Internet of Things Device Capabilities, Architectures, Protocols, and Smart Applications in Healthcare Domain: A Review

Nowadays, the Internet has spread to practically every country around the world and is having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more popular and has a high level of interest in both practitioners and academicians in the age of wireless communication due to its diverse applications. The IoT is a technology that enables everyday things to become savvier, everyday computation towards becoming intellectual, and everyday communication to become a little more insightful. In this paper, the most common and popular IoT device capabilities, architectures, and protocols are demonstrated in brief to provide a clear overview of the IoT technology to the researchers in this area. The common IoT device capabilities including hardware (Raspberry Pi, Arduino, and ESP8266) and software (operating systems, and built-in tools) platforms are described in detail. The widely used architectures that have been recently evolved and used are the three-layer architecture, SOA-based architecture, and middleware-based architecture. The popular protocols for IoT are demonstrated which include CoAP, MQTT, XMPP, AMQP, DDS, LoWPAN, BLE, and Zigbee that are frequently utilized to develop smart IoT applications. Additionally, this research provides an in-depth overview of the potential healthcare applications based on IoT technologies in the context of addressing various healthcare concerns. Finally, this paper summarizes state-of-the-art knowledge, highlights open issues and shortcomings, and provides recommendations for further studies which would be quite beneficial to anyone with a desire to work in this field and make breakthroughs to get expertise in this area.

Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.