This page has only limited features, please log in for full access.
Reliability, Availability, Maintainability, and Safety/Security (RAMS) analysis of Critical Infrastructures (CIs) can be applied to investigate their performance subjected to failure modes. The literature has witnessed earlier research approaches to RAM analysis. However, the integration of cybersecurity or safety aspects, along with the RAM to develop RAMS analysis methods, tools, and models, is a significant aspect for protecting CIs as their digitalization is progressing. In recent times, digital components and systems comprising information and communication technologies, smart devices, efficient gateways, faster protocols have transformed the CIs. This transformation comes with a considerable cost, such as the necessity to integrate innovative cyber-defense measures and policies due to new vulnerabilities, efficient maintenance strategies, risk management scenario development, resilience with self-healing architecture. Nevertheless, the current knowledge body lacks an up-to-date Systematic Literature Review (SLR) focused on addressing integrated protection aspects, recent developments, and evaluation criteria towards RAMS analysis for CIs. Accordingly, this study aims to provide a comprehensive SLR on existing studies dealing with developing techniques, methodologies, protocols, models, and RAMS analysis tools for different applications. This SLR followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 1513 records published between 2011 and 2020 are initially identified, which are later screened to 212 records to be considered for the eligibility check process. Finally, after examining the exclusion and inclusion criteria, a total of 19 articles are included in this research. In this SLR, a comparative analysis of existing solutions based on included studies is presented in different aspects such as developed architectures and methodologies, performance evaluation mechanisms, implementation scenarios, targeted applications, pros and cons, and applied CIs protection aspects associated with RAMS. The findings suggest that RAMS analysis for CIs is an emerging research area, with currently focused applications including power grid stations, Cyber-Physical Systems, cloud computing, maritime transportation, and Industrial Control Systems. 36.8% of the studies have performed a real-time implementation, while the remaining approaches have either performed simulation or preliminary analysis. Also, 84.2% of the included studies focus on integrating only three aspects of CIs protection. Finally, the open research challenges and limitations of existing studies and their respective emerging solutions are presented.
Sandeep Pirbhulal; Vasileios Gkioulos; Sokratis Katsikas. A Systematic Literature Review on RAMS analysis for critical infrastructures protection. International Journal of Critical Infrastructure Protection 2021, 33, 100427 .
AMA StyleSandeep Pirbhulal, Vasileios Gkioulos, Sokratis Katsikas. A Systematic Literature Review on RAMS analysis for critical infrastructures protection. International Journal of Critical Infrastructure Protection. 2021; 33 ():100427.
Chicago/Turabian StyleSandeep Pirbhulal; Vasileios Gkioulos; Sokratis Katsikas. 2021. "A Systematic Literature Review on RAMS analysis for critical infrastructures protection." International Journal of Critical Infrastructure Protection 33, no. : 100427.
The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.
Hina Magsi; Ali Sodhro; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Mabrook Al-Rakhami. A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications. Electronics 2021, 10, 367 .
AMA StyleHina Magsi, Ali Sodhro, Noman Zahid, Sandeep Pirbhulal, Lei Wang, Mabrook Al-Rakhami. A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications. Electronics. 2021; 10 (4):367.
Chicago/Turabian StyleHina Magsi; Ali Sodhro; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Mabrook Al-Rakhami. 2021. "A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications." Electronics 10, no. 4: 367.
