The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

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Due to the advanced growth in multimedia data and Cloud Computing (CC), Secure Image Archival and Retrieval System (SIARS) on cloud has gained more interest in recent times. Content based image retrieval (CBIR) systems generally retrieve the images relevant to the query image (QI) from massive databases. However, the secure image retrieval process is needed to ensure data confidentiality and secure data transmission between cloud storage and users. Existing secure image retrieval models faces difficulties like minimum retrieval performance, which fails to adapt with the large-scale IR in cloud platform. To resolve this issue, this article presents a SIARS using deep learning (DL) and multiple share creation schemes. The proposed SIARS model involves Adagrad based convolutional neural network (AG-CNN) based feature extractor to extract the useful set of features from the input images. At the same time, secure multiple share creation (SMSC) schemes are executed to generate multiple shares of the input images. The resultant shares and the feature vectors are stored in the cloud database with the respective image identification number. Upon retrieval, the user provides a query image and reconstructs the received shared image to attain the related images from the database. An elaborate experimentation analysis is carried out on benchmark Corel10K dataset and the results are examined in terms of retrieval efficiency and image quality. The attained results ensured the superior performance of the SIARS model on all the applied test images.

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.