A Survey of multimedia streaming in wireless sensor networks: progress, issues and design challenges

Advancements in Complementary Metal Oxide Semiconductor (CMOS) technology have enabled Wireless Sensor Networks (WSN) to gather, process and transport multimedia (MM) data as well and not just limited to handling ordinary scalar data anymore. This new generation of WSN type is called Wireless Multimedia Sensor Networks (WMSNs). Better and yet relatively cheaper sensors that are able to sense both scalar data and multimedia data with more advanced functionalities such as being able to handle rather intense computations easily have sprung up. In this paper, the applications, architectures, challenges and issues faced in the design of WMSNs are explored. Security and privacy issues, over all requirements, proposed and implemented solutions so far, some of the successful achievements and other related works in the field are also highlighted. Open research areas are pointed out and a few solution suggestions to the still persistent problems are made, which, to the best of my knowledge, so far have not been explored yet

A local-holistic graph-based descriptor for facial recognition

Face recognition remains critical and up-to-date due to its undeniable contribution to security. Many descriptors, the most vital figures used for face discrimination, have been proposed and continue to be done. This article presents a novel and highly discriminative identifier that can maintain high recognition performance, even under high noise, varying illumination, and expression exposure. By evolving the image into a graph, the feature set is extracted from the resulting graph rather than making inferences directly on the image pixels as done conventionally. The adjacency matrix is created at the outset by considering the pixels’ adjacencies and their intensity values. Subsequently, the weighted-directed graph having vertices and edges denoting the pixels and adjacencies between them is formed. Moreover, the weights of the edges state the intensity differences between the adjacent pixels. Ultimately, information extraction is performed, which indicates the importance of each vertex in the graphic, expresses the importance of the pixels in the entire image, and forms the feature set of the face image. As evidenced by the extensive simulations performed, the proposed graphic-based identifier shows remarkable and competitive performance regarding recognition accuracy, even under extreme conditions such as high noise, variable expression, and illumination compared with the state-of-the-art face recognition methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

Improved exploiting modification direction steganography for hexagonal image processing

Steganography has made significant advances in the Square-pixel-based Image Processing (SIP) domain, but to our knowledge, no work has yet been done in Hexel (Hexagonal Pixel)-based Image Processing (HIP). This paper presents a HIP-domain data hiding method that exploits and improves the SIP-domain Exploiting Modification Direction (EMD) embedding scheme. The proposed method, Hexagonal EMD (HexEMD), utilizes a HIP-domain cover image's hexagonal nature and infrastructure to embed the secret message. In standard digital imaging systems, the sensor portion that converts photonic energy into an analog electrical signal and all the subunits that digitize, process, and display this signal are based on square pixel logic, so there is currently no commercial equipment available to produce HIP-domain images. Thus, the image is first transformed into the HIP domain in software using the infrastructure developed in the project. Then the HIP-domain image is partitioned into non-overlapping heptads of the standard size, each containing seven hexels. Rather than embedding segments to the independent pixel pairs as done in SIP-domain EMD, we do the embedding iteratively in each heptad. Experimental results show that the HexEMD outperforms its SIP equivalent, EMD, by improving embedding capacity and achieving low visual quality distortion. © 2022 The Author

Reversible Logic-Based Hexel Value Differencing—A Spatial Domain Steganography Method for Hexagonal Image Processing

The field of steganography has witnessed considerable advancements in square-pixel-based image processing (SIP). However, the application of steganography in Hexel (Hexagonal pixel)-based Image Processing (HIP) is still underexplored. This study introduces a pioneering spatial steganography method called the Reversible Logic-Based Hexel Value Differencing (RLBHVD) method in the HIP domain. Our approach draws inspiration from Pixel-Value-Differencing (PVD), a SIP fundamental spatial-domain (S-D) steganography method. Initially, the image is transformed into the HIP domain using the custom software infrastructure developed for this project. Due to the absence of commercial equipment capable of producing HIP-domain images, traditional digital imaging systems are employed with their sensor components, analog-to-digital conversion units, and square-pixel-based displays. Once the image is converted, it is partitioned into standardized heptads, each comprising seven hexels. Simultaneously, the secret message is segmented for embedding into the hexels within each heptad. Unlike SIP-domain PVD, which embeds segments into independent pixel pairs, our method performs iterative embedding within each heptad. Additionally, we leverage Feynman gates, a core element of reversible logic, to achieve retrieval of both the cover image and the secret message. Unlike PVD in SIP, our approach enables reversibility in the recovery process. Experimental results demonstrate that our proposed method, RLBHVD, outperforms its SIP counterpart, PVD, by achieving a low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and significant similarity between the stego-image and cover image histograms. These findings highlight the efficacy and superiority of our HIP-based steganography approach in comparison to existing SIP methods