As the Industrial Internet of Things (IIoT) is one of the emerging trends and paradigm shifts to revolutionize the traditional industries with the fourth wave of evolution or transform it into Industry 4.0. This all is merely possible with the sensor-enabled technologies, e.g., wireless sensor networks (WSNs) in various landscapes, where security provisioning is one of the significant challenges for miniaturized power hungry networks. Due to the increasing demand for the commercial Internet of things (IoT) devices, smart devices are also extensively adopted in industrial applications. If these devices are compromising the date/information, then there will be a considerable loss and critical issues, unlike information compromising level by the commercial IoT devices. So emerging industrial processes and smart IoT based methods in medical industries with state-of-the-art blockchain security techniques have motivated the role of secure industrial IoT. Also, frequent changes in android technology have increased the security of the blockchain-based IIoT system management. It is very vital to develop a novel blockchain-enabled cyber-security framework and algorithm for industrial IoT by adopting random initial and master key generation mechanisms over long-range low-power wireless networks for fast encrypted data processing and transmission. So, this paper has three remarkable contributions. First, a blockchain-driven secure, efficient, reliable, and sustainable algorithm is proposed. It can be said that the proposed solution manages keys randomly by introducing the chain of blocks with less power drain, a small number of cores, will slightly more communication and computation bits. Second, an analytic hierarchy process (AHP) based intelligent decision-making approach for the secure, concurrent, interoperable, sustainable, and reliable blockchain-driven IIoT system. AHP based solution helps the industry experts to select the more relevant and critical parameters such as (reliability in-line with a packet loss ratio), (convergence in mapping with delay), and (interoperability in association with throughput) for improving the yield of the product in the industry. Third, sustainable technology-oriented services are supporting to propose the novel cloud-enabled framework for the IIoT platform for regular monitoring of the products in the industry. Moreover, experimental results reveal that proposed approach is a potential candidate for the blockchain-driven IIoT system in terms of reliability, convergence, and interoperability with a strong foundation to predict the techniques and tools for the regulation of the adaptive system from Industry 4.0 aspect.
Ali Hassan Sodhro; Sandeep Pirbhulal; Muhammad Muzammal; Luo Zongwei. Towards Blockchain-Enabled Security Technique for Industrial Internet of Things Based Decentralized Applications. Journal of Grid Computing 2020, 18, 615 -628.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Muhammad Muzammal, Luo Zongwei. Towards Blockchain-Enabled Security Technique for Industrial Internet of Things Based Decentralized Applications. Journal of Grid Computing. 2020; 18 (4):615-628.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Muhammad Muzammal; Luo Zongwei. 2020. "Towards Blockchain-Enabled Security Technique for Industrial Internet of Things Based Decentralized Applications." Journal of Grid Computing 18, no. 4: 615-628.
Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data.
Romana Talat; Mohammad S. Obaidat; Muhammad Muzammal; Ali Hassan Sodhro; Zongwei Luo; Sandeep Pirbhulal. A decentralised approach to privacy preserving trajectory mining. Future Generation Computer Systems 2019, 102, 382 -392.
AMA StyleRomana Talat, Mohammad S. Obaidat, Muhammad Muzammal, Ali Hassan Sodhro, Zongwei Luo, Sandeep Pirbhulal. A decentralised approach to privacy preserving trajectory mining. Future Generation Computer Systems. 2019; 102 ():382-392.
Chicago/Turabian StyleRomana Talat; Mohammad S. Obaidat; Muhammad Muzammal; Ali Hassan Sodhro; Zongwei Luo; Sandeep Pirbhulal. 2019. "A decentralised approach to privacy preserving trajectory mining." Future Generation Computer Systems 102, no. : 382-392.
Wireless Body Sensor Network (BSNs) are wearable sensors with varying sensing, storage, computation, and transmission capabilities. When data is obtained from multiple devices, multi-sensor fusion is desirable to transform potentially erroneous sensor data into high quality fused data. In this work, a data fusion enabled Ensemble approach is proposed to work with medical data obtained from BSNs in a fog computing environment. Daily activity data is obtained from a collection of sensors which is fused together to generate high quality activity data. The fused data is later input to an Ensemble classifier for early heart disease prediction. The ensembles are hosted in a Fog computing environment and the prediction computations are performed in a decentralised manners. The results from the individual nodes in the fog computing environment are then combined to produce a unified output. For the classification purpose, a novel kernel random forest ensemble is used that produces significantly better quality results than random forest. An extensive experimental study supports the applicability of the solution and the obtained results are promising, as we obtain 98% accuracy when the tree depth is equal to 15, number of estimators is 40, and 8 features are considered for the prediction task.
Muhammad Muzammal; Romana Talat; Ali Hassan Sodhro; Sandeep Pirbhulal. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion 2019, 53, 155 -164.
