A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.

The vast interconnection of resource-constrained devices and the immense amount of data exchange in the Internet of Things (IoT) environment resulted in the resurgence of various security threats. This resource-constrained environment of IoT makes data security a very challenging task. Recent trends in integrating lightweight cryptographic algorithms have significantly improved data security in the IoT without affecting performance. The PRESENT block cipher, a standard and lightweight benchmark algorithm, is a widely accepted and implemented algorithm with a simple design, low-cost implementation, and optimum performance. However, this simple design utilizing lightweight linear and non-linear functions led to slow confusion and diffusion properties. The static bits in the permutation layer are the leading cause of slow diffusion, showcasing dependencies between plaintext and ciphertext bits. This research addresses and seeks to overcome this shortcoming of slow confusion and diffusion using the Deoxyribonucleic Acid (DNA) replication process and shift-aided operations, leading to the DNA-PRESENT block cipher. Security, cost, and performance analyses were performed to verify the improvements. The results demonstrated that with only 33.5% additional cost, DNA-PRESENT increased key sensitivity to 73.57%, plaintext sensitivity to 33%, and consistently ensured an average bit error rate (BER) of 50.2%. An evident increase of 176.47 kb/s in throughput and reduced latency to 17 cycles/block kept the good hardware efficiency of 43.41 kbps/KGE, and the reduction in execution time by 0.2333 s led to better performance. Considering the security advances achieved, this cost increase is a trade-off between security and performance.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network’s security and integrity. This paper proposes a novel method—a Composite Ensemble Learning (CEL)-based neural network—for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

Modulation recognition plays a crucial role in noncooperative communication in which receivers have no prior information regarding transmitter modulation scheme. This paper presents a novel convolutional neural network with single Stem and Inception module for autonomous modulation classification from raw I/Q received channels. This addresses the computationally intensive problem of conversion of I/Q channels to constellation image processing. The proposed system is capable of classifying 11 standard modulation schemes with both 2D and 3D input array configurations for varying SNR conditions. The performance of proposed system has been evaluated for a realtime communication system simulated with Rician fading channel and AWGN noise model, providing realistic distortion effects. The proposed CNN design achieves an average accuracy of 90% at 10 dB and 99% at 20 dB SNR with reduced network learnables and lower training and testing time, which makes it computationally efficient. These attributes make Inception module based CNN a viable solution for modulation classification in practical low cost and portable yet reliable communication systems.

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness.