Developing an interactive electronic maternity record
- Homer, Caroline, Catling-Paull, Christine, Sinclair, Dee, Faizah, Nor, Balasubramanian, Venki, Foureur, Maralyn, Hoang, Doan, Lawrence, Elaine
- Authors: Homer, Caroline , Catling-Paull, Christine , Sinclair, Dee , Faizah, Nor , Balasubramanian, Venki , Foureur, Maralyn , Hoang, Doan , Lawrence, Elaine
- Date: 2010
- Type: Text , Journal article
- Relation: British Journal of Midwifery Vol. 18, no. 6 (2010), p. 384-389
- Full Text: false
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- Description: Women have a strong need to be involved in their own maternity care. Pregnancy hand-held records encourage women's participation in their maternity care; gives them an increased sense of control and improves communication among care providers. They have been successfully used in the UK and New Zealand for almost 20 years. Despite evidence that supports the use of hand-held records, widespread introduction has not occurred in Australia. The need for an electronic version of pregnancy hand-held records has become apparent, especially after the introduction of the Electronic Medical Record in Australia. A personal digital assistant (PDA) was developed as an interactive antenatal electronic maternity record that health-care providers could use in any setting and women could access using the internet. This article will describe the testing of the antenatal electronic maternity record.
A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
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- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Full Text:
- Reviewed:
- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
Continuous patient monitoring with a patient centric agent : A block architecture
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
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- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
A lightweight blockchain based framework for underwater ioT
- Uddin, Md, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
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- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Uddin, Md , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices
- Solyman, Ahmad, Attar, Hani, Khosravi, Mohammad, Menon, Varun, Jolfaei, Alireza, Balasubramanian, Venki, Selvaraj, Buvana, Tavallali, Pooya
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
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- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
- Full Text:
- Reviewed:
- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
A secured framework for SDN-based edge computing in IoT-enabled healthcare system
- Li, Junxia, Cai, Jinjin, Khan, Fazlullah, Rehman, Ateeq, Balasubramanian, Venki
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
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- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Jacob, Sunil, Alagirisamy, Mukil, Menon, Varun, Kumar, B. Manoj, Balasubramanian, Venki
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
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- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Full Text:
- Reviewed:
- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
Blockchain leveraged decentralized IoT eHealth framework
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
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- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
- Full Text:
- Reviewed:
- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
Privacy protection and energy optimization for 5G-aided industrial internet of things
- Humayun, Mamoona, Jhanjhi, Nz, Alruwaili, Madallah, Amalathas, Sagaya, Balasubramanian, Venki, Selvaraj, Buvana
- Authors: Humayun, Mamoona , Jhanjhi, Nz , Alruwaili, Madallah , Amalathas, Sagaya , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 183665-183677
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- Description: The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Humayun, Mamoona , Jhanjhi, Nz , Alruwaili, Madallah , Amalathas, Sagaya , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 183665-183677
- Full Text:
- Reviewed:
- Description: The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Rapid health data repository allocation using predictive machine learning
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
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- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
- Reviewed:
- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
A scalable framework for healthcare monitoring application using the Internet of Medical Things
- Balasubramanian, Venki, Jolfaei, Alireza
- Authors: Balasubramanian, Venki , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: Software - Practice and Experience Vol. 51, no. 12 (2021), p. 2457-2468
- Full Text:
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- Description: Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd.
- Authors: Balasubramanian, Venki , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: Software - Practice and Experience Vol. 51, no. 12 (2021), p. 2457-2468
- Full Text:
- Reviewed:
- Description: Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd.
AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities
- Jacob, Sunil, Alagirisamy, Mukil, Xi, Chen, Balasubramanian, Venki, Srinivasan, Ram
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
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- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
- Full Text:
- Reviewed:
- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
An AI-enabled lightweight data fusion and load optimization approach for internet of things
- Jan, Mian, Zakarya, Muhammad, Khan, Muhammad, Mastorakis, Spyridon, Balasubramanian, Venki
- Authors: Jan, Mian , Zakarya, Muhammad , Khan, Muhammad , Mastorakis, Spyridon , Balasubramanian, Venki
- Date: 2021
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 122, no. (2021), p. 40-51
- Full Text: false
- Reviewed:
- Description: In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications. © 2021 Elsevier B.V. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.
Contention resolution in wi-fi 6-enabled internet of things based on deep learning
- Chen, Chen, Li, Junchao, Balasubramanian, Venki, Wu, Yongqiang, Zhang, Yongqiang, Wan, Shaohua
- Authors: Chen, Chen , Li, Junchao , Balasubramanian, Venki , Wu, Yongqiang , Zhang, Yongqiang , Wan, Shaohua
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 8, no. 7 (2021), p. 5309-5320
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- Description: Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE.