AMA StyleMuhammad Muzammal, Romana Talat, Ali Hassan Sodhro, Sandeep Pirbhulal. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion. 2019; 53 ():155-164.
Chicago/Turabian StyleMuhammad Muzammal; Romana Talat; Ali Hassan Sodhro; Sandeep Pirbhulal. 2019. "A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks." Information Fusion 53, no. : 155-164.
Internet of Things (IoT) is an emerging technology for the smart city that interconnects various digital devices through Internet, hence, providing multiple innovative facilities from academia to industry and healthcare to business. Smart city is the ubiquitous and a paradigm shift which has revolutionized the entire landscape with the support of information and communication technology (ICT), sensor-enabled IoT devices. For the better and big picture of the entire scenarios with high visibility multimedia (i.e., video, audio, text, and images) transmission is the soul-concept in the smart world, but due to resource-constrained (power hungry and limited battery lifetime) nature of these tiny devices (which are building blocks of smart city) and voluminous amount of the data it is very challenging to openly talk about the sustainable and Green smart city platform. Thus, to remedy these problems two Hybrid Adaptive Bandwidth and Power Algorithm (HABPA), and Delay-tolerant Streaming Algorithm (DSA) are proposed by adopting stored video stream titled, StarWarsIV. Besides, a novel architecture of smart city system is proposed. Experimental results are obtained and analyzed in terms of performance metrics i.e., power drain, battery lifetime, delay, standard deviation and packet loss ratio (PLR) in association to the buffer size. It is concluded that the HABPA (45%,37%,20ms) significantly optimizes power drain, battery lifetime (37%), standard deviation (3.5dB), PLR (4.5%) of the IoT-enabled devices with less delay than DSA (43%, 32%,25ms, 5 dB, 5.75%) and Baseline (42%,28%, 30ms,6dB, 6.53% ) respectively during media transmission in smart city.
Ali Hassan Sodhro; Sandeep Pirbhulal; Zongwei Luo; Victor Hugo C. de Albuquerque. Towards an optimal resource management for IoT based Green and sustainable smart cities. Journal of Cleaner Production 2019, 220, 1167 -1179.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Zongwei Luo, Victor Hugo C. de Albuquerque. Towards an optimal resource management for IoT based Green and sustainable smart cities. Journal of Cleaner Production. 2019; 220 ():1167-1179.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Zongwei Luo; Victor Hugo C. de Albuquerque. 2019. "Towards an optimal resource management for IoT based Green and sustainable smart cities." Journal of Cleaner Production 220, no. : 1167-1179.
The video salient object detection (SOD) is the first step for the devices in the Internet of Things (IoT) to understand the environment around them. The video SOD needs the objects’ motion information in contiguous video frames as well as spatial contrast information from a single video frame. A large number of IoT devices’ computing power is not sufficient to support the existing SOD methods’ expensive computational complexity in emotion estimation, because they might have low hardware configurations (e.g., surveillance camera, and smartphone). In order to model the objects’ motion information efficiently for SOD, we propose an end-to-end video SOD algorithm with an efficient representation of the objects’ motion information. This algorithm contains two major parts: a 3D convolution-based X-shape structure that directly represents the motion information in successive video frames efficiently, and 2D densely connected convolutional neural networks (DenseNet) with pyramid structure to extract the rich spatial contrast information in a single video frame. Our method not only can maintain a small number of parameters as the 2D convolutional neural network but also represents spatiotemporal information uniformly that enables it can be trained end-to-end. We evaluate our proposed method on four benchmark datasets. The results show that our method achieves state-of-the-art performance compared with the other five methods.
Shizhou Dong; Zhifan Gao; Sandeep Pirbhulal; Gui-Bin Bian; Heye Zhang; Wanqing Wu; Shuo Li. IoT-based 3D convolution for video salient object detection. Neural Computing and Applications 2019, 32, 735 -746.
AMA StyleShizhou Dong, Zhifan Gao, Sandeep Pirbhulal, Gui-Bin Bian, Heye Zhang, Wanqing Wu, Shuo Li. IoT-based 3D convolution for video salient object detection. Neural Computing and Applications. 2019; 32 (3):735-746.
