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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

(a) Histogram representation of RR-interval for 1 min; (b) histogram representation of RR-interval for 1 h; and (c) histogram representation of RR-interval for complete wave.
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sensors-15-15067-f009: (a) Histogram representation of RR-interval for 1 min; (b) histogram representation of RR-interval for 1 h; and (c) histogram representation of RR-interval for complete wave.

Mentions: Figure 7 represents ECG waveform of a 20-year-old subject (female) for duration of 10 s. The grid interval used in this waveform is 0.2 s and amplitude is 0.5 mV. A 12-bit Analog-to-Digital converter sampling at 128 Hz frequency is used to get the digital signal. The RR-interval representation at different time durations of the mentioned subject is shown in Figure 8. This time interval is distance between two consecutive R-peaks. Figure 8a–c exploits the RR-interval for time duration of 1 min, 1 h and for complete wave, respectively. Figure 9 demonstrates histogram of RR-interval for different time durations. Figure 9a–c explains histogram for RR-interval representation for time duration of 1 min, 1 h and for complete wave, 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)

(a) Histogram representation of RR-interval for 1 min; (b) histogram representation of RR-interval for 1 h; and (c) histogram representation of RR-interval for complete wave.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15067-f009: (a) Histogram representation of RR-interval for 1 min; (b) histogram representation of RR-interval for 1 h; and (c) histogram representation of RR-interval for complete wave.
Mentions: Figure 7 represents ECG waveform of a 20-year-old subject (female) for duration of 10 s. The grid interval used in this waveform is 0.2 s and amplitude is 0.5 mV. A 12-bit Analog-to-Digital converter sampling at 128 Hz frequency is used to get the digital signal. The RR-interval representation at different time durations of the mentioned subject is shown in Figure 8. This time interval is distance between two consecutive R-peaks. Figure 8a–c exploits the RR-interval for time duration of 1 min, 1 h and for complete wave, respectively. Figure 9 demonstrates histogram of RR-interval for different time durations. Figure 9a–c explains histogram for RR-interval representation for time duration of 1 min, 1 h and for complete wave, 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