Sandeep Pirbhulal

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

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.

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

Assessment of Biofeedback Training for Emotion Management Through Wearable Textile Physiological Monitoring System

Negative emotion has a wide range of pernicious impacts on people, ranging from the failure in real-time task performance to the development of chronic health conditions. An unobtrusive wearable biofeedback system for personalized emotional management has been designed and presented in this paper. The system integrated heart rate variability (HRV) biofeedback to wearable biosensor platform, which could function both as an early stress warning system as well as a visual interface to manipulate subjects affective state. The designed and developed system would help subject to transform the negative emotion state into positive through real-time HRV biofeedback training. The results indicated that the real-time HRV biofeedback is significantly effective in cases of negative emotion. With the aid of the developed biofeedback system, the subhealth subjects could transform heart rhythm from negative emotion to positive emotion-related oscillation mode.

Relationship of short-term blood pressure variability with carotid intima-media thickness in hypertensive patients

BackgroundHigh blood pressure (BP) is among significant risk factor for stroke and other vascular occurrences, it experiences nonstop fluctuations over time as a result of a complex interface among cardiovascular control mechanisms. Large blood pressure variability (BPV) has been proved to be promising in providing potential regulatory mechanisms of the cardiovascular system. Although the previous studies also showed that BPV is associated with increased carotid intima-media thickness (IMT) and plaque, whether the correlation between variability in blood pressure and left common carotid artery-intima-media thickness (LCCA-IMT) is stronger than right common carotid artery-intima-media thickness (RCCA-IMT) remains uncertain in hypertension.MethodsWe conduct a study (78 hypertensive subjects, aged 28–79) to evaluate the relationship between BPV and carotid intima-media thickness in Shenzhen. The blood pressure was collected using the 24xa0h ambulatory blood pressure monitoring, and its variability was evaluated using standard deviation (SD), coefficient of variation (CV), and average real variability (ARV) during 24xa0h, daytime and nighttime. All the IMT measurements are collected by ultrasound.ResultsAs the results showed, 24xa0h systolic blood pressure variability (SBPV) evaluated by SD and ARV were significantly related to LCCA-IMT (r1xa0=xa00.261, Pxa0=xa00.021; r1xa0=xa00.262, Pxa0=xa00.021, resp.). For the daytime diastolic blood pressure variability (DBPV), ARV indices were significantly related to LCCA-IMT (r1xa0=xa00.239, Pxa0=xa00.035), which differed form BPV evaluated by SD and CV. For the night time, there is no significant correlation between the BPV and IMT. Moreover, for all the subjects, there is no significant correlation between the BPV and RCCA-IMT/number of plaques, whereas, the SD, CV, and ARV of daytime SBP showed a positive correlation with LCCA-IMT (r1xa0=xa00.312, Pxa0=xa00.005; r1xa0=xa00.255, Pxa0=xa00.024; r1xa0=xa00.284, Pxa0=xa00.012, resp.). Moreover, the ARV of daytime SBPV, 24xa0h SBPV and nighttime DBPV showed a positive correlation with the number of plaques of LCCA (r1xa0=xa00.356, Pxa0=xa00.008; r1xa0=xa00.297, Pxa0=xa00.027; r1xa0=xa00.278, Pxa0=xa00.040, resp.). In addition, the number of plaques in LCCA had higher correlation with pulse pressure and diastolic blood pressure than that in RCCA. And multiple regression analysis indicated LCCA-IMT might not only be influenced by age or smoking but also by the SD index of daytime SBPV (pxa0=xa00.035).ConclusionsThe results show that SBPV during daytime and 24xa0h had significant correlation with IMT, for the hypertensive subjects from the southern area of China. Moreover, we also found the daytime SBPV to be the best predictor for the progression of IMT in multivariate regression analysis. In addition, the present study suggests that the correlation between BPV and left common carotid artery—intima-media thickness/number of plaques is stronger than right common carotid artery-intima-media thickness/number of plaques.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).

A Novel Biometric Algorithm to Body Sensor Networks

In Body Sensor Networks (BSN) several sensor nodes are attached on, inside or around human body to monitor vital sign signals such as, Electrocardiogram (ECG), Electroencephalogram (EEG), Blood pressure etc. The information from each sensor node is very significant; therefore privacy and security is very important during data transmission in BSN. The conventional cryptographic approaches make use of cryptographic keys to achieve authentication, and use of these keys not only require high resource utilization and computation time, but also consume large amount of energy, power and memory in BSN. Therefore, it is necessary to develop power efficient and less computational complex authentication technique for BSN. In this paper we design a novel biometric algorithm which is based on biometric feature Electrocardiogram (ECG) and uses Data Authentication Function (DAF) for the security of BSN instead of utilizing traditional key generation procedure. Our proposed algorithm is compared with two cryptographic authentication techniques, Data Encryption Standard (DES) which is symmetric or private-key based encryption technique and RSA (Rivest Shamir Adleman) which is asymmetric or public-key based encryption scheme. Simulation is performed in MATLAB and results explain that our algorithm is efficient in terms of transmission time utilization and average remaining energy.