- Authors: Chen, Chen , Li, Junchao , Balasubramanian, Venki , Wu, Yongqiang , Zhang, Yongqiang , Wan, Shaohua
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 8, no. 7 (2021), p. 5309-5320
- Full Text:
- Reviewed:
- Description: Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE.
IoT-powered deep learning brain network for assisting quadriplegic people
- Vinoj, P., Jacob, Sunil, Menon, Varun, Balasubramanian, Venki, Piran, Md Jalil
- Authors: Vinoj, P. , Jacob, Sunil , Menon, Varun , Balasubramanian, Venki , Piran, Md Jalil
- Date: 2021
- Type: Text , Journal article
- Relation: Computers and Electrical Engineering Vol. 92, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Brain-Computer Interface (BCI) systems have recently emerged as a prominent technology for assisting paralyzed people. Recovery from paralysis in most patients using the existing BCI-based assistive devices is hindered due to the lack of training and proper supervision. The system's continuous usage results in mental fatigue, owing to a higher user concentration required to execute the mental commands. Moreover, the false-positive rate and lack of constant control of the BCI systems result in user frustration. The proposed framework integrates BCI with a deep learning network in an efficient manner to reduce mental fatigue and frustration. The Deep learning Brain Network (DBN) recognizes the patient's intention for upper limb movement by a deep learning model based on the features extracted during training. DBN correlates and maps the different Electroencephalogram (EEG) patterns of healthy subjects with the identified pattern's upper limb movement. The stroke-affected muscles of the paralyzed are then activated using the obtained superior pattern. The implemented DBN consisting of four healthy subjects and a quadriplegic patient achieved 94% accuracy for various patient movement intentions. The results show that DBN is an excellent tool for providing rehabilitation, and it delivers sustained assistance, even in the absence of caregivers. © 2021
Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks
- Jan, Syed, Khan, Rahim, Khan, Fazlullah, Jan, Mian, Balasubramanian, Venki
- Authors: Jan, Syed , Khan, Rahim , Khan, Fazlullah , Jan, Mian , Balasubramanian, Venki
- Date: 2021
- Type: Text , Journal article
- Relation: Computer Communications Vol. 174, no. (2021), p. 101-108
- Full Text: false
- Reviewed:
- Description: In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station. © 2021 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
Multi-variate data fusion technique for reducing sensor errors in intelligent transportation systems
- Manogaran, Gunasekaran, Balasubramanian, Venki, Rawal, Bharat, Saravanan, Vijayalakshmi, Montenegro-Marin, Carlos
- Authors: Manogaran, Gunasekaran , Balasubramanian, Venki , Rawal, Bharat , Saravanan, Vijayalakshmi , Montenegro-Marin, Carlos
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15564-15573
- Full Text: false
- Reviewed:
- Description: Connected vehicles in intelligent transportation system (ITS) scenario rely on environmental data for supporting user-centric applications along the driving time. Sensors equipped in the vehicles are responsible for accumulating data from the environment, followed by the fusion process. Such fusion process provides accurate and stable data required for the applications in a recurrent manner. In order to enhance the data fusion of connected vehicles, this article introduces multi-variate data fusion (MVDF) technique. This technique is competent in handling asynchronous and discrete data from the environment and streamlining them into continuous and delay-less inputs for the applications. The process of data fusion is aided through least square regression learning to determine the errors in different time instances. The indefinite and definite data fusion instances are differentiated using this regression model to identify the errors in fore-hand. Besides, the differentiation relies on the application run-time interval to progress data fusion within the same or extended time instance and data slots. In this manner the differentiation along with the error identification is regular until the application required data is met. The performance of this technique is verified using network simulator experiments for the metrics error, data utilization ratio, and computation time. The results show that this technique improves data utilization under controlled time and fewer errors. © 2001-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.
