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Reasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.
Hemant Ghayvat; Prosanta Gope. Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI. Neural Computing and Applications 2021, 1 -13.
AMA StyleHemant Ghayvat, Prosanta Gope. Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI. Neural Computing and Applications. 2021; ():1-13.
Chicago/Turabian StyleHemant Ghayvat; Prosanta Gope. 2021. "Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI." Neural Computing and Applications , no. : 1-13.
Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
Muhammad Awais; Hemant Ghayvat; Anitha Krishnan Pandarathodiyil; Wan Maria Nabillah Ghani; Anand Ramanathan; Sharnil Pandya; Nicolas Walter; Mohamad Naufal Saad; Rosnah Binti Zain; Ibrahima Faye. Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging. Sensors 2020, 20, 5780 .
AMA StyleMuhammad Awais, Hemant Ghayvat, Anitha Krishnan Pandarathodiyil, Wan Maria Nabillah Ghani, Anand Ramanathan, Sharnil Pandya, Nicolas Walter, Mohamad Naufal Saad, Rosnah Binti Zain, Ibrahima Faye. Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging. Sensors. 2020; 20 (20):5780.
Chicago/Turabian StyleMuhammad Awais; Hemant Ghayvat; Anitha Krishnan Pandarathodiyil; Wan Maria Nabillah Ghani; Anand Ramanathan; Sharnil Pandya; Nicolas Walter; Mohamad Naufal Saad; Rosnah Binti Zain; Ibrahima Faye. 2020. "Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging." Sensors 20, no. 20: 5780.
Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
Sharnil Pandya; Hemant Ghayvat; Anirban Sur; Muhammad Awais; Ketan Kotecha; Santosh Saxena; Nandita Jassal; Gayatri Pingale. Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living. Sensors 2020, 20, 5448 .
AMA StyleSharnil Pandya, Hemant Ghayvat, Anirban Sur, Muhammad Awais, Ketan Kotecha, Santosh Saxena, Nandita Jassal, Gayatri Pingale. Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living. Sensors. 2020; 20 (18):5448.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat; Anirban Sur; Muhammad Awais; Ketan Kotecha; Santosh Saxena; Nandita Jassal; Gayatri Pingale. 2020. "Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living." Sensors 20, no. 18: 5448.
Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).
Hemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors 2019, 19, 766 .
AMA StyleHemant Ghayvat, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan, Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors. 2019; 19 (4):766.
Chicago/Turabian StyleHemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. 2019. "Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection." Sensors 19, no. 4: 766.
The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.
Sharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation 2018, 1, 42 .
AMA StyleSharnil Pandya, Hemant Ghayvat, Ketan Kotecha, Mohammed Awais, Saeed Akbarzadeh, Prosanta Gope, Subhas Chandra Mukhopadhyay, Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation. 2018; 1 (4):42.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. 2018. "Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol." Applied System Innovation 1, no. 4: 42.
The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.
Sharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Moi Hoon Yap; Prosanta Gope. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol. 2018, 1 .
AMA StyleSharnil Pandya, Hemant Ghayvat, Ketan Kotecha, Moi Hoon Yap, Prosanta Gope. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol. . 2018; ():1.
Chicago/Turabian StyleSharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Moi Hoon Yap; Prosanta Gope. 2018. "Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol." , no. : 1.
Heart sounds deliver vital physiological and pathological evidence about health. Wireless cardiac auscultation offers continuous cardiac monitoring of an individual without 24*7 manual healthcare care services. In this paper, a novel wireless sensing system to monitor and analyze cardiac condition is proposed, which sends the information to the caregiver as well as a medical practitioner with an application of the Internet of Things (IoT). An integrated system for heart sound acquisition, storage, asynchronous analysis has been developed, from scratch to information uploading through IoT and signal analysis. Cardiac auscultation sensing unit has been designed to monitor cardiovascular health of an individual. Bluetooth protocol is used to offer power efficiency and moderate data transmission rate. The Hilbert-Huang transform is used to eliminate interference signals and to help to extract the heart sound signal features. Subsequence segmentation algorithm based on double-threshold has been developed to extract physiological parameters. Preprocessing, segmentation and clustering technique were performed for significant health information interpretation. The cardiac auscultation monitoring system may provide a way for heart disease self-management.
Haoran Ren; Hailong Jin; Chen Chen; Hemant Ghayvat; Wei Chen. A Novel Cardiac Auscultation Monitoring System Based on Wireless Sensing for Healthcare. IEEE Journal of Translational Engineering in Health and Medicine 2018, 6, 1 -12.
AMA StyleHaoran Ren, Hailong Jin, Chen Chen, Hemant Ghayvat, Wei Chen. A Novel Cardiac Auscultation Monitoring System Based on Wireless Sensing for Healthcare. IEEE Journal of Translational Engineering in Health and Medicine. 2018; 6 (99):1-12.
Chicago/Turabian StyleHaoran Ren; Hailong Jin; Chen Chen; Hemant Ghayvat; Wei Chen. 2018. "A Novel Cardiac Auscultation Monitoring System Based on Wireless Sensing for Healthcare." IEEE Journal of Translational Engineering in Health and Medicine 6, no. 99: 1-12.
Sandeep Pirbhulal; Heye Zhang; Eshrat E Alahi; Hemant Ghayvat; Subhas Chandra Mukhopadhyay; Yuan-Ting Zhang; Wanqing Wu. Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69. Sensors 2017, 17, 606 .
AMA StyleSandeep Pirbhulal, Heye Zhang, Eshrat E Alahi, Hemant Ghayvat, Subhas Chandra Mukhopadhyay, Yuan-Ting Zhang, Wanqing Wu. Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69. Sensors. 2017; 17 (3):606.