Chicago/Turabian StyleShizhou Dong; Zhifan Gao; Sandeep Pirbhulal; Gui-Bin Bian; Heye Zhang; Wanqing Wu; Shuo Li. 2019. "IoT-based 3D convolution for video salient object detection." Neural Computing and Applications 32, no. 3: 735-746.
This paper proposes novel Rate Control Video Transmission Algorithm (RCVTA) to optimize medical quality of service (m-QoS) in terms of network metrics such as, standard deviation (Std dev), throughput, peak-to-mean ratio (PMR), delay, average delay, jitter and average jitter during transmission of high-definition (HD) video stream named ‘Tracking and Retargeting in GI endoscopy’ over joint Internet of Multimedia Things (IoMT) and Body Sensor Networks (BSNs). Experimental results reveal that m-QoS is optimized with workahead transmission over joint BSN and IoMT networks for Tele-surgery.
Ali Hassan Sodhro; Aicha Sekhari; Yacine Ouzrout; Gul Hassan Sodhro; Noman Zahid; Sandeep Pirbhulal; M. Irfan Younas. Medical Quality of Service Optimization over Joint Body Sensor Networks and Internet of Multimedia Things. Internet of Things 2018, 205 -220.
AMA StyleAli Hassan Sodhro, Aicha Sekhari, Yacine Ouzrout, Gul Hassan Sodhro, Noman Zahid, Sandeep Pirbhulal, M. Irfan Younas. Medical Quality of Service Optimization over Joint Body Sensor Networks and Internet of Multimedia Things. Internet of Things. 2018; ():205-220.
Chicago/Turabian StyleAli Hassan Sodhro; Aicha Sekhari; Yacine Ouzrout; Gul Hassan Sodhro; Noman Zahid; Sandeep Pirbhulal; M. Irfan Younas. 2018. "Medical Quality of Service Optimization over Joint Body Sensor Networks and Internet of Multimedia Things." Internet of Things , no. : 205-220.
For intelligent medical systems, clinical information security is one of critical requirements. Recently, random binary sequences (RBSs) based on interpulse intervals (IPIs) were applied as secret keys to secure medical information in medical applications. Most of the existing RBS generation methods acquire a uniform quantity of bits per IPI, thereby requiring more processing time. However, the functional capacity of humans influences their heart rate variability, thereby resulting in the extraction of adaptive entropic bits per IPI across individuals. Therefore, adaptive computing-based RBS generation method is proposed to extract a variable number of bits on the basis of the heart rate (HR) bands of individuals to provide a balance between processing time and security in WBSNs. The proposed method is evaluated by using ECG recordings of 126 subjects with dynamic scenarios. Our experimental results that 128-bit RBSs generated by applying the proposed method can be used as secret keys for entity identifiers or patient’s data encryption for securing intelligent medical applications. In this study, the hamming distance metric is used to measure the uniqueness of the generated RBSs, and randomness of RBSs is computed by means of statistical tests, for different HR bands. Furthermore, the processing time of the proposed method for RBS generation shows improvement compared with the conventional techniques. The proposed approach is approximately three times faster for 55 ≤ HR < 80 and approximately two times faster for 80 ≤ HR < 105 and 80 ≤ HR < 105 than the existing IPI-based RBS generation techniques. Therefore, this study has got real-time significance for smart healthcare applications.
Wanqing Wu; Sandeep Pirbhulal; Guanglin Li. Adaptive computing-based biometric security for intelligent medical applications. Neural Computing and Applications 2018, 32, 11055 -11064.
AMA StyleWanqing Wu, Sandeep Pirbhulal, Guanglin Li. Adaptive computing-based biometric security for intelligent medical applications. Neural Computing and Applications. 2018; 32 (15):11055-11064.
Chicago/Turabian StyleWanqing Wu; Sandeep Pirbhulal; Guanglin Li. 2018. "Adaptive computing-based biometric security for intelligent medical applications." Neural Computing and Applications 32, no. 15: 11055-11064.
