Recent Progress in Challenges of Wireless Biomedical Sensor Network

Zhang, Lili; Wu, Xiaoming

doi:10.1109/icbbe.2009.5163284

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The most common is a star topology where nodes are connected to a central coordinator in star manner [3], [4]. Consequently, the capability of connecting to a local or wide area networks is a requirement for WBANs [15][16][17][18][19]. In order to accomplish a connection to a local or a wide area network, WBANs need to have effective and well-organized network architecture.…”

Section: Related Workmentioning

confidence: 99%

A new Network Coordinator Node Design Selecting the Optimum Wireless Technology for Wireless Body Area Networks

2013

KSII TIIS

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This paper proposes a new network coordinator node design to select the most suitable wireless technology for WBANs by using fuzzy logic. Its goal is to select a wireless communication technology available considering the user/application requirements and network conditions. A WBAN is composed of a set of sensors placed in, on, or around human body, which monitors the human body functions and the surrounding environment. In an effort to send sensor readings from human body to medical center or a station, a WBAN needs to stay connected to a local or a wide area network by using various wireless communication technologies. Nowadays, several wireless networking technologies may be utilized in WLANs and/or WANs each of which is capable of sending WBAN sensor readings to the desired destination. Therefore, choosing the best serving wireless communications technology has critical importance to provide quality of service support and cost efficient connections for WBAN users. In this work, we have developed, modeled, and simulated some networking scenarios utilizing our fuzzy logic-based NCN by using OPNET and MATLAB. Besides, we have compared our proposed fuzzy logic based algorithm with widely used RSSI-based AP selection algorithm. The results obtained from the simulations show that the proposed approach provides appropriate outcomes for both the WBAN users and the overall network.

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Section: Related Workmentioning

confidence: 99%

A new Network Coordinator Node Design Selecting the Optimum Wireless Technology for Wireless Body Area Networks

2013

KSII TIIS

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“…For an ANN with predictive power to analyse the data, the dataset needs to be free from noise to reduce errors in classification and prediction, which would significantly impact medical experts in their professional biomedical interpretation. Given recent technological solutions, one relevant control algorithm is highlighted in solving these challenges [8].…”

Section: Introductionmentioning

confidence: 99%

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

et al. 2021

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With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

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“…driving a car or riding a bus. Therefore, the capability of connecting to local and wide area networks is a requirement for WBASN [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware Gateway Selection for Increasing the Lifetime of Wireless Body Area Sensor Networks

Bayılmış

Younis

2010

J Med Syst

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A Wireless Body Area Sensor Network (WBASN) is composed of a set of sensor nodes, placed on, near or within a human body. WBASNs opt to continuously monitor the health conditions of individuals under medical risk, e.g., elders and chronically ill people, without keeping them in a hospital or restraining their motion. A WBASN needs to stay connected to local or wide area networks using wireless technologies in order to send sensor readings to a medical center. The WBASN nodes are implanted within the human body and would thus have limited energy supply. Since the mission of the WBASN is very critical, increasing the lifetime of nodes is essential in order to maintain both practicality and effectiveness. This paper presents a new Gateway Selection Algorithm (GSA) that factors in the use of energy harvesting technologies and dynamically picks the most suitable WBASN node that serves as a gateway to other wireless networks. The goal of GSA is to balance the load among the nodes by adaptively changing the gateway node in WBASN depending on the energy reserve of nodes. Computer modeling and simulations of the proposed GSA are carried out using OPNET. The simulation results demonstrate the effectiveness of the proposed GSA approach.

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Recent Progress in Challenges of Wireless Biomedical Sensor Network

Cited by 6 publications

References 17 publications

A new Network Coordinator Node Design Selecting the Optimum Wireless Technology for Wireless Body Area Networks

A new Network Coordinator Node Design Selecting the Optimum Wireless Technology for Wireless Body Area Networks

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

Energy-aware Gateway Selection for Increasing the Lifetime of Wireless Body Area Sensor Networks

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