Medical Internet of Things (MIoT) is a rapidly growing field that promises to revolutionize health care. The ability to connect devices and collect data from them has the potential to transform the way it monitors and treat patients. However, the security and privacy of this data is a major concern. The cryptography and other conventional methods for security in resource constrained MIoT has several shortcomings. First, it does not provide adequate protection for patient data. Second, it is not designed to work with the large number of devices and huge volumes of data that are typically generated in MIoT applications. In this paper, Secure M-Trust Privacy protocol (SMP) has been designed to address these issues. The SMP protocol uses a combination of trust, cryptographic and Machine Learning techniques to provide security and privacy for data in transit. The SMP protocol has been designed to work with the Smart Health Care Monitoring System to provide a secure and private communication channel between devices in the system. The SMP protocol is an improvement over the existing security and privacy protocols for medical data. The simulation results proves that the SMP protocol is more efficient and scalable than the existing protocols. The SMP protocol is a valuable addition to the MIoT landscape and can help improve the privacy of data exchanged between medical devices.

The ability to effectively store and transmit high-resolution images such as MRI and CT scans without losing quality is critical to modernizing medical imaging. Traditional compression methods risk losing essential medical image data, which requires perfect detail for diagnosis. Quantum algorithms use superposition and entanglement to compress faster while preserving important information. This research presents a Quantum-enhanced Artificial Neural Network (QANN) model that combines quantum feature extraction with classical neural network topologies to improve image compression. Our approach consists of converting standardized classical data into quantum states, controlling these states using parameterized quantum circuits, and measuring the resulting states to produce enhanced feature vectors. The quantum-enhanced features are fed into a traditional neural network for image compression. The experimental results clearly show that our QANN framework outperforms standard models in terms of accurate reconstructed images, reduced size, and increased space-saving percentage, especially when dealing with large and complicated datasets.The QANN model demonstrates how quantum computing can significantly enhance the effectiveness of medical image processing solutions. Kaggle brain CT and MRI datasets and COVID-CXNet chest x-ray images are used. The proposed QANN model improves peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Using quantum technology, the image size is reduced for MRI (73.3 %), X-ray (74.1%), and CT-SCAN (71.8%) to save space.

Wireless Sensor network has emerged as a promising technique that revolutionizes the way of sensing information. Dense deployed sensor nodes at a specific region are likely to transfer redundant data to the base station. This increase the communicational overhead and affects network lifetime. Since energy conservation is the key issue in WSNs, data aggregation should be incorporated in order to save energy. The main aim of data aggregation technique is to collect and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we present a survey on data aggregation technique in Wireless Sensor Networks (WSN). Here, we explore different types of data aggregation algorithms based on network architecture, approaches and performance characteristics. Based on the study, the future research areas and issues to be focused are Security, Energy conservation, query processing, communication & uncertainty in sensor reading.

Federated learning is a technique of decentralized machine learning that allows multiple parties to collaborate and learn a shared model without sharing their raw data. Our paper proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The proposed framework employs SMOTE for handling class imbalance, outlier detection for identifying and removing abnormal observations, and hyperparameter tuning to optimize the model's performance. We evaluated the proposed framework using various performance metrics and demonstrated its effectiveness in detecting intrusions with other datasets (KDD-Cup 99 and UNSW-NB-15) and conventional classifiers. Furthermore, the proposed framework can protect sensitive data while achieving high intrusion detection performance.