The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

With the continuous improvement of Internet of Things (IoT) technologies, various IoT platforms are under development. However, each IoT platform is developed based on its own device identification system. That is, it is challenging to identify each sensor device between heterogeneous IoT platforms owing to the resource request format (e.g., device identifier) varying between platforms. Moreover, despite the considerable research focusing on resource interoperability between heterogeneous IoT platforms, little attention is given to sensor device identification systems in diverse IoT platforms. In order to overcome this problem, the current work proposes an IoT device name system (DNS) architecture based on the comparative analysis of heterogeneous IoT platforms (i.e., oneM2M, GS1 ‘Oliot’, IBM ‘Watson IoT’, OCF ‘IoTivity’, FIWARE). The proposed IoT DNS analyzes and translates the identification system of the device and resource request format. In this process, resource requests between heterogeneous IoT platforms can be reconfigured appropriately for the resources and services requested by the user, and as a result, users can use heterogeneous IoT services. Furthermore, in order to illustrate the aim of the proposed architecture, the proposed IoT DNS is implemented and tested on a microcomputer. The experimental results show that a oneM2M-based device successfully performs a resource request to a Watson IoT and FIWARE sensor devices.

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems.

With the development of the fourth industrial technology, such as the Internet of Things (IoT) and cloud computing, developed countries including the U.S. are investigating the efficiency of national defense, the public sector and national innovation, and constructing the infrastructure for cloud computing environments through related policies. The Republic of Korea is enacting the related legislation and considering the fourth industrial technology in various fields. Particularly, it is considering the adaptation of the cloud to the command and control system in the national defense sector; hence, related research and pilot projects are being conducted. However, if the existing information system is converted to a cloud computing system by introducing IoT devices, existing security requirements cannot solve problems related to the security vulnerabilities of cloud computing. Therefore, to build a cloud-based secure command and control system, it is necessary to derive additional cloud computing-related security requirements that are lacking in the existing security requirements, and to build a secure national defense command and control system architecture based upon it. In this paper, we derive security requirements for a cloud-based command control system, propose a security architecture designed based thereupon, and implement a security architecture with an open-stack-based cloud platform, “OpenStack”.

The rapid advancement of the industrial Internet of things (IIoTs) has facilitated the development of diverse platforms and management technologies. However, the lack of standardized protocols and interoperability between platforms developed by various organizations hinders seamless integration. These limitations represent the primary challenge in access control between different IIoT platforms, resulting in limited service utilization across heterogeneous platforms. This study addresses the challenges of effectively sharing user attributes and representations for access control between heterogeneous IIoT platforms using blockchain and a metadata registry (MDR). Utilizing blockchain ensures tamper-proof and transparent attribute sharing, whereas the MDR facilitates the accurate mapping of user attributes for cross-platform resource access control. We demonstrated the feasibility of the proposed framework by successfully implementing access control between two representative standard platforms, namely oneM2M and FIWARE. Our results indicate that enhancing service utilization through access control between heterogeneous IIoT platforms can improve security and efficiency across various industries.

As IoT technology is applied to various fields, many IoT platforms are being developed to provide connections between numerous services and users. However, this diversity and heterogeneity can limit interoperability. Users on different platforms cannot be authenticated because IoT platforms have different representations and data formats for the same resource and do not understand each other's data models. In addition, users on other platforms cannot grant permissions because they do not understand the policies and required attribute values of other platforms. To solve these problems, this study proposes a framework focusing on access control, such as authentication and authorization for resource use between various IoT platforms.