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The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors. [ABSTRACT FROM AUTHOR]

Advancements in Healthcare Internet of Things (H-IoT) systems have created new opportunities and solutions for healthcare services, including the remote treatment and monitoring of patients. In addition, the security and privacy of personal health data must be ensured during data transfer. Security breaches in H-IoT can have serious safety and legal implications. This comprehensive review provides insights about secured data accession by employing cryptographic platforms such as H-IoT in big data, H-IoT in blockchain, H-IoT in machine learning and deep learning, H-IoT in edge computing, and H-IoT in software-defined networks. With this information, this paper reveals solutions to mitigate threats caused by different kinds of attacks. The prevailing challenges in H-IoT systems, including security and scalability challenges, real-time operating challenges, resource constraints, latency, and power consumption challenges are also addressed. We also discuss in detail the current trends in H-IoT, such as remote patient monitoring and predictive analytics. Additionally, we have explored future prospects, such as leveraging health data for informed strategic planning. A critical analysis performed by highlighting the prevailing limitations in H-IoT systems is also presented. This paper will hopefully provide future researchers with in-depth insights into the selection of appropriate cryptographic measures to adopt an energy-efficient and resource-optimized healthcare system. [ABSTRACT FROM AUTHOR]

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality. [ABSTRACT FROM AUTHOR]

Summary: Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real‐time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT‐based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact. [ABSTRACT FROM AUTHOR]