As WSNs combine with a diversity of next-generation technologies, wireless sensor networks (WSNs) have gained considerable attention as a promising ubiquitous technology. Even though several studies on WSNs are being undertaken, few systematically analyze the security issues relating to them. Moreover, recent systems tend to be implemented without sufficient consideration about owns security requirements, which can lead to lethal threats. Systems that do not consider security requirements may provide attackers the opportunity to reduce the overall efficiency and performance of the system. This means that inadequately applied security requirements can result in defective security of systems. Therefore, in this study, we emphasized the importance of security requirements to raise awareness regarding them. In addition, we analyzed literature that could be improved by including WSNs security requirements such as characteristics, constraints, and threats. Furthermore, we adopted a systematic methodology by referring to reliable literature and performed a different analysis from previous studies. We derived and mapped the different security factors based on the literature and illustrated the relationships of each security factor. Finally, our research compared with studies of a similar type to evaluate whether it provided a significant contribution. In other words, in this study, we analyzed various factors related to WSNs security based on reviewing the literature and show our contribution, such as a systematic analysis framework and factor mapping compared with traditional studies. Though there are some considerations, we expect that this research derived the essential security requirements in any WSNs environments. INDEX TERMS Wireless sensor network, security requirement, next-generation technologies.

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A range of video contents and technology have provided convenience to humans, with realtime video applications-such as surveillance applications-able to contribute to increasing public safety by reducing physical crimes. The development of video technology has made it possible to achieve an improved quality of life. However, this technology can also be exploited and lead to security issues such as physical and digital crimes. Unfortunately, security breaches are increasing in complexity and frequency, making current countermeasures insufficient to prevent them. Given recent trends, we recognize the need for security technology to respond to advanced video crimes. Intelligent security is one of the methods that can be used to respond to these issues. Although research on video data security has been actively conducted, not enough studies have been published on video data security that also addresses intelligent security. Specifically, a classification system for research on video data security has not been provided, and no systematic analysis has been conducted for advanced research. Thus, the purpose of this is to fill in these gaps in existing research. This study offers a classification of research on video data security based on the collection and analysis of related works. Moreover, this study presents an analysis of research on video data security technologies combined with intelligent technologies based on SLR methodology.

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The region of interest (ROI) encryption in the video can reduce the complexity of calculation and improve encryption speed by encrypting only the area containing critical visual information. Above all, ROI encryption can expand the utilization domain (e.g., video surveillance), unlike general encryption methods that de-identify the entire frame. However, the traditional ROI encryption process in high-efficiency video coding (HEVC)/H.265 is more complex than in advanced video coding/H.264, and the encrypted area tends to be wider than the ROI. Thus, cryptographic algorithms are applied outside the ROI, which wastes computing resources and restricts the visual information that needs to be provided. Therefore, this paper proposes a coding unit (CU)-based ROI encryption for HEVC/H.265 video. The proposed method selectively encrypts HEVC/H.265 parameters, such as the intra prediction mode, motion vector (MV), MV sign, transform coefficient (TC), and TC sign, which have significant visual influence, and adopts the tile concept for parallel processing frames. The CU-based ROI encryption reduces complexity by identifying the encryption area based on the CU coordinates, applying encryption only to the CUs associated with the ROI. This approach can preserve the area around the ROI by restricting the reference area. Moreover, it provides up to about 30% faster encryption speed than the traditional method while maintaining performance (i.e., peak signal-to-noise ratio and structural similarity index measure).