Modern quantum computing technologies endanger the security mechanisms of RSA and ECC used to protect IoT-based healthcare systems. This research introduces a privacy-protecting healthcare data infrastructure that uses post-quantum lattice cryptography along with the Paillier scheme in order to secure IoT exchange and protected data processing. Vital signs of IoT devices reach a central server through a secure system which also allows data encryption and processing outside the server. Our simulation models a medical institution containing 200 IoT devices to test the framework’s operation regarding encryption delays together with aggregated data precision and quantum resistance capabilities. Experiment results demonstrate that the framework raises quantum breach resistance from R ≈ 0.985 to R′≈ 0.3. Future work will focus on real-time optimization to improve the system performance despite the existing performance bottlenecks from Paillier’s encryption.

The growing use of smart technologies in homes has changed the way we manage power consumption. In this paper, CHEFS an innovative framework is proposed for smart home power consumption analysis, using Explainable AI (XAI) to improve clarity and accuracy with prediction and continuous decision-making by edge computing. Devices like the Internet of Things (IoT) and smart meters are used in smart homes to observe energy usage precisely. Such devices are also used for cutting energy costs and reducing environmental impacts. They often lack clarity in decision-making which makes it difficult, whereas the traditional machine learning models offer accurate predictions. To show this, we use XAI methods like SHAP and LIME to get a better understanding of the power usage patterns and forecasts. Edge computing reduces delays and bandwidth usage while also enhancing response time. This approach enhances clarity and dependability in Artificial Intelligence (AI) models providing reliable insights into household power consumption. A comprehensive evaluation helps create a more transparent and effective system for creating smarter energy solutions.

With the advent of technology, the transfer of sensitive information has become more prone to misuse, especially through unsecured platforms like social media. To address this challenge, UAV communication, particularly drones, has emerged as an alternative for document delivery in highly sensitive areas. But these devices can also get vulnerable to attacks which can cause a serious issue when the information is sensitive. Therefore, we propose a UAV-based secure document delivery framework that leverages AI models to detect potential attacks on the UAVs. The system ensures security throughout the document transfer process by evaluating various drone parameters. We employ the Decision Tree Classifier, which uses entropy to classify the potential threats. The result of the classification is used to guide the decision-making process for a secure delivery process. We further compare the various models based on metrics like accuracy, precision, recall, and$\mathbf{F - 1}$score, demonstrating the effectiveness of our framework, which enhances the security of UAV-based communication systems.

Integration of the Internet of Things (IoT), intelligent sensor networks, and patient-centric modules has successfully revitalized the way we pursue healthcare services. Seamless online doctor-patient communication facilities have vanished the partial line between traditional physical on-site treatment and current remote monitoring. In the Healthcare 4.0 environment, patients can connect with doctors via video conference, send audio transcripts as responses, and share pictorial or text-based vital information. One of the major concerns related to remote healthcare treatment applications is the efficient utilization of networks for data transmission. To mitigate this paramount challenge, we propose SDN care. It is a Deep Learning(DL)-based SDN-enabled network classification approach to facilitate seamless and secured communication between doctors and patients in the Healthcare 4.0 ecosystem. SDN care relies on one-dimensional Convolutional Neural Network (CNN) architecture to efficiently classify the type of data under communication and make adjustments in SDN parameters. The latency, bandwidth, jitter, etc., are adjusted based on prediction from the CNN model for effective utilization. Proposed SDN-care is further compared with Artificial Neural Network (ANN), and it also has been examined using different types of optimizers. The performance evaluation of SDN-care has been done through various metrics such as accuracy, loss convergence, precision, recall, f1 score, Receiver Operating Characteristic (ROC) curve, precision-recall trade-off curve and compared with different optimizers such as Adam, SGD, RMSprop, and Adadelta. Thus, SDN care introduces significant advancements in SDN-enabled remote patient-doctor communication environments using various modes of data exchange.

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

Security and reliability in data transmission are considered as the challenging concerns in VANET. Software-defined networking (SDN) and network coding (NC) are the two key apprehensions in networking that have caught much attention from both industry and academia in recent years. Decoupling of control plane and data plane in SDN enables the centralized control of the network, providing flexibility to a great extent. On the other hand, NC has shown immense potential for improving robustness and security when deployed on VANET. This article advocates for the design of an architecture which exercises the SDN concept by incorporating NC with multigeneration-mixing (MGM) functionalities to increase reliability as well as security of data transmission in vehicular networks. We design an MGM-based NC protocol for encoding and decoding of data. Moreover, a centralized SDN controller takes charge of authentication of vehicles. Finally, we build a simulation model based on realistic traffic and communication characteristics. The simulation results show that the proposed protocol leveraged by the SDN framework outperforms the conventional NC-based protocol in case of security and reliability. Moreover, the simulation model is exhibited and point-by-point execution assessment is given to demonstrate the scalability and prevalence of the proposed strategy.