Heart-beats based biometric random binary sequences generation to secure wireless body sensor networks

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25–30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study. The proposed technique incorporates a finite monotonic increasing sequences generation mechanism of IPIs and a cyclic block encoding procedure that extracts a high number of entropic bits from each IPI. To validate the proposed technique, 89 ECG recordings including 25 healthy individuals in a laboratory environment, 20 from MIT-BIH Arrhythmia Database, and 44 cardiac patients from the clinical environment are considered. By applying the proposed technique on the ECG signals, at most 16 random bits can be extracted from each heartbeat to generate 128-bit RBSs via concatenation of eight consecutive IPIs. And the randomness and distinctiveness of generated 128-bit RBSs are measured based on the National Institute of Standards and Technology statistical tests and hamming distance, respectively. From the experimental results, the generated 128-bit RBSs from both healthy subjects and patients can potentially be used as keys for encryption or entity identifiers to secure WBSNs. Moreover, the proposed approach is examined to be up to four times faster than the existing heartbeat-based RBSs generation schemes. Therefore, the developed technique necessitates less processing time (0–8 s) in real-time health monitoring scenarios to construct 128-bit RBSs in comparisons with current methods.

Quantification of three-dimensional computed tomography angiography for evaluating coronary luminal stenosis using digital subtraction angiography as the standard of reference.

ObjectiveWe sought to evaluate the accuracy of quantitative three-dimensional (3D) CT angiography (CTA) for the assessment of coronary luminal stenosis using digital subtraction angiography (DSA) as the standard of reference.MethodTwenty-three patients with 54 lesions were referred for CTA followed by DSA. The CTA scans were performed with 256-slice spiral CT. 3D CTA were reconstructed from two-dimensional CTA imaging sequences in order to extract the following quantitative indices: minimal lumen diameter, percent diameter stenosis (%DS), minimal lumen area, and percent area stenosis (%AS). Correlation and limits of agreement were calculated using Pearson correlation and Bland–Altman analysis, respectively. The diagnostic performance and the diagnostic concordance of 3D CTA-derived anatomic parameters (%DS, %AS) for the detection of severe coronary arterial stenosis (as assessed by DSA) were presented as sensitivity, specificity, diagnostic accuracy, and Kappa statistics. Of which vessels with %DS >50% or with %AS >75% were identified as severe coronary arterial lesions.ResultThe correlations of the anatomic parameters between 3D CTA and DSA were significant (rxa0=xa00.51–0.74, Pxa0<xa00.001). Bland–Altman analysis confirmed that the mean differences were small (from −1.11 to 27.39%), whereas the limits of agreement were relatively wide (from ±28.07 to ±138.64%). Otherwise, the diagnostic accuracy (74.1% with 58.3% sensitivity and 86.7% specificity for DS%; 74.1% with 45.8% sensitivity and 96.7% specificity for %AS) and the diagnostic concordance (kxa0=xa00.46 for DS%; 0.45 for %AS) of 3D CTA-derived anatomic parameters for the detection of severe stenosis were moderate.Conclusion3D advanced imaging reconstruction technique is a helpful tool to promote the use of CTA as an alternative to assess luminal stenosis in clinical practice.

Convergence of IoT and product lifecycle management in medical health care

Abstract Emerging trends in Internet of Medical Things (IoMT) or Medical Internet of Things (MIoT), and miniaturized devices with have entirely changed the landscape of the every corner. Main challenges that heterogeneous sensor-enabled devices are facing during the connectivity and convergence with other domains are, first, the information/knowledge sharing and collaboration between several communicating parties such as, from manufacturing engineer to medical expert, then from hospitals/healthcare centers to patients during disease diagnosis and treatment. Second, battery lifecycle and energy management of wearable/portable devices. This paper solves first problem by integrating IoMT with Product Lifecycle Management (PLM), to regulate the information transfer from one entity to another and between devices in an efficient and accurate way. While, second issue is resolved by proposing two, battery recovery-based algorithm (BRA), and joint energy harvesting and duty-cycle optimization-based (JEHDO) algorithm for managing the battery lifecycle and energy of the resource-constrained tiny wearable devices, respectively. Besides, a novel joint IoMT and PLM based framework is proposed for medical healthcare applications. Experimental results reveal that BRA and JEHDO are battery-efficient and energy-efficient respectively.

Energy-Efficiency of Tools and Applications on Internet

Abstract This chapter presents the comparative analysis of the various tools and applications in terms of energy-efficiency. Due to increasing demand of Green technology it is very challenging to minimize the energy consumption of software and hardware components. In addition, the use of applications or software on our computers consumes energy and it also affects the energy drain of several hardware components and system resources. Consequently, running web browsers, media players, file transfer protocols, wired and wireless security protocol applications will utilize the considerable amount of energy. In this remarkable research, we have run different types of experiments which contain the use of several measuring tools. Firstly, joulemeter and powertop (pTop) are used to monitor and calculate the energy drain of hardware and software while running web-based and stand-alone applications with Windows 7 and Linux (i.e. Ubuntu 16.04) operating systems on desktop and laptop computers. Secondly, it is presented that how much energy is consumed by an ordinary citizen on typical things of everyday use on the web.