The fourth Industrial Revolution is rapidly growing due to the development of ICT and the need for organic linkage in the industry. The Internet of Things (IoT), in which objects and objects from active relationships such as sensing, networking and mutual cooperation without human intervention is the core technology of the fourth Industrial Revolution, which can be linked to various industrial fields. As the IoT environment spreads, IoT devices are continuously increasing in various industries. IoT devices connected to the Internet are as different as each application environment. Many information is derived from the interaction between an IoT device and a device or between a human and an IoT device, as well as devices that provide simple data such as sensing. This paper proposes a support method to enable extended service search when users search for services using information generated in IoT environment. The existing service discovery method focuses on the method by which the user selects the service based on the simple service information disclosed by the service provider. In order to solve the problem of existing service discovery method, this study proposed a support method that enables users to search service discovery flexibly even when using existing methods in service discovery in IoT environment. The proposed method provides a user-centered service search environment construction through a search method using user IoT information which was not considered in the existing service search. This makes up for the problem of service availability and provider-oriented service discovery through the establishment of a user service discovery environment.
As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.
Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.
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