The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.
Voice of any human plays an important role in communication and sharing information among each other. Through voice, internal behavior can be identified as to whether the person is happy or angry which is reflected. A person’s behavior is not exactly reflected by their face; variation in his/her voice reflects somehow their behavior as there will be variation in voice and variation in frequency and pitch. Feelings and natural behaviors are important features, and there are many biological aspects through which they can be identified. Therefore, in this paper, we consider a Hindi speech specimen of different groups to identify the person’s behavior and natural feelings under different acoustic conditions. Many research papers show emotion recognition based on neural networks with different models using speech signals to identify the present status of any patient, and it helps to build a way for a smart healthcare system. Enabling service in terms of Blockchain means the sufferer does not require communicating with complex and failed tasks for collecting information from various sources to send to their expert. Blockchain provides experts access to systems and enables entry to the dataset section. Patients have total control over their data, and they no longer require monitoring to keep their data managed and up to date. Also, manually coordinating with data is required for multiple visitors, which can be a very tedious one. In this paper, we focused on feature removal of speech using different extraction approaches which were used to know the quality or state of voice specimens and also understand which feature extraction plays a vital role in gaining a close state of speech. Internet of Things-based learning platforms are used to gather the voice sample, and also, a deep gaining method was followed to reach and achieve the best accuracy and identify the error rate which will help to gather close behavior and state of mind. Finally, a proposed model based on the Gaussian mixture model as a classifier was used for its spotting and attestation.
Advancements in Healthcare Internet of Things (H-IoT) systems have created new opportunities and solutions for healthcare services, including the remote treatment and monitoring of patients. In addition, the security and privacy of personal health data must be ensured during data transfer. Security breaches in H-IoT can have serious safety and legal implications. This comprehensive review provides insights about secured data accession by employing cryptographic platforms such as H-IoT in big data, H-IoT in blockchain, H-IoT in machine learning and deep learning, H-IoT in edge computing, and H-IoT in software-defined networks. With this information, this paper reveals solutions to mitigate threats caused by different kinds of attacks. The prevailing challenges in H-IoT systems, including security and scalability challenges, real-time operating challenges, resource constraints, latency, and power consumption challenges are also addressed. We also discuss in detail the current trends in H-IoT, such as remote patient monitoring and predictive analytics. Additionally, we have explored future prospects, such as leveraging health data for informed strategic planning. A critical analysis performed by highlighting the prevailing limitations in H-IoT systems is also presented. This paper will hopefully provide future researchers with in-depth insights into the selection of appropriate cryptographic measures to adopt an energy-efficient and resource-optimized healthcare system.
The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.
Cloud-based storage ensures the secure dissemination of media. Authentication and integrity are important aspects in the distribution of digital media. Encryption-based techniques shelter this media between the communicating parties which are involved in a transaction. The challenge is how to restrict the digital media which is illegally redistributed by the authorized users. However, the digital watermarking technique and encryption-based methods are also not sufficient enough to provide copyright protection. The watermarking protocol is used to provide intellectual property for the customer and the service provider. This research paper provides a vigorous buyer-seller watermarking protocol without trusted certificate authority for copyright protection in the cloud environment. This research work uses the cloud environment which enables the cloud as a service infrastructural provider for storing credentials such as public and private secret keys and the digital certificates of interacting parties. The scheme uses additive homomorphism encryption with an effective key exchange algorithm for exchanging digital media. This proposed approach addresses the problems of anonymity and copy deterrence and protects the digital rights of the buyer and seller; these most up-to-date issues are related to information security. Furthermore, the experiment results conclude that the proposed protocol is flexible and secure even in a non-secure communication channel. We have used performance measures such as PSNR, NCC and cost in time methods for checking the integrity of the proposed protocol. The conducted experiments show a stronger robustness and high imperceptibility for the watermark and watermarked images.
Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.
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