IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

No abstract

Viruses are omnipresent and persistent in wastewater, which poses a risk to human health. In this review, we summarize the different qualitative and quantitative methods for virus analysis in wastewater and systematically discuss the spatial distribution and temporal patterns of various viruses (i.e., enteric viruses, Caliciviridae (Noroviruses (NoVs)), Picornaviridae (Enteroviruses (EVs)), Hepatitis A virus (HAV)), and Adenoviridae (Adenoviruses (AdVs))) in wastewater systems. Then we critically review recent SARS-CoV-2 studies to understand the ongoing COVID-19 pandemic through wastewater surveillance. SARS-CoV-2 genetic material has been detected in wastewater from France, the Netherlands, Australia, Italy, Japan, Spain, Turkey, India, Pakistan, China, and the USA. We then discuss the utility of wastewater-based epidemiology (WBE) to estimate the occurrence, distribution, and genetic diversity of these viruses and generate human health risk assessment. Finally, we not only promote the prevention of viral infectious disease transmission through wastewater but also highlight the potential use of WBE as an early warning system for public health assessment.

In this study, artificial intelligence and image recognition technologies are combined with environmental sensors and the Internet of Things (IoT) for pest identification. Real-time agricultural meteorology and pest identification systems on mobile applications are evaluated based on intelligent pest identification and environmental IoT data. We combined the current mature AIoT technology and deep learning and applied it to smart agriculture. We used deep learning YOLOv3 for image recognition to obtain the location of Tessaratoma papillosa and analyze the environmental information from weather stations through Long Short-Term Memory (LSTM) to predict the occurrence of pests. The experimental results showed that the pest identification accuracy reached 90%. Precise positioning can effectively reduce the amount of pesticides used and reduce pesticide damage to the soil. The current research provides the location of the pest and the extent of the pests to farmers can accurately use pesticide application at a precise time and place and thus reduce the agricultural workforce required for timely pest control, thus achieving the goal of smart agriculture. The proposed system notifies farmers of the presence of different pests before they start multiplying in large numbers. It improves overall agricultural economic value by providing appropriate pest control methods that decrease crop losses and reduce the environmental damage caused by the excessive usage of pesticides.