Abstract As the demand for secure image transmission continues to rise, encryption techniques based on chaotic systems have emerged as a vital component in the field of information security. To meet this demand and enhance transmission security, we propose a novel encryption scheme that significantly improves the security of images during transmission. In this work, a novel Sinusoidal-Quadratic Map Coupled Map Lattices (SQMCML) model is proposed, incorporating a non-adjacent coupling mechanism guided by cellular automata (CA). Besides, we presented the Sinusoidal-Quadratic Map (SQM) to enhance the system's dynamics behavior. The relevant comprehensive dynamics analysis indicates that all lattices of SQMCML are in a chaotic state. Based on the SQMCML system, a new plaintext-related image encryption scheme is proposed, featuring a random-trajectory Josephus permutation method. Firstly, in order to improve the scheme's capability to withstand differential attacks, a plaintext-related method for generating the chaotic initial values of SQMCML is proposed, which significantly enhances the plaintext sensitivity of the image cryptosystem. Subsequently, we presented the random-trajectory Josephus permutation method, in which chaotic sequences are utilized to dynamically determine the starting points and step sizes of each traversal round, enhancing both the randomness and security of the permutation process. Additionally, the diffusion operation is carried out by leveraging the iterative dynamics of 2D-CA model. The results of comprehensive simulations confirm the high security strength of the proposed approach, demonstrating its applicability to the secure transmission of image data. Comprehensive simulations validate the high security of the proposed scheme: chi-square test values significantly exceed the 0.01 threshold, NPCR and UACI meet theoretical expectations, and global Shannon entropy exceeds 7.999 while local Shannon entropy conforms to standard benchmarks—confirming its suitability for secure image transmission.

To improve the security and efficiency of multi-image encryption, this paper proposes a hybrid encryption method that combines Interferenceless Coded Aperture Correlation Holography (I-COACH) with chaotic modulation and compressed sensing techniques. The method constructs a dual-layer encryption framework, integrating optical and digital processing to overcome the limitations of single-domain schemes.<br>In the optical layer, I-COACH is employed to encode multiple input images by recording their point spread holograms without interference, providing initial encryption and resistance against physical attacks. The resulting hologram is then processed using block-wise Discrete Cosine Transform (DCT) to achieve sparsity. Dual chaotic sequences perturb DCT coefficients to enhance key sensitivity and randomness. Finally, compressed sensing is applied to achieve secondary encryption while reducing the data volume by 30%, enabling efficient and secure storage or transmission. Experimental results demonstrate that the proposed method achieves an average Number of Pixels Change Rate (NPCR) of 99.44% and a Unified Average Changing Intensity (UACI) of 33.04% against differential attacks, with a ciphertext entropy of 7.9996 bit. Moreover, it exhibits excellent encryption performance in terms of key sensitivity, robustness, and resistance to statistical analysis. This method provides a practical solution for secure image application scenarios such as medical imaging and surveillance.

OFDM secure transmission scheme based on double chaotic encryption

A secure transmission protocol using C-ACO routing selection in MANET

Secure transmission enhancement in shotgun cellular systems using transmitter-side information over correlated shadowing channels

Measurement-Device-Independent Quantum Key Distribution (MDIQKD) protocols can effectively resist all possible attacks targeting the measurement devices in a Quantum Key Distribution (QKD) system, thus exhibiting high security. However, due to the protocol's high sensitivity to channel attenuation, its key generation rate and transmission distance are significantly limited in practical applications.<br>To improve the performance of MDI-QKD, researchers have proposed quantum memory (QM) assisted MDI-QKD protocols, which have enhanced the protocol's performance to a certain extent. Nevertheless, under finite-size conditions where the total number of transmitted pulses is limited, accurately estimating the relevant statistical parameters remains a challenge. As a result, existing QM-assisted MDI-QKD schemes still suffer from issues such as low key rates and limited secure transmission distances.<br>To address these problems, this paper proposes a novel improved finite-size QM-assisted MDI-QKD protocol. By utilizing quantum memories to temporarily store early-arriving pulses and release them synchronously, the protocol effectively reduces the impact caused by channel asymmetry. Additionally, the protocol introduces a four-intensity decoy-state method to improve the estimation accuracy of single-photon components. Meanwhile, to mitigate the impact of finite-length effects on QM schemes, the proposed protocol incorporates a collective constraint model and a double-scanning algorithm to jointly estimate scanning error counts and vacuum-related counts. This approach enhances the estimation accuracy of the single-photon detection rate and phase error rate under finite-size conditions, thereby significantly improving the secure key rate of the MDI-QKD system.<br>Simulation results demonstrate that under the same experimental conditions, compared with the existing QM-assisted three-intensity decoystate MDI-QKD protocol and the four-intensity decoy-state MDI-QKD protocol based on Heralded Single-photon Source, (HSPS), the proposed protocol extends the secure transmission distance by more than 30 kilometers and 100 kilometers, respectively. This proves that under the same parameter settings, the proposed scheme exhibits significant advantages in both key rate and secure transmission distance. Therefore, this research provides important theoretical references and valuable benchmarks for the development of long-distance, high-security quantum communication networks.

