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An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Pirbhulal S, Zhang H, Mukhopadhyay SC, Li C, Wang Y, Li G, Wu W, Zhang YT - Sensors (Basel) (2015)

Bottom Line: Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc.Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security.However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China. sandeep@siat.ac.cn.

ABSTRACT
Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

No MeSH data available.


Related in: MedlinePlus

Block diagram of capacitive measurement system.
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sensors-15-15067-f002: Block diagram of capacitive measurement system.

Mentions: We design a device for extraction of ECG signal from a textile electrode. This device is based on filters and amplifiers for measuring ECG and breathing signal, its block diagram is shown in Figure 2. For sensing breathing activity and ECG signal, differential separation filter and a common part were included in the device, respectively. The common part consisted of two buffers, to provide impedance to the capacitive coupling with low impedance entailed by the consequent circuitry. Our study utilized operational amplifier ICs having high input resistance. The differential separation filter split the input signal into low frequency component including breathing signal (<1 Hz) and high frequency component containing ECG signal (>1 Hz). The circuit diagram from [18], was used in order to reduce common mode noise mainly due to power line interference. The differential separation filter divides the input signal into sets of subtractors, amplifiers and integrators according to DC suppression circuit. The part for sensing ECG signal consisted of an instrumentation amplifier, a high-pass filter (HPF), a low pass filter (LPF) and two inverting amplifiers. The circuit elements of the LPF and the HPF were designed to achieve a cutoff frequency of 40 and 5 Hz, respectively.


An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Pirbhulal S, Zhang H, Mukhopadhyay SC, Li C, Wang Y, Li G, Wu W, Zhang YT - Sensors (Basel) (2015)

Block diagram of capacitive measurement system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541821&req=5

sensors-15-15067-f002: Block diagram of capacitive measurement system.
Mentions: We design a device for extraction of ECG signal from a textile electrode. This device is based on filters and amplifiers for measuring ECG and breathing signal, its block diagram is shown in Figure 2. For sensing breathing activity and ECG signal, differential separation filter and a common part were included in the device, respectively. The common part consisted of two buffers, to provide impedance to the capacitive coupling with low impedance entailed by the consequent circuitry. Our study utilized operational amplifier ICs having high input resistance. The differential separation filter split the input signal into low frequency component including breathing signal (<1 Hz) and high frequency component containing ECG signal (>1 Hz). The circuit diagram from [18], was used in order to reduce common mode noise mainly due to power line interference. The differential separation filter divides the input signal into sets of subtractors, amplifiers and integrators according to DC suppression circuit. The part for sensing ECG signal consisted of an instrumentation amplifier, a high-pass filter (HPF), a low pass filter (LPF) and two inverting amplifiers. The circuit elements of the LPF and the HPF were designed to achieve a cutoff frequency of 40 and 5 Hz, respectively.

Bottom Line: Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc.Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security.However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China. sandeep@siat.ac.cn.

ABSTRACT
Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

No MeSH data available.


Related in: MedlinePlus