2690-6791ISSN (Online)
2769-786XISSN (Print)
In the last two decades, Wireless Sensor Networks (WSNs) are gaining more popularity, where the concept of WSN always exists when everything connects. Almost of WSN applications cover wide area and large spaces for assessing and monitoring certain phenomenon. Moreover, WSN components have been integrated in daily life objects or things (object, place, and person), so that they could be monitored and controlled. As a result, a new paradigm called the Internet of Things (IoT) connects WSN components to the Internet to be globally monitored and controlled representing the surrounding environmental events and conditions. The future IoT is called the Web of Things (WoT), which visualizes the IoT data (sensory data) using current web tools and services (HTTP, RESTful services). This paper presents an overview of the WSNs, the IoT and its future paradigm (WoT) discussing key elements, main layers, main challenges, and annotation formats.
Read MoreDoi: https://doi.org/10.54216/JISIoT.040201
Vol. 4 Issue. 2 PP. 56-68, (2021)
Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.
Read MoreDoi: https://doi.org/10.54216/JISIoT.040202
Vol. 4 Issue. 2 PP. 69-98, (2021)
With the development of society, people pay more and more attention to the safety of food, and relevant laws and policies are gradually introduced and being improved. The research and development of agricultural product quality and safety system has become a research hot spot, and how to obtain the Web information of the system effectively and quickly is the focus of the research, so it is essential to carry out the intelligent extraction of Web information for agricultural product quality and safety system. The purpose of this paper is to solve the problem of how to efficiently extract the Web information of the agricultural product quality and safety system. By studying the Web information extraction methods of various systems, the paper makes a detailed analysis and research on how to realize the efficient and intelligent extraction of the Web information of the agricultural product quality and safety system. This paper analyzes in detail all kinds of template information extraction algorithms used at present, and systematically discusses a set of schemes that can automatically extract the Web information of agricultural product quality and safety system according to the template. The research results show that the proposed scheme is a dynamically extensible information extraction system, which can independently implement dynamic configuration templates according to different requirements without changing the code. Compared with the general way, the Web information extraction speed of agricultural product quality safety system is increased by 25%, the accuracy is increased by 12%, and the recall rate is increased by 30%.
Read MoreDoi: https://doi.org/10.54216/JISIoT.040203
Vol. 4 Issue. 2 PP. 99-110, (2021)