Enhanced gray wolf optimization for estimation of time difference of arrival in WSNs

E., Devika; Saravanan, A.

doi:10.1108/ijpcc-05-2022-0181

Cited by 1 publication

(1 citation statement)

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“…The method optimizes the data-fusion process by taking into consideration the communication delay and energy consumption of the sensor nodes. Refs [15,16] proposed an approach that uses a combination of rule-based and machine learning techniques to integrate data from multiple sources and generate meaningful insights. The study provided simulation findings that showed how the suggested strategy might enhance the precision and dependability of IoT health systems.…”

Section: Data Fusion For Healthcare Data Security In Iotmentioning

confidence: 99%

A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network

Almaiah,

Yelisetti,

Arya

et al. 2023

Electronics

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IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.

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Section: Data Fusion For Healthcare Data Security In Iotmentioning

confidence: 99%