Abstract—In the present digital era, ensuring secure and confidential communication over open networks has become increasingly important due to rapid growth of cyber threats and data interception techniques.Traditional encryption methods protect the content of information but do not conceal the existence of the communication, which can attract unwanted attention. This work presents the development of a secure data communication system utilizing steganography for embedding sensitive information into multimedia files, including images and text. The preposed methodology incorporates least significant bit (LSB) technique. Experimental results demonstrate that the system effectively embeds and retrieves hidden data with high accuracy, maintain imperceptibility and robustness under controlled conditions. The user interface was evaluated for usability, ensuring operational stability and effective feedback mechanisms.The study highlights that while the system achieves reliable concealment and secure transmission of data, challenges such as limited embedding capacity, vulnerability to steganalysis, and susceptibility to media manipulations remain. Keywords—Steganography, Secure data Communication, Least Significant Bit, Multimedia data hiding,InformationSecurity, Steganalysis, Data Confidentiality

With the rapid advancement of Internet of Things (IoT) and Wireless Sensor Networks (WSNs), healthcare systems have evolved to support continuous patient monitoring, real-time data acquisition, and cloud-based decision support. The secure transmission of sensitive medical data and the reliability of healthcare decision-making remain major challenges. Traditional routing techniques fail to provide robust trust management, making the system vulnerable to malicious nodes and unreliable data paths. The lack of lightweight, end-to-end encryption increases the risk of data breaches during transmission. Compounding the issue is the limited diagnostic accuracy of conventional analytics platforms, which struggle to effectively process complex, high-dimensional healthcare data. To address this, this study introduces a Dual Secure optimal Trusted routing (DST-Route) technique designed to ensure secure, trust-aware data transfer and enhance patient diagnostic decision-making in IoT-WSN. In the data transfer phase, the Enhanced Pomarine Jaeger Optimization (EPJO) algorithm is used to perform trust-based clustering and optimal cluster head selection, ensuring that only reliable nodes participate in data transmission. The sensitive health data collected from patients is protected using SmartNetcryption, a lightweight encryption used to secure information before cloud storage. In the analytics phase, the framework uses pre-trained deep learning models, including ResNet, DenseNet, EfficientNet, and UNet for feature extraction, while a Modular Deep Transfer Learning (MDTL) enables accurate healthcare state prediction and early diagnosis. Experimental results demonstrate that DST-Route significantly improves trust accuracy, energy efficiency, and prediction performance when compared to conventional routing techniques. The proposed UNet, combined with the MDTL model, achieved a healthcare state prediction accuracy of 98% with a loss rate of 0.05, showing 12.54% improvement over state-of-the-art models. This performance underscores the effectiveness of the DST-Route technique in ensuring secure and reliable sensitive data transfer for accurate patient state prediction.

Background. At the present stage, electronic communications must provide: ultra-fast data exchange between production systems, which increases productivity and safety; the use of robotic systems and artificial intelligence to support people in production processes. Reliable electronic communications ensure the security of industrial networks and personal data; intelligent factories with network sensors and automated data processing systems. In this context, new challenges are facing electronic communications and, accordingly, new problems are identified, the solution of which requires conducting promising scientific research. Objective. The purpose of this work is to generalise and highlight the key areas of development of electronic communications, which determine the current and future state of the industry. It is aimed at outlining scientific challenges and prospects associated with the digitalisation of society, the growth of demand for fast, reliable and secure data transmission. Methods. Analysis of factors and technologies that influence the growth of needs for the further development of electronic communications, the quality of telecommunications services in fifth and next generation networks, the introduction of new types of electronic communications and their integration with advanced information technologies. Results. The presented research directions determine the future of electronic communications. Research in these areas will ensure the sustainable development of the industry, taking into account the growing needs of modern society in fast, secure and reliable data transmission technologies. Conclusions. The main goal of the 6G direction and future generations of networks is to create ultra-fast, ultra-reliable and intelligent networks capable of supporting new types of services and applications, from autonomous control and remote medicine to full-scale multimedia communications. The direction of quantum communications is a revolutionary direction that promises to ensure absolute security of information transmission and open up new opportunities for communication. The direction of IoT scaling is a key direction that determines the future of many communication technologies. The research area that includes artificial intelligence (AI) and machine learning (ML) calls for further integration of AI and ML into all aspects of electronic communications, leading to smarter, more efficient and secure networks. Security and privacy in electronic communications is a dynamic area that is constantly evolving due to the growth of cyber threats and technological advancements. Edge and Fog Computing are key technologies for the future of electronic communications, and their development requires an interdisciplinary approach that combines computer science, telecommunications, AI and IoT. Research in the field of NFV and SDN is necessary to improve the performance, automation and security of networks. They play a key role in the future of 5G/6G, cloud computing, IoT and industrial networks. Green communications is an important research area that aims to improve the energy efficiency and sustainability of telecommunications networks. Networks for critical applications are a key element of modern infrastructures, and their development requires an interdisciplinary approach that combines telecommunications, cybersecurity, artificial intelligence and other fields. Holographic and tactile communication interaction technologies will become the basis of future digital communications, changing the way people and machines interact.

This work resolves the crucial problem of secure data transmission in IoT-aided healthcare devices, where confidential patient data is vulnerable to breaches and cyberattacks. To resolve these complexities, this work proposes a novel secure data transmission system that combines blockchain technology, Multiobjective Weighted Restricted Boltzmann Machine (MW-RBM) for feature extraction, Magnified Feeding-based American Zebra Optimization (MFAZO) for weight optimization, and a Multiscale Stacked Residual-Gated Recurrent Unit (MSRes-GRU) for attack detection. The novelty of this work lies in combining residual GRU and blockchain for secure IoT healthcare data transmission, guaranteeing both transparency and attack detection. The improvement is displayed in weight optimization through MFAZO, which refines the feature extraction task and boosts the accuracy of the technique in attack detection. The designed approach involves gathering attack detection data, performing feature extraction utilizing MW-RBM with optimized weights and identifying IoT node attacks via the MSRes-GRU technique’s multiscale layers and the residual connections. The Homomorphic Polynomial Encryption (HPE) is further employed to secure the healthcare data during transmission. Lastly, the performance of the model is determined with conventional models. The accuracy of the designed MSRes-GRU is 96.22%, which is higher than the existing models such as DNN (85.65%), LSTM (80.71%), SVM (89.99%), and GRU (94.14%). The key results demonstrate the technique’s high detection accuracy and robust performance in recognizing the IoT-based attacks while guaranteeing effective, secure and transparent data transmission via blockchain. This research contributes to improving the secure and scalable IoT-enabled healthcare devices, providing a reliable model for trustworthy healthcare applications that preserve data integrity and privacy.

Abstract: Quantum-Based Wireless Sensor Networks (QWSNs) represent a promising evolution of traditional wireless sensor networks by integrating quantum communication principles to enhance data transmission security and reliability. This paper presents a systematic review of existing research on energy efficiency, secure communication, and reliable routing protocols in QWSNs. The study examines key advancements in quantum-based routing algorithms, energy optimization techniques, and cryptographic mechanisms that utilize quantum key distribution (QKD) for enhanced network security. Additionally, the review identifies major challenges, including scalability, resource constraints, and implementation complexity in real-world scenarios. By analyzing existing methodologies and highlighting research gaps, this work provides insights into potential directions for developing energy-efficient and secure routing frameworks in next-generation QWSNs. The findings emphasize the need for adaptive, lightweight, and quantum-compatible routing schemes to ensure sustainable and secure communication in future sensor network architectures. Keywords: Quantum-Based Wireless Sensor Networks (QWSNs); Energy Efficiency; Secure Routing; Quantum Key Distribution (QKD); Reliability; Cryptography; Network Optimization; Quantum Communication

Simultaneous ascending auctions find extensive applications in spectrum licensing and advertising space allocation. However, existing quantum sealed-bid auction protocols suffer from dual limitations: they cannot support multi-item simultaneous bidding scenarios, and their reliance on complex quantum resources along with requiring full quantum operational capabilities from bidders fails to accommodate practical constraints of quantum resource-limited users. To address these challenges, this paper proposes a multi-party semi-quantum simultaneous ascending auction protocol based on single-particle states. The protocol employs a trusted honest third party (HTP) responsible for quantum state generation, distribution, and security verification. Bidders determine their groups through quantum measurements and privately encode their bid vectors. Upon successful HTP authentication, each bidder obtains a unique identity code. During the bidding phase, HTP dynamically updates quantum sequences, allowing bidders to submit bids for multiple items by performing only simple unitary operations. HTP announces the highest bid for each item in real time and iteratively generates auction sequences until no new highest bid emerges, thereby achieving simultaneous ascending auctions for multiple items. It acts as a quantum-secured signaling layer, ensuring unconditional security for bid transmission and identity verification while maintaining classical auction logic. Quantum circuit simulations validate the protocol’s feasibility with current technology while satisfying critical security requirements, including anonymity, verifiability, non-repudiation, and privacy preservation. It provides a scalable semi-quantum auction solution for resource-constrained scenarios.

ABSTRACT The research aims to explore a secure information encryption control algorithm for Internet of Things identity device authentication, to improve the security and efficiency of data transmission, and reduce the consumption of computing resources and energy by Internet of Things devices in the encryption and decryption process. Chaotic cryptography is utilized to generate keys that are applied to encrypt data between Internet of Things devices and cloud computing platforms. Finally, in a distributed Internet of Things environment, efficient authentication of device identity is achieved through Schnorr aggregated signatures. The experiment findings denote that the proposed algorithm performs well in convergence speed, with the fastest convergence speed, reaching a low loss function value of 10 −5 after 30 iterations. In terms of throughput, even with 15 nodes, the throughput is still close to 600 TPS, far higher than other comparative algorithms. The algorithm proposed by the research significantly improves the performance of Internet of Things devices in secure information encryption control, ensuring data security while improving system efficiency, making the algorithm widely applicable in resource‐limited Internet of Things environments.

ABSTRACT In today's digital era, images play a vital role across diverse fields, including healthcare, banking, defence, traffic monitoring, and weather forecasting. As digital footprints, their use is rapidly growing, but they are also increasingly vulnerable to unauthorised access and misuse. To address this challenge, we propose a novel encryption scheme for multiple red, green, blue (RGB) images. The scheme takes an arbitrary number of images, overlays them to form a three‐dimensional (3D) image, and then divides it into four subparts. A five‐dimensional multi‐wing hyperchaotic map is employed for random selection of parts, images, rows, columns, and key images. Rows and columns from selected images are repeatedly swapped to produce scrambled 3D images, which are further XORed to generate the final encrypted outputs. The encrypted images are then combined into a large 3D RGB cipher image. To enhance security, the scheme integrates a 256‐bit salt key with SHA‐256 hash codes, ensuring strong key space and plaintext sensitivity. Experimental results demonstrate that the proposed approach provides robustness against multiple threats, real‐world applicability, and high security. Notably, the scheme achieved a highly competitive information entropy value of 7.99994, confirming its effectiveness. Extensive experiments further show that the ciphertexts exhibit high randomness and robustness: average correlation coefficients (CCs) between adjacent pixels are close to zero, the number of pixel change rate is 99.63%, and the unified average changing intensity is 33.45%. The decrypted images achieve peak signal‐to‐noise ratio (PSNR) values of ∞ (O–D) and 7.9982 (O–C), confirming lossless reconstruction. Moreover, the scheme demonstrates strong resistance to chosen plaintext as well as noise and cropping attacks, while maintaining competitive computational efficiency. Comparative analysis with recent chaos‐based algorithms verifies that the proposed approach provides superior security, randomness, and robustness for secure image transmission.

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems.

Underwater Optical Wireless Communication systems face severe signal attenuation, scattering, and turbulence, which significantly degrade image transmission quality and limit the communication range. To address these challenges, this paper proposes a secure and high-capacity RGB image transmission framework based on Optical Code Division Multiple Access (OCDMA) using Identity Row Shift Matrix (IRSM) codes. The IRSM-OCDMA scheme enhances data confidentiality by assigning unique orthogonal codes to each user while supporting simultaneous multiuser transmission with an aggregate rate of up to 30 Gbps. System performance is analyzed across five water types: Pure Seawater (PS), Clear Ocean, Coastal Ocean, Harbour I, and Harbour II (HR II), covering a broad range of attenuation coefficients. Image quality is quantitatively evaluated using standard metrics including Root Mean Square Error, Signal-to-Noise Ratio, Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Correlation Coefficient. Two distinct post-processing methods are applied: median filtering for impulsive noise reduction and a Particle Swarm Optimization-based correction algorithm that adaptively restores image features under underwater channel conditions. Simulation results show a maximum transmission distance of 27 m in PS and 4 m in turbid HR II water, demonstrating the effectiveness of the proposed framework. The combination of IRSM coding with adaptive post-processing offers a robust solution for secure, high-quality image transmission in Internet of Underwater Things applications.

With the rapid development of unmanned surface vessels (USVs), a vector thruster was designed in this paper to meet their evolving operational demands. The anti-impact capability of the vector thruster, in which the universal joint plays a critical role in attenuating impact loads, directly governs the stability and security of power transmission in USVs. A mechanical model of the vector thruster with a universal joint was established, incorporating length and stiffness ratio coefficients to characterize its key dynamics. Based on this model, numerical simulation using the Newmark method was conducted to systematically evaluate the thruster’s mechanical characteristics, particularly the dynamic variation of the inclination angle, under various working conditions and impact loads. The results indicate that an increase in stiffness ratio amplifies the angular displacement amplitude of the driven shaft but shortens the vibration stabilization time. During the operation of the vector thruster, an increase in the inclination angle leads to greater vibration amplitude. Furthermore, systems with a higher, longer ratio exhibit a more pronounced tendency for amplitude growth as the inclination angle increases. Finally, the theoretical model was validated through a test bench, and the variation pattern of dynamic thrust under impact load was revealed. These results emphasize that the stiffness and dimensional parameters must be carefully considered in the design and control optimization of vector thrusters to ensure reliable performance under demanding operational conditions.

In the communication era, secure transmission of digital data through networks of communication channels and their storage is carried out by encrypting the data. It transpires that the encryption methods heavily depend on The Theory of Numbers - a fancied topic of Higher Algebra. The discreteness inherent in this algebra employs special constructs, setting it apart from other topics of the subject. Its logical development requires careful understanding of the theory. On the other hand, Cryptography as a subject freely employs the concepts and methods of Number Theory, and a number of books have appeared on the subject. The reading of these texts however is not smooth for readers not conversant with the certain specialities of Number Theory. This survey in simple terms, is a compendium of these specialities that may ease the study of Cryptography.

Fog computing, a new computing paradigm that has gained popularity, brings calculations closer to data sources from healthcare facilities. The health care industry is the driving force behind the growth of Internet of Things (IoT) driven Fog computing that improves network performance and efficiency, particularly when it comes to the safe and effective aggregation and transmission of healthcare data. This requires optimizing resource allocation and addressing overflow issues. This study introduces a novel approach that combines Task Group Aggregation (TGA) with a Recurrent Neural Network (RNN) to assess Quality of Service (QoS) characteristics and detect overloaded servers. The TGA method is utilized to effectively manage data movement to Virtual Machines (VMs), thereby alleviating congestion and improving system stability. Furthermore, it uses Chaotic Fruit Fly Optimization Algorithm (CFOA), a neural computing system, to optimize service and user separation based on individual qualities in the context of secure healthcare data aggregation and transmission within IoT networks. The integration of TGA with CFF enhances the detection of overflow problems within the RNN framework, enabling proactive management of resource allocation. The proposed work is evaluated by using the Java programming language and the results demonstrate the effectiveness of the Fog computing overflow control model in mitigating congestion and optimizing resource scheduling, thereby facilitating the efficient and secure aggregation and transmission of healthcare data within IoT networks.

The wavelength-dependent positive and negative responses make bipolar photodiodes essential for applications in sensing, secure communication, and imaging systems. However, existing bipolar photodiodes typically rely on tandem light-absorbing layers or the synergy of multiple physical effects, leading to complex architectures and limited adaptability. Here, we present an isotype transport layer-structured bipolar single-layer perovskite photodiode (n-i-n type) with an adjustable bipolar response range through an electric field flipping mechanism. The device features two electric fields in opposite directions. Through the in situ modulation engineering of the energy band of nanomaterials, the electric field at the perovskite/SnS2 (electron transport layer) interface is precisely modulated, resulting in flipping of the overall device electric field, driving carriers generated at different depths toward opposite electrodes, and producing a wavelength-dependent bipolar response. In particular, tuning the internal electric field expands the positive response range from 300-480 nm to 300-700 nm. A secure optical communication system constructed by this bipolar photodiode can transmit positive and negative signals, enabling the secure transmission of Fourier-transformed color images. The optical communication system demonstrated excellent security. When intercepted by unipolar photodiodes, the information leakage rate is only 10.85%, and the difference rate of transmitted images reaches a perfect 100%. This work utilized an interfacial potential engineering strategy to fabricate tunable bipolar perovskite photodiodes, offering a promising route toward compact, high-speed, and intrinsically secured optoelectronic communication systems.