Optimizing Data Security and Storage in IoT Health Systems Through Adaptive Huffman Encoding and AES Encryption

Problem Definition

The increasing demand for IoT in healthcare services has led to the development of low-cost monitoring systems for patients with various medical conditions. However, the current systems face limitations in terms of security and performance. Traditional IoT security models have focused on registration, identification, and implementation phases to prevent unauthorized access to data. While this approach has been effective to some extent, there are shortcomings that have impacted the overall performance of the system. For example, the key generation module in the registration process relies on standard Hash functions which can be challenging to implement and enumerate.

Additionally, the encryption algorithm used in current systems may encounter storage issues when dealing with large amounts of data. These limitations highlight the need for an updated key generation module and a more efficient data storage solution to enhance the overall performance and security of IoT systems in healthcare services.

Objective

The objective of this research project is to enhance data security and storage optimization in IoT healthcare systems by introducing an adaptive Huffman encoding scheme to reduce data size and improve processing speed. Additionally, the implementation of an AES encryption algorithm aims to ensure patient data security by converting it into an unreadable form, making unauthorized access nearly impossible. By applying these advanced algorithms to a dataset sourced from the MIT-BIH database, the proposed work seeks to demonstrate the effectiveness of the enhanced technique in improving system performance and protecting patient data in a healthcare context.

Proposed Work

To overcome the issues related to data security and storage in IoT systems, an enhanced technique is proposed in this research project. The proposed method aims to address the limitations identified in existing systems by introducing an adaptive Huffman encoding scheme to reduce data size and improve processing speed. This encoding scheme will be beneficial in optimizing storage space and enhancing the overall performance of the system. Additionally, to enhance the security level of patient data, an AES encryption algorithm will be implemented in the proposed work. The AES encryption technique ensures that patient data is converted into an unreadable and unrecognizable form, making it nearly impossible for unauthorized individuals to decode or access sensitive information.

The rationale behind using Adaptive Huffman and AES encryption techniques lies in their efficiency, robustness, and widespread applicability, making them suitable for ensuring data protection in IoT healthcare systems. By employing these advanced algorithms, the proposed work aims to enhance data security and optimize storage while addressing the challenges faced by traditional IoT systems. In this research project, the proposed approach will be applied to a dataset sourced from the MIT-BIH database available on Physionet.org. This dataset includes ECG recordings from 47 subjects studied in the BIH Arrhythmia lab between 1975 and 1979.

The dataset contains 48 half-hour ECG recordings, with 23 selected randomly from 4000 patients who underwent 24-hour ambulatory ECG recordings at Boston's Beth Israel hospital. The remaining 25 recordings represent clinically significant arrhythmias and provide a diverse range of data for testing and validating the proposed technique. By utilizing real-world data from the MIT-BIH database, the proposed work aims to demonstrate the effectiveness of the enhanced technique in improving data security and storage optimization in IoT healthcare systems. The dataset selection aligns with the research objectives and enables the evaluation of the proposed approach in a healthcare context, highlighting its potential impact on enhancing patient data security and system performance.

Application Area for Industry

This project can be used in various industrial sectors such as healthcare, manufacturing, logistics, and smart cities. In the healthcare industry, the proposed solutions can enhance the security and storage of patient data, ensuring privacy and protection against unauthorized access. The adaptive Huffman encoding scheme will reduce data size and improve processing speed, while the AES encryption technique will secure the data in an unreadable form, safeguarding it from hackers. In manufacturing, the project can help in enhancing the security of production data and optimizing processes by ensuring data integrity and confidentiality. In logistics, the solutions can improve the tracking and monitoring of goods and vehicles by providing secure data transmission and storage.

In smart cities, the project can be utilized to secure critical infrastructure and enhance data protection in various smart devices and systems. Overall, implementing these solutions can address challenges related to data security and storage in IoT systems across different industrial domains, leading to improved performance and efficiency.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training in the field of IoT data security and storage management. By addressing the limitations of existing systems through the utilization of Adaptive Huffman encoding and AES encryption techniques, the project offers a new and innovative approach to ensuring data protection and efficient data management in IoT systems. This project can be highly relevant in the domain of healthcare monitoring systems, where the security and confidentiality of patient data are critical. Researchers, MTech students, and PhD scholars working in the field of IoT, data security, and healthcare technology can benefit from the code and literature generated by this project. They can utilize the proposed algorithms and methodologies to enhance their research methods, conduct simulations, and analyze data within educational settings.

The utilization of the MIT-BIH database for testing the proposed techniques adds real-world relevance to the project, allowing researchers and students to apply the developed methods to actual healthcare data. By focusing on practical applications and addressing current challenges in IoT systems, this project has the potential to contribute significantly to advancing research in the field. In the future, the scope of this project could be expanded to include additional datasets, testing scenarios, and optimization techniques. Further research could explore the integration of other encryption methods or data compression algorithms to enhance the overall performance of IoT systems. Additionally, collaboration with industry partners and healthcare providers could lead to the development of practical solutions for secure and efficient healthcare monitoring using IoT technology.

Algorithms Used

The proposed work uses Adaptive Huffman encoding and AES encryption algorithms to address data security and data storage issues in IoT. Adaptive Huffman encoding is utilized to reduce data size and enhance processing speed by extending storage space. This algorithm efficiently compresses data by maintaining a tree structure with non-increasing weights for sibling nodes. On the other hand, AES encryption ensures security by converting data into an unreadable form, making it challenging for unauthorized users to decode. AES is known for its robustness, as it uses longer keys and is widely applied in various fields due to its efficiency and resistance to attacks.

The project utilizes the MIT-BIH database for testing, which includes ECG recordings from 47 subjects studied in the BIH Arrhythmia lab.

Keywords

IoT, healthcare monitoring, data security, encryption algorithm, AES, adaptive Huffman encoding, data protection, key generation, IoT systems, storage issues, network security, cybersecurity, secure communication, data privacy, authentication, access control, secure data transmission, MIT-BIH database, ECG recordings.

SEO Tags

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