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Modern networks may not even be equipped to fully satisfy rapidly increasing traffic needs due to the fast growth of intelligent endpoints and systems and various programs with diverse needs. As a result, a study on 6G technology has already been undertaken by businesses and academics. AI (artificial intelligence) has subsequently been used as a paradigm shift to optimise high-intelligence 6G technology. To realize information exploration intelligent resource planning, automatic network adaptation, and astute service configuration management, the authors proposed an AI-enabled smart architectural design for 6th generations ystems; here, it is categorized into four tiers: data mining, intelligent sensing layer, smart application layer, and intelligent control layer. The author also discussed modern technology in the mobile generation, uses, security, opportunities, and challenges.

WSN is comprised of sensor nodes that sense the data for various applications. The nodes are employed for transmitting sensed data to BS through intermediate nodes or the cluster heads in multi-hop environment. Erroneous selection of CHs may lead to large energy consumption and thereby degrades system performance. Hence, an effective technique was developed by proposing Rider-ASO for secure-aware multipath routing in the WSN. The proposed routing protocol offers security to the network concerning various trust factors. Initially, cluster head selection is done using RCSO. Then, the trust values of the cluster heads that are selected is computed to ensure security while routing. For the multipath routing, proposed Rider-ASO is developed by combining ASO and ROA. Thus, the proposed algorithm finds multiple secured paths from the source into destination based on selected CHs. The developed Rider-ASO outperformed other methods with minimal delay of 0.009 sec, maximal average residual energy 0.5494 J, maximal PDR of 97.82%, maximal throughput rate of 96.07%, respectively.

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.