Short-term forecasting of load and renewable energy using artifical neural network
- Srinivasan, Ram, Balasubramanian, Venki, Selvaraj, Buvana
- Authors: Srinivasan, Ram , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2021
- Type: Text , Journal article
- Relation: International journal of engineering trends and technology Vol. 69, no. 6 (2021), p. 175-181
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- Description: Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for short-term electrical load forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularisation (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand. © 2021 Seventh Sense Research Group®
- Authors: Srinivasan, Ram , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2021
- Type: Text , Journal article
- Relation: International journal of engineering trends and technology Vol. 69, no. 6 (2021), p. 175-181
- Full Text:
- Reviewed:
- Description: Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for short-term electrical load forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularisation (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand. © 2021 Seventh Sense Research Group®
Smart sensing-enabled decision support system for water scheduling in orange orchard
- Khan, Rahim, Zakarya, Muhammad, Balasubramanian, Venki, Jan, Mian, Menon, Varun
- Authors: Khan, Rahim , Zakarya, Muhammad , Balasubramanian, Venki , Jan, Mian , Menon, Varun
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 16 (2021), p. 17492-17499
- Full Text:
- Reviewed:
- Description: The scarcity of water resources throughout the world demands its optimum utilization in various sectors. Smart Sensing-enabled irrigation management systems are the ideal solutions to ensure the optimum utilization of water resources in the agriculture sector. This paper presents a wireless sensor network-enabled Decision Support System (DSS) for developing a need-based irrigation schedule for the orange orchard. For efficient monitoring of various in-field parameters, our proposed approach uses the latest smart sensing technology such as soil moisture, leaf-wetness, temperature and humidity. The proposed smart sensing-enabled test-bed was deployed in the orange orchard of our institute for approximately one year and successfully adjusted its irrigation schedule according to the needs and demands of the plants. Moreover, a modified Longest Common SubSequence (LCSS) mechanism is integrated with the proposed DSS for distinguishing multi-valued noise from the abrupt changing scenarios. To resolve the concurrent communication problem of two or more wasp-mote sensor boards with a common receiver, an enhanced RTS/CTS handshake mechanism is presented. Our proposed DSS compares the most recently refined data with pre-defined threshold values for efficient water management in the orchard. Irrigation activity is scheduled if water deficit criterion is met and the farmer is informed accordingly. Both the experimental and simulation results show that the proposed scheme performs better in comparison to the existing schemes. © 2001-2012 IEEE.
- Authors: Khan, Rahim , Zakarya, Muhammad , Balasubramanian, Venki , Jan, Mian , Menon, Varun
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 16 (2021), p. 17492-17499
- Full Text:
- Reviewed:
- Description: The scarcity of water resources throughout the world demands its optimum utilization in various sectors. Smart Sensing-enabled irrigation management systems are the ideal solutions to ensure the optimum utilization of water resources in the agriculture sector. This paper presents a wireless sensor network-enabled Decision Support System (DSS) for developing a need-based irrigation schedule for the orange orchard. For efficient monitoring of various in-field parameters, our proposed approach uses the latest smart sensing technology such as soil moisture, leaf-wetness, temperature and humidity. The proposed smart sensing-enabled test-bed was deployed in the orange orchard of our institute for approximately one year and successfully adjusted its irrigation schedule according to the needs and demands of the plants. Moreover, a modified Longest Common SubSequence (LCSS) mechanism is integrated with the proposed DSS for distinguishing multi-valued noise from the abrupt changing scenarios. To resolve the concurrent communication problem of two or more wasp-mote sensor boards with a common receiver, an enhanced RTS/CTS handshake mechanism is presented. Our proposed DSS compares the most recently refined data with pre-defined threshold values for efficient water management in the orchard. Irrigation activity is scheduled if water deficit criterion is met and the farmer is informed accordingly. Both the experimental and simulation results show that the proposed scheme performs better in comparison to the existing schemes. © 2001-2012 IEEE.
A supervised learning model to identify the star potential of a basketball player
- Srinivasan, Ram, Balasubramanian, Venki, Vidyasagar, Abhishek
- Authors: Srinivasan, Ram , Balasubramanian, Venki , Vidyasagar, Abhishek
- Date: 2022
- Type: Text , Journal article
- Relation: Expert Systems Vol. 39, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: Basketball is a mathematical game with many abstract data interpretations. An average fan ceases to witness the revolution in sports, which is influenced using data science and analytics unless someone brings it to light. Nowadays, teams look at data and tend to make decisions on scouting the player for the team. The decision making for the coaches can be made easier using machine learning algorithms to identify the star potential of players. The paper provides a novel algorithm by building a machine learning model on all players to predict whether the player is a star or not. Besides, an interactive user interface is developed for coaches to input the player's data and to make an informed decision based on the prediction. © 2021 John Wiley & Sons Ltd.
- Authors: Srinivasan, Ram , Balasubramanian, Venki , Vidyasagar, Abhishek
- Date: 2022
- Type: Text , Journal article
- Relation: Expert Systems Vol. 39, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: Basketball is a mathematical game with many abstract data interpretations. An average fan ceases to witness the revolution in sports, which is influenced using data science and analytics unless someone brings it to light. Nowadays, teams look at data and tend to make decisions on scouting the player for the team. The decision making for the coaches can be made easier using machine learning algorithms to identify the star potential of players. The paper provides a novel algorithm by building a machine learning model on all players to predict whether the player is a star or not. Besides, an interactive user interface is developed for coaches to input the player's data and to make an informed decision based on the prediction. © 2021 John Wiley & Sons Ltd.