedical telemetry has gained a lot of attention with the development in the healthcare industry. This technology has made it feasible to monitor the physiological signs of patient remotely without traditional hospital appointments and follow up routine check-ups. Implantable Medical Devices(IMDs) play an important role to monitor the patients through wireless telemetry. IMDs consist of nodes and implantable sensors in which antenna is a major component. The implantable sensors suffer a lot of limitations. Various factors need to be considered for the implantable sensors such as miniaturization, patient safety, bio-compatibility, low power consumption, lower frequency band of operation and dual-band operation to have a robust and continuous operation. The selection of the antenna is a challenging task in implantable sensor design as it dictates performance of the whole implant. In this paper a critical review on implantable antennas for biomedical applications is presented.

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps.

Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS‐assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS‐assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi‐users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed.

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps.