The healthcare sector involves many steps to ensure efficient care for patients, such as appointment scheduling, consultation plans, online follow-up, and more. However, existing healthcare mechanisms are unable to facilitate a large number of patients, as these systems are centralized and hence vulnerable to various issues, including single points of failure, performance bottlenecks, and substantial monetary costs. Furthermore, these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access. To address these issues, this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials. Furthermore, also utilize the capabilities of the InterPlanetary File System (IPFS) to store the Electronic Health Record (EHR) in a distributed way. This IPFS platform addresses not only the issue of high data storage costs on blockchain but also the issue of a single point of failure in the traditional centralized data storage model. The simulation results demonstrate that our model outperforms the benchmark schemes and provides an efficient mechanism for managing healthcare sector operations. The results show that it takes approximately 3.5 s for the smart contract to authenticate the node and provide it with the decryption key, which is ultimately used to access the data. The simulation results show that our proposed model outperforms existing solutions in terms of execution time and scalability. The execution time of our model smart contract is around 9000 transactions in just 6.5 s, while benchmark schemes require approximately 7 s for the same number of transactions.

Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures secure, tamper-proof access to health data. Using the PhysioNet dataset, the proposed model achieves an accuracy of 0.95, F1 score of 0.93, precision of 0.94, and recall of 0.96, outperforming baseline models like random forest (0.88), adaptive boosting (0.90), logistic regression (0.86), perceptron (0.83), and deep neural networks (0.92).

The controller is a main component in the Software-Defined Networking (SDN) framework, which plays a significant role in enabling programmability and orchestration for 5G and next-generation networks. In SDN, frequent communication occurs between network switches and the controller, which manages and directs traffic flows. If the controller is not strategically placed within the network, this communication can experience increased delays, negatively affecting network performance. Specifically, an improperly placed controller can lead to higher end-to-end (E2E) delay, as switches must traverse more hops or encounter greater propagation delays when communicating with the controller. This paper introduces a novel approach using Deep Q-Learning (DQL) to dynamically place controllers in Software-Defined Internet of Things (SD-IoT) environments, with the goal of minimizing E2E delay between switches and controllers. E2E delay, a crucial metric for network performance, is influenced by two key factors: hop count, which measures the number of network nodes data must traverse, and propagation delay, which accounts for the physical distance between nodes. Our approach models the controller placement problem as a Markov Decision Process (MDP). In this model, the network configuration at any given time is represented as a “state,” while “actions” correspond to potential decisions regarding the placement of controllers or the reassignment of switches to controllers. Using a Deep Q-Network (DQN) to approximate the Q-function, the system learns the optimal controller placement by maximizing the cumulative reward, which is defined as the negative of the E2E delay. Essentially, the lower the delay, the higher the reward the system receives, enabling it to continuously improve its controller placement strategy. The experimental results show that our DQL-based method significantly reduces E2E delay when compared to traditional benchmark placement strategies. By dynamically learning from the network’s real-time conditions, the proposed method ensures that controller placement remains efficient and responsive, reducing communication delays and enhancing overall network performance.

Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms. Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content. Image watermarking can also be used to verify the authenticity of digital media, such as images or videos, by ascertaining the watermark information. In this paper, a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform (DWT), chaotic map, and Laplacian operator. The DWT can be used to decompose the image into its frequency components, chaos is used to provide extra security defense by encrypting the watermark signal, and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image. These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands. The mid-sub-band maintains the invisible property of the watermark, and chaos combined with the second-order derivative Laplacian is vulnerable to attacks. Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks, i.e., compression, noise addition, and filtering. Moreover, this approach also maintains image quality through peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM). The highest achieved PSNR and SSIM values are 55.4 dB and 1. In the same way, normalized correlation (NC) values are almost 10%–20% higher than comparative research. These results support assistance in copyright protection in multimedia content.