Chicago/Turabian StyleSandeep Pirbhulal; Heye Zhang; Eshrat E Alahi; Hemant Ghayvat; Subhas Chandra Mukhopadhyay; Yuan-Ting Zhang; Wanqing Wu. 2017. "Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69." Sensors 17, no. 3: 606.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Activity Detection and Wellness Pattern Generation. Internet of Things 2017, 121 -143.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Activity Detection and Wellness Pattern Generation. Internet of Things. 2017; ():121-143.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Activity Detection and Wellness Pattern Generation." Internet of Things , no. : 121-143.
The smart home data analysis can be divided into two parts; one, domain is activity recognition that has been discussed in the last chapter, and the other one is wellness pattern generation and forecasting. The forecasting in the WSN based smart home is the dynamic learning from the historical sensing events. Transformation of prior sensing events into pattern and forecast can be done by the analysis of knowledge discovery and soft computing techniques. There are a number of knowledge and soft computing methods available, but these methods do not perform well in the AAL environment. Either these methods are complex and needs large training data or too simple where they offer poor accuracy (Moutacalli et al. 2015; Pulsford et al. 2011; Candás et al. 2014). For the Wellness Protocol based AAL the time series approach has been proposed and implemented. This time series approach includes the seasonal parameters from last year; it does not demand too much learning data. The rest of the chapter includes the wellness forecasting analysis and comparative results with other existing data mining methods.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Wellness Pattern Generation and Forecasting. Smart Sensors, Measurement and Instrumentation 2017, 145 -157.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Wellness Pattern Generation and Forecasting. Smart Sensors, Measurement and Instrumentation. 2017; ():145-157.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Wellness Pattern Generation and Forecasting." Smart Sensors, Measurement and Instrumentation , no. : 145-157.
The functional characteristics and performance metrics of Institute of Electrical and Electronic Engineering (IEEE) 802.15.4 standard make it the only effective option in short-range environmental control and monitoring applications. The composite sensor and actuator nodes based on the wireless technology are placed in the building environment. Wireless technology deployed in building ambiance endures from the interference of various communication protocols operating in the same unlicensed, unregulated Industrial Scientific Medical (ISM) band, apart from the attenuation loss. A smart home designer could not omit these factors in the smart building ambiance because the adverse effects of these issues on the system performance are substantial. Most of the researchers have reported on adverse effects, but not with the aspects of a smart building. The current research reports on the detailed realistic-experimental analysis and mitigation for different types of interference, the attenuation losses, and direction of arrival associated with smart building condition. This research also tries to investigate the mitigation by direction of arrival of the radio signal. Additionally, the wellness approach aims to generate the customised methodology that will support, suggest and assist the system designer in a smart building environment to evaluate and measure the on-site performance, so that the assessments are efficient, precise and accurate.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Issues and Mitigation of WSNs-Based Smart Building System. Smart Sensors, Measurement and Instrumentation 2017, 93 -120.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Issues and Mitigation of WSNs-Based Smart Building System. Smart Sensors, Measurement and Instrumentation. 2017; ():93-120.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Issues and Mitigation of WSNs-Based Smart Building System." Smart Sensors, Measurement and Instrumentation , no. : 93-120.
The present chapter concludes and summarizes whole research, moreover it defines the possibility of extension in this research according to future demands.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Conclusion and Future Works. Smart Sensors, Measurement and Instrumentation 2017, 159 -160.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Conclusion and Future Works. Smart Sensors, Measurement and Instrumentation. 2017; ():159-160.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Conclusion and Future Works." Smart Sensors, Measurement and Instrumentation , no. : 159-160.
The present chapter presents the most relevant research approach and methodologies based on WSNs-based home monitoring system for Ambient Assisted Living (AAL). There are some research works, but this section includes only the researchers who give full understanding and justify the work.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Literature Survey. Smart Sensors, Measurement and Instrumentation 2017, 13 -51.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Literature Survey. Smart Sensors, Measurement and Instrumentation. 2017; ():13-51.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Literature Survey." Smart Sensors, Measurement and Instrumentation , no. : 13-51.
In a smart home living environment, technology assists the occupants in their daily life. With the introduction of sensors, embedded processors and wireless communication technology, normal homes are converted into smart homes.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Introduction. Smart Sensors, Measurement and Instrumentation 2017, 1 -11.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Introduction. Smart Sensors, Measurement and Instrumentation. 2017; ():1-11.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Introduction." Smart Sensors, Measurement and Instrumentation , no. : 1-11.
In the present research, the WSNs-based Wellness Protocol System for home monitoring is planned and realized two levels; hardware and software. At the hardware level, heterogeneous sensors are installed to get multi-activity and multi-event; these wireless sensor nodes are developed on Intel Galileo. The sensors, XBee module (as RF transceiver only) are connected and programmed to Intel Galileo board. Data is received through central coordinator node and collected into local home gateway computer server. The software logic has been developed for wellness protocol. The software module is subdivided into different levels, such as data logging, data extraction, and data storage. One of the important tasks of the task of the software module is to forecast the change in activity and correlate it with the wellness of the occupant in near time or real time (Ghayvat et al. in IEEE Sens J 15:7341–7348, 2015a). Intel Galileo-based intelligent monitoring sensing system have been designed that operates on the wellness protocol and uses the features of IoTs. The sensors are interfaced to Intel Galileo. Galileo board processes sensor data by two algorithms, one is packet encapsulation, and another is intelligent sampling and control. The packet encapsulation algorithm is common for all sensing nodes in a network. While the intelligent sampling and control algorithm is programmed individually according to sensors characteristics and application. In the present chapter, the details of sensing node development, device configuration, deployment, wireless data communication, storage, and analysis approach of heterogeneous sensor data fusