In recent years, wearable devices have attracted attention because of their ability to enhance the quality of life. This disruptive technology has helped healthcare professionals with intervening early in chronic diseases, especially amongst independently living patients, and has facilitated real-time monitoring of patients’ vital signs remotely. One of the major bottlenecks that hampers the adoption of wearable device is continuous power supply. Most wearable devices solely depend on battery supply. When the energy stored in the battery is depleted, the operation of wearable devices is affected. To overcome this limitation, efficient energy harvesters for wearable devices are crucial. The paper primarily aims to present a comprehensive classification of different energy sources that can be capitalised to power wearable devices. In addition, this research paper deals with the key challenges that must be considered in the development of autonomous wearable devices for telemedicine applications with a proposed system design for wearable device that uses energy harvesting technology.

A new theory called Vivaldi nonuniform slot profile antenna (VNSPA) is developed based on the nonuniform transmission lines (NTLs) theory and applied to reduce the size of the predesigned compact ultrawide band (UWB) Vivaldi-tapered slot antenna (VTSA) without affecting its performance in terms of matching and bandwidth. Using this theory, 33% of the taper slot length is reduced, resulting in a novel compact UWB Vivaldi nonuniform slot antenna (VNSA). Furthermore, compactness is achieved by performing parametric studies on VNSA with a final 51.94% size reduction. MATLAB optimization codes, CST simulation, and prototype manufacturing and measurement are used to validate the new theory. The proposed antenna provides a measured S ₁₁ < −10.89 dB through 2.9–13.55 GHz with 2.91% and 5.8% bandwidth and maximum realized gain enhancement, respectively, as compared with VTSA’s results. The effectiveness of VNSPA theory is proven by comparing the simulated and measured findings of VNSA and VTSA.

Tracking and monitoring system using radio frequency identification (RFID) have gained a lot of improvements especially for applications that need automation with reduction in human intervention and become more interesting nowadays with the increasing market demand for internet of things (IoT) technologies. The objective of this study is to improve the machine-to-machine (M2M) communication using active RFID with wireless sensor networks (WSNs) with heterogeneous data transfer (regardless of power meter type) for monitoring and identification of household electrical consumption. M2M is a popular technology and has become a part of our daily life. WSN through ZigBee technology is applied to monitor data and to read the load consumption from the electrical household power meter by embedding a tag module into the meter. In-house built-in tag was embedded into an electrical power meter with a power management circuit communicating to a reader at an RF signal of 2.45 GHz based on the star network topology with the sleep mode function. The experimental results indicated that the electrical power meters with embedded tags successfully worked wirelessly with acceptable mean value of electrical consumption difference in Watts more than those without tags (standalone meter), which is in the range of 2.44 W to 2.5 W based on 10-hour measurements.

The study of human crowd density estimation (H-CDE) using radio frequency is limited due to the nature of wireless medium and the advancement of visual-based systems. There were two statistical methods, namely, One-Way Analysis of Variance and Design of Experiment applied in designing the H-CDE system. One-Way Analysis of Variance is used to investigate the difference in signal attenuation between dynamic and static crowds. The Design of Experiment is utilized to identify significant crowd properties that affect wireless signal propagation. The significant factors were later trained into the H-CDE algorithm for the purpose of estimating the human crowd density in a defined sector. A sector comprising three placements of 2.4 GHz ZigBee wireless nodes continuously reported the received signal strength indicator to the main node. The results showed that the H-CDE system was 75.00% and 70.83% accurate in detecting the low and medium human crowd density, respectively. A signal path loss propagation model was also proposed to assist in predicting the human crowd density. The human crowd properties verified by using the statistical approach may offer a new side of understanding and estimating the human crowd density.

The advancement in the field of transportation has been accelerating with policymakers shifting the focus towards autonomous and connected vehicles. Being an important component of the IoT infrastructure, a majority of its deployment is still based on the results of conventional approaches and measurement practice. Factors and the best conditions are identified using trial-and-error approaches which are not entirely accurate. Therefore, a thorough understanding of the influences that affect the performance of the windshield tag and developing a prediction model based on the most influential factor has been presented in this paper. In addition, two machine learning algorithms are proposed to achieve the objective of the paper. Linear regression analysis provides a reliable estimation of the RSSI levels of the windshield tag from all the selected location. The second proposed algorithm is based on the logistic regression model which provides an accurate prediction model based on tag readability by the reader. The predicted tag detectability model coupled with the RSSI level estimation model gives a complete overview of the behavior of the RFID tag when deployed on the windshield of the vehicle. This will serve as a guideline for the RFID transportation application which may extensively rely on RFID as a part of the IoT transformation.