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Currently, with the growth of the Internet of Things devices and the emergence of massive edge resources, security protection content has not only empowered IoT devices with the accumulation of networked computing and storage as a flexible whole but also enabled storing, transferring and processing DIKW (data, information, knowledge, and wisdom) content at the edge of the network from multiple devices in a mobile manner. However, understanding various DIKW content or resources poses a conceptual challenge in unifying the semantics of the core concepts as a starting point. Through building metamodels of the DIKW framework, we propose to cognitively formalize the semantics of the key elements of the DIKW in a conceptual process. The formalization centers on modeling the perceived world only by relationships or semantics as the prime atomic comprising elements. Based on this cognitive world model, we reveal the difference between relationships and entities during the conceptualization process as a foundation for distinguishing data and information. Thereafter, we show the initial case for using this formalization to construct security protection solutions for edge computing scenarios centering on type conversions among typed resources formalized through our proposed formalization of the DIKW.

Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3,0.5), histogram equalization, and contrast increasing (0.5,0.6)) on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines.

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