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.
Ali Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah. Convergence of IoT and product lifecycle management in medical health care. Future Generation Computer Systems 2018, 86, 380 -391.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Arun Kumar Sangaiah. Convergence of IoT and product lifecycle management in medical health care. Future Generation Computer Systems. 2018; 86 ():380-391.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah. 2018. "Convergence of IoT and product lifecycle management in medical health care." Future Generation Computer Systems 86, no. : 380-391.
Wanqing Wu; Sandeep Pirbhulal; Arun Kumar Sangaiah; Subhas Chandra Mukhopadhyay; Guanglin Li. Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Generation Computer Systems 2018, 86, 515 -526.
AMA StyleWanqing Wu, Sandeep Pirbhulal, Arun Kumar Sangaiah, Subhas Chandra Mukhopadhyay, Guanglin Li. Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Generation Computer Systems. 2018; 86 ():515-526.
Chicago/Turabian StyleWanqing Wu; Sandeep Pirbhulal; Arun Kumar Sangaiah; Subhas Chandra Mukhopadhyay; Guanglin Li. 2018. "Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications." Future Generation Computer Systems 86, no. : 515-526.
This chapter gives an innovative research for streaming of video over the Internet of Multimedia Things (IoMT) – an enrichment to the Internet of Things (IoT). Its primary goal is to support video streaming as a part of the implementation of IoT with Telemedicine and medical quality of service (m-QoS) optimization. The IoT enabled systems cannot be successful if the idea of pervasive connectivity of everything is unable to include ‘multimedia objects’. This problem is analyzed and focused by envisioning the concept of IoT and depicting a motivation concerning the vision of IoMT. Moreover, this chapter proposes three novel algorithms namely, Modified Lazy Video Transmission Algorithm (MLVTA), Online Video Transmission Algorithm (OVTA) and Rate Control Video Transmission Algorithm (RCVTA) for applications such as, Tele-monitoring, Tele-vision news casts and Tele-surgery by considering video streams of pre-recorded (i.e., stored), online and high-definition (HD) videos respectively, through work ahead transmission to optimize m-QoS in terms of standard deviation throughput, peak-to-mean ratio, jitter, average jitter, delay, and average delay over IoMT network.
Ali Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Mir Muhammad Lodro; Aicha Sekhari; Yacine Ouzrout; Sandeep Pirbhulal; Kaneez Fatima. Medical Quality of Service Optimization Over Internet of Multimedia Things. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications 2018, 271 -295.
AMA StyleAli Hassan Sodhro, Arun Kumar Sangaiah, Gul Hassan Sodhro, Mir Muhammad Lodro, Aicha Sekhari, Yacine Ouzrout, Sandeep Pirbhulal, Kaneez Fatima. Medical Quality of Service Optimization Over Internet of Multimedia Things. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications. 2018; ():271-295.
Chicago/Turabian StyleAli Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Mir Muhammad Lodro; Aicha Sekhari; Yacine Ouzrout; Sandeep Pirbhulal; Kaneez Fatima. 2018. "Medical Quality of Service Optimization Over Internet of Multimedia Things." Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications , no. : 271-295.
Currently, medical media technologies have become a center of attention due to emerging trends in miniaturized wearable devices from factories to health corner stores everywhere. Due to the power-constrained nature of these portable devices, it is challenging to adopt them during critical medical operations and diagnoses. Maximizing energy efficiency and, hence, extending the battery life is vital. In addition, conventional approaches with constant transmission power are inappropriate option for green and smart healthcare. Thus, this paper first proposes a transmission power control (TPC)-based energy-efficient algorithm (EEA) for when a subject is in different postures, i.e., standing, walking, and running, in wireless body sensor networks. Second, a hardware platform was developed on the Intel Galileo board to test and compare the proposed EEA and conventional adaptive TPC (ATPC) in terms of energy and channel reliability or packet loss ratio (PLR). Experimental results revealed that the proposed EEA obtained energy savings of 42.5% with an acceptable PLR compared with that of the traditional ATPC method.
Ali Hassan Sodhro; Sandeep Pirbhulal; Marwa Qaraqe; Sonia Lohano; Gul Hassan Sodhro; Naveed Ur Rehman Junejo; Zongwei Luo. Power Control Algorithms for Media Transmission in Remote Healthcare Systems. IEEE Access 2018, 6, 42384 -42393.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Marwa Qaraqe, Sonia Lohano, Gul Hassan Sodhro, Naveed Ur Rehman Junejo, Zongwei Luo. Power Control Algorithms for Media Transmission in Remote Healthcare Systems. IEEE Access. 2018; 6 ():42384-42393.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Marwa Qaraqe; Sonia Lohano; Gul Hassan Sodhro; Naveed Ur Rehman Junejo; Zongwei Luo. 2018. "Power Control Algorithms for Media Transmission in Remote Healthcare Systems." IEEE Access 6, no. : 42384-42393.
Rapid proliferation in the medical wearable device market has become the center of attention and changed the every corner of the medical world for the effective and economical information transmission, but because of the tiny size and high power drain more battery charge is consumed, so to remedy that problem this paper proposes ON-OFF Battery Friendly Algorithm (OBFA) to minimize the energy drain and hence to enhance the battery lifetime of these portable devices. Patient’s bio-signals such as, electrocardiogram (ECG) data from World’s larger database, i.e., PhysioNet is taken and examined with our proposed OBFA for further transmission over joint IoT and Wireless Body Sensor Networks (WBSNs). Experimental platform reveals that battery charge consumption is reduced and lifetime is improved in comparison with traditional baseline scheme.
Sandeep Pirbhulal; Ali Hassan Sodhro; Aicha Sekhari; Yacine Ouzrout; Wanqing Wu. Battery Friendly Internet of Medical Media Things Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 11 -18.
AMA StyleSandeep Pirbhulal, Ali Hassan Sodhro, Aicha Sekhari, Yacine Ouzrout, Wanqing Wu. Battery Friendly Internet of Medical Media Things Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; ():11-18.
Chicago/Turabian StyleSandeep Pirbhulal; Ali Hassan Sodhro; Aicha Sekhari; Yacine Ouzrout; Wanqing Wu. 2018. "Battery Friendly Internet of Medical Media Things Networks." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 11-18.
Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological (Stress Response Inventory, SRI), biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment. With the proposed model, a mental stress index (MSI) based on the correlation between salivary cortisol and HRV time-/frequency-domain features were established. A total of 30 college students were recruited to verify the feasibility of proposed method by identifying targeted stressful event. The obtained results reveal that MSI values were sensitive to acute stress, and could predict the association level of normal individual to a stress group with approximately 97% accuracy. Findings from this study could provide potential insight on self-tracking and training of individual's stress with adoption of wearable sensor system in a dynamic setting.
Wanqing Wu; Sandeep Pirbhulal; Heye Zhang; Subhas Chandra Mukhopadhyay. Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System. IEEE Journal of Biomedical and Health Informatics 2018, 23, 703 -713.
AMA StyleWanqing Wu, Sandeep Pirbhulal, Heye Zhang, Subhas Chandra Mukhopadhyay. Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System. IEEE Journal of Biomedical and Health Informatics. 2018; 23 (2):703-713.
Chicago/Turabian StyleWanqing Wu; Sandeep Pirbhulal; Heye Zhang; Subhas Chandra Mukhopadhyay. 2018. "Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System." IEEE Journal of Biomedical and Health Informatics 23, no. 2: 703-713.
Fog computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods.
Ali Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah; Sonia Lohano; Gul Hassan Sodhro; Zongwei Luo. 5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices. Sustainability 2018, 10, 1258 .
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Arun Kumar Sangaiah, Sonia Lohano, Gul Hassan Sodhro, Zongwei Luo. 5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices. Sustainability. 2018; 10 (4):1258.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah; Sonia Lohano; Gul Hassan Sodhro; Zongwei Luo. 2018. "5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices." Sustainability 10, no. 4: 1258.
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).
Ali Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Sonia Lohano; Sandeep Pirbhulal. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors 2018, 18, 923 .
AMA StyleAli Hassan Sodhro, Arun Kumar Sangaiah, Gul Hassan Sodhro, Sonia Lohano, Sandeep Pirbhulal. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors. 2018; 18 (3):923.
Chicago/Turabian StyleAli Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Sonia Lohano; Sandeep Pirbhulal. 2018. "An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications." Sensors 18, no. 3: 923.
Rapid proliferation in state-of-the art technologies has revolutionized the medical market for providing urgent, effective and economical health facilities to aging society. In this context media (i.e., video) transmission is considered as a quite significant step during first hour of the emergency for presenting a big and better picture of the event. However, the energy hungry media transmission process and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in medical internet of things (MIoT) or internet of medical things (IoMT). So, promoting Green (i.e., energy-efficient) transmission during voluminous and variable bit rate (VBR) video in MIoT is a challenging and crucial problem for researchers and engineers. Therefore, the need arose to conduct research on Green media transmission techniques to cater the need of upcoming wearable healthcare devices. Thus, this research contributes in two distinct ways; first, a novel and sustainable Green Media Transmission Algorithm (GMTA) is proposed, second, a mathematical model and architecture of Green MIoT are designed by considering a 8-min medical media stream named, ‘Navigation to the Uterine Horn, transection of the horn and re-anastomosis’ to minimize transmission energy consumption in media-aware MIoT, and to develop feasible media transmission schedule for sensitive and urgent health information from physian to patients and vice vers through extremely power hungry natured wearable devices. The experimental results demonstrate that proposed GMTA saves energy up to 41%, to serve the community.
Ali Hassan Sodhro; Arun Kumar Sangaiah; Sandeep Pirphulal; Aicha Sekhari; Yacine Ouzrout. Green media-aware medical IoT system. Multimedia Tools and Applications 2018, 78, 3045 -3064.
AMA StyleAli Hassan Sodhro, Arun Kumar Sangaiah, Sandeep Pirphulal, Aicha Sekhari, Yacine Ouzrout. Green media-aware medical IoT system. Multimedia Tools and Applications. 2018; 78 (3):3045-3064.
Chicago/Turabian StyleAli Hassan Sodhro; Arun Kumar Sangaiah; Sandeep Pirphulal; Aicha Sekhari; Yacine Ouzrout. 2018. "Green media-aware medical IoT system." Multimedia Tools and Applications 78, no. 3: 3045-3064.
Ali Hassan Sodhro; Giancarlo Fortino; Sandeep Pirbhulal; Mir Muhammad Lodro; Madad Ali Shah. Energy Efficiency in Wireless Body Sensor Networks. Networks of the Future 2017, 339 -354.
AMA StyleAli Hassan Sodhro, Giancarlo Fortino, Sandeep Pirbhulal, Mir Muhammad Lodro, Madad Ali Shah. Energy Efficiency in Wireless Body Sensor Networks. Networks of the Future. 2017; ():339-354.
Chicago/Turabian StyleAli Hassan Sodhro; Giancarlo Fortino; Sandeep Pirbhulal; Mir Muhammad Lodro; Madad Ali Shah. 2017. "Energy Efficiency in Wireless Body Sensor Networks." Networks of the Future , no. : 339-354.
Sandeep Pirbhulal; Heye Zhang; Subhas Chandra Mukhopadhyay; Chunyue Li; Yumei Wang; Guanglin Li; Wanqing Wu; Yuan-Ting Zhang. Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067–15089. Sensors 2017, 17, 607 .
AMA StyleSandeep Pirbhulal, Heye Zhang, Subhas Chandra Mukhopadhyay, Chunyue Li, Yumei Wang, Guanglin Li, Wanqing Wu, Yuan-Ting Zhang. Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067–15089. Sensors. 2017; 17 (3):607.
Chicago/Turabian StyleSandeep Pirbhulal; Heye Zhang; Subhas Chandra Mukhopadhyay; Chunyue Li; Yumei Wang; Guanglin Li; Wanqing Wu; Yuan-Ting Zhang. 2017. "Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067–15089." Sensors 17, no. 3: 607.