Image encryption schemes can be vulnerable to a variety of cryptanalysis attacks. The use of key-dependent dynamic S-boxes has been shown to improve security. Threats of chosen-plaintext and chosen-ciphertext attacks still linger. In this paper, we present an efficient algorithm for constructing secure dynamic S-boxes derived from Henon map. We use the proposed dynamic S-box to construct an image encryption scheme that includes a novel combination of security features to resist chosen-plaintext and chosen-ciphertext attacks. Namely, a hash verification step at the end of the decryption procedure effectively thwarts chosen-ciphertext attacks. The hash also serves as an image dependent initialization for the keystream, which together with using an image dependent S-box resist known-plaintext attacks. Furthermore, encryption keys are protected against cryptanalysis using elliptic curve cryptography (ECC).Therefore, the recovery of secret keys is as hard as the elliptic curve discrete logarithm problem even in the unlikely case of the recovery of the temporary S-box or keystream. Our evaluation of the proposed image encryption scheme reveals that it achieves a higher security standard than existing techniques. Moreover, the proposed scheme is computationally efficient with encryption throughput approaching 60 MB/s.

Most of the computational time in many chaotic image encryption schemes is spent generating the required chaotic sequences. Since chaotic systems are defined by recurrence relations, they are often generated sequentially. In this paper, we propose a chaotic image encryption scheme which enables pixel-level parallelism to boost the computational speed of generating chaotic sequences. We use a group defined over elliptic curve (EC) points and the addition operator to generate a discrete chaotic sequence and use it to construct an image encryption scheme. The proposed scheme is designed such that encryption and decryption operations are highly parallelizable to take advantage of readily available parallel processing platforms such as GPU acceleration, DSPs and multi-core CPUs. Complexity analysis indicates that the proposed scheme is more efficient than existing EC-based image encryption schemes. Practical experiments on a quad-core CPU show that the proposed scheme can achieve a speedup of 3.93, confirming its superior parallelization efficiency in comparison with existing parallel image encryption schemes. We also provide detailed analysis of the immunity of the proposed scheme to all common cryptanalysis attacks. Results reveal that the proposed technique shows promising performance in terms of security and efficiency.

The shortage of water stands as a global challenge, prompting considerable focus on the management of water consumption and irrigation. The suggestion is to introduce a smart irrigation system based on wireless sensor networks (WSNs) aimed at minimizing water consumption while maintaining the quality of agricultural crops. In WSNs deployed in smart irrigation, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a new localization method for localizing unknown sensor nodes in WSN-based smart irrigation systems using estimated range measurements. The proposed method can accurately determine the positions of unknown nodes, even when they are located at a distance from anchors. It utilizes the Levenberg–Marquardt (LM) optimization algorithm to solve a nonlinear least-squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the proposed method, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes. Specifically, it achieves 19% and 58% improvement in estimation accuracy when compared to distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. Additionally, the method exhibits robustness against measurement noise and scalability for large-scale networks. Ultimately, integrating the proposed localization method into the smart irrigation system has the potential to achieve approximately 28% reduction in water consumption.

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

The use of 12-bit levels of grayscale in medical image representation allows for more precise image analysis and diagnosis, and encrypting such images is essential for protecting patient privacy and security. Existing 8-bit image encryption schemes become inefficient when applied to 12-bit images. In this paper, we design a cryptographically strong encryption scheme to improve the efficiency of encrypting 12-bit medical images. The core of the proposed scheme is a key-dependent 12×12 S-box, which plays a crucial role in enhancing the security and efficiency of the encryption scheme. Most notably, test results show that 12×12 S-boxes offer significantly stronger confusion and key-sensitivity than their 8×8 counterparts. This finding enables the proposed 12×12 S-box-based scheme to process 12-bit images 3.3 times faster than an 8×8 S-box while passing all security tests. Experimental results show that the proposed scheme can encrypt 12-bit images at speeds up to 300MB/s. Moreover, the proposed scheme has also been shown to efficiently handle 8-bit images. Comparative results highlight the security and efficiency advantages of the proposed scheme over existing medical image encryption schemes.

The performance of the UAVs while executing various mission profiles greatly depends on the selection of planning algorithms. Reinforcement learning (RL) algorithms can effectively be utilized for robot path planning. Due to random action selection in case of action ties, the traditional Q-learning algorithm and its other variants face the issues of slow convergence and suboptimal path planning in high-dimensional navigational environments. To solve these problems, we propose an improved deep Q-network (DQN), incorporating an efficient tie-breaking mechanism, prioritized experience replay (PER), and L2-regularization. The adopted tie-breaking mechanism improves the action selection and ultimately helps in generating an optimal trajectory for the UAV in a 3D cluttered environment. To improve the convergence speed of the traditional Q-algorithm, prioritized experience replay is used, which learns from experiences with high temporal difference (TD) error and avoids uniform sampling of stored transitions during training. This also allows the prioritization of high-reward experiences (e.g., reaching a goal), which helps the agent to rediscover these valuable states and improve learning. Moreover, L2-regularization is adopted that encourages smaller weights for more stable and smoother Q-values to reduce the erratic action selections and promote smoother UAV flight paths. Finally, the performance of the proposed method is presented and thoroughly compared against the traditional DQN, demonstrating its superior effectiveness.