Browsing by Author "Pabitha, B."

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A comprehensive security framework for WBANs in the healthcare environment
(CRC Press, 2024) Pabitha, B.; Sanshi, S.; Karthik, N.
The emergence of technology is constantly required by human society's healthcare system, where a patient is added to the environment of sickness every day. By providing real-time monitoring of patient vital signs, enabling remote patient care, and enhancing general medical diagnostics, Wireless Body Area Networks (WBANs) in e-healthcare have revolutionized the healthcare business. In our fast-paced environment, it is impossible to monitor every patient individually. Instead, WBAN can be used to treat patients in life-threatening situations. A new wireless network called WBAN was created using many tiny, short-power sensor nodes, communication links between nearby nodes, and a central base station to store and analyze the data. WBAN uses low-power, low-cost hardware with a 10 Kbps to 10 Mbps data rate. Many industries, including healthcare, senior care, sports and fitness, chronic illness management, military and emergency services, innovative apparel and fashion, precision agriculture, and farming, can successfully implement WBAN. These WBANs can be made using wearable digital apparel, accessories, and other items. In this chapter, WBAN is used in the healthcare system to monitor the fundamental values of temperature, pressure, blood sugar, and other parameters using a larger number of sensor nodes, transmit the monitored information promptly to a nearby base station (server), and then conduct data analysis to determine the patient's status accurately. As a result, tracking nodes and data transfer protocols must be highly secure to ensure data integrity. Here, strategies for potential node failures, improved technology for data connection faults, and corrective measures are provided to ensure confidentiality, integrity, and availability (CIA) for accurate analysis of patient data collection. However, there are significant privacy and data security issues that have been brought up by the use of WBANs in healthcare settings. This chapter offers a thorough security framework to handle the particular problems WBANs in e-healthcare provide. To guarantee the confidentiality, integrity, and accessibility of sensitive patient data, the framework includes encryption, authentication, access control, and intrusion detection technologies. Adopting contemporary security measures will lead to better patient outcomes and a more robust and secure healthcare ecosystem, promoting confidence between patients, healthcare providers, and technology. © 2025 selection and editorial matter, Anuj Kumar Singh and Sachin Kumar. All rights reserved.
Cardiovascular Diseases Divination using Artificial Neural Network with Ensemble Models
(Institute of Electrical and Electronics Engineers Inc., 2023) Pabitha, B.; Sanshi, S.; Karthik, N.
Health is wealth, but nowadays, wealth is health, where humans keep running their day-to-day activities without caring about their health for various reasons. Every human being in this world suffers from one or other diseases. Recently, cardiovascular diseases like heart attacks are prevalent in all age groups. Addressing cardiovascular diseases is essential before the disease reaches a crucial stage. Nowadays, artificial intelligence algorithms have been used to detect diseases in their early stages. In this piece of writing, a model of an artificial neural network is utilised to analyze, detect and predict the likelihood of cardiovascular disease in the early stages. In this proposed work, feed forward propagation, forward the input data to learn and map the relationships between inputs and outputs, and backward propagation is used to reduce the errors in the data. Further, an ensemble learning stacked model is used to achieve high accuracy in the prediction of diseases. To verify the correctness of the model, ensemble learning to stack is executed with three different models, namely Model 1, Model 2, and Model 3, with varying sets of feature selections. The experiment results show an accuracy rate of 93% in their predictions. Â© 2023 IEEE.
Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques
(Springer Science and Business Media Deutschland GmbH, 2024) Pabitha, B.; Vani, V.; Sanshi, S.; Karthik, N.
In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable sensors, and on-body sensors. The doctorâ€™s access the data recorded and monitored by the sensor embedded in the patient to treat critical situations immediately. Maintaining data integrity and guarding against threats is necessary to secure sensitive patient information. Several people have proposed schemes for authenticating data access through formal and informal verification. In this research work, we carry out multi-factor authentication extensively using zero-knowledge proofs. The anomaly detection of the sensors is detected using machine learning algorithms, which help tune the sensors to their correct working conditions. The work aims to concentrate on sensor working conditions promptly and to handle attacks like masquerade, forgery, and key escrow attacks. To assess whether performance metrics are superior in computing cost, storage overhead, and communication overhead, utilize the BAN logic tool. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Performance Evaluation of Wireless Health and Remote Monitoring Network Throughput Under Varying Conditions Using NetSim
(Institute of Electrical and Electronics Engineers Inc., 2024) Pabitha, B.; Vani, V.; Sanshi, S.
The Wireless Body Area Network (WBAN), organized in/out of the human body region to form Wireless enabled Health and Remote monitoring Network (WHRN), is trending on the medical platform for efficient diagnosis by the physician without the patient's physical visit. This network is framed with different biological sensors in the regional area of the human body to sense unlike biological signals promptly. Wearable WHRN, like smart watches and mobile phones, can notify people about stress, heart rate, and other physiological nods. The technology developed enhances the treatment for the patient, but the security of the information transmitted over different mediums is vulnerable. WHRN is simulated using the NetSim standard tool. Network performance metrics and their plots are analyzed using various encryption standards to provide data transmission and diagnosis security. Security is the primary concern for physiological data sensed and transmitted over different mediums. Â© 2024 IEEE.