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An exponential growth in multimedia applications has led to fast adoption of digital watermarking phenomena to protect the copyright information and authentication of digital contents. A novel spatial domain symmetric color image robust watermarking scheme based on chaos is presented in this research. The watermark is generated using chaotic logistic map and optimized to improve inherent properties and to achieve robustness. The embedding is performed at 3 LSBs (Least Significant Bits) of all the three color components of the host image. The sensitivity of the chaotic watermark along with redundant embedding approach makes the entire watermarking scheme highly robust, secure and imperceptible. In this paper, various image quality analysis metrics such as homogeneity, contrast, entropy, PSNR (Peak Signal to Noise Ratio), UIQI (Universal Image Quality Index) and SSIM (Structural Similarity Index Measures) are measures to analyze proposed scheme. The proposed technique shows superior results against UIQI. Further, the watermark image with proposed scheme is tested against various image-processing attacks. The robustness of watermarked image against attacks such as cropping, filtering, adding random noises and JPEG compression, rotation, blurring, darken etc. is analyzed. The Proposed scheme shows strong results that are justified in this paper. The proposed scheme is symmetric; therefore, reversible process at extraction entails successful extraction of embedded watermark.

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes.

Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate.