Pervasive Healthcare System Based on Environmental Monitoring

Sangeetha, A.; Amudha, T.

doi:10.1002/9781119439004.ch7

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Examination of the methods and solutions to secure agriculture data is necessary to ensure that WSN becomes a resilient, safe, and trustable network. In the context of cloud computing, further studies are required to understand the role of this technology to bring financially economical agricultural systems [107] and enhance their technical properties, including scalability, efficiency, storage capacity, and overall performance [108]. To further accelerate the transition toward data-driven agriculture, the development of more precise, accurate, and efficient machine learning algorithms constitutes an intriguing opportunity for future research.…”

Section: Potentials Of Iot Cloud Computing and Ai For Agriculturementioning

confidence: 99%

Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis

Abdollahi

Rejeb

et al. 2021

Sustainability

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This study investigates how wireless sensor network (WSN) applications in agriculture are discussed in the current academic literature. On the basis of bibliometric techniques, 2444 publications were extracted from the Scopus database and analyzed to identify the temporal distribution of WSN research, the most productive journals, the most cited authors, the most influential studies, and the most relevant keywords. The computer program VOSviewer was used to generate the keyword co-occurrence network and partition the pertinent literature. Findings show the remarkable growth of WSN research in recent years. The most relevant journals, cited countries, and influential studies were also identified. The main results from the keyword co-occurrence clustering and the detailed analysis illustrate that WSN is a key enabler for precision agriculture. WSN research also focuses on the role of other technologies such as the Internet of Things, cloud computing, artificial intelligence, and unmanned aerial vehicles in supporting several agriculture activities, including smart irrigation and soil management. This study illuminates researchers’ and practitioners’ views of what has been researched and identifies possible opportunities for future studies. To the authors’ best knowledge, this bibliometric study represents the first attempt to map global WSN research using a comprehensive sample of documents published over nearly three decades.

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Section: Potentials Of Iot Cloud Computing and Ai For Agriculturementioning

confidence: 99%

Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis

Abdollahi

Rejeb

et al. 2021

Sustainability

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“…However, environmental factors are also equally important to detect the user's health status accurately [21]. Environmental data like environment temperature, noise level, pollution level have a direct impact on the user's health data generated by wearable devices [22]. Both of the issues, including fog offloading and environmental data analysis, are not thoroughly analyzed in the prior research works from the perspective of healthcare systems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Ijaz

Wang

et al. 2020

Electronics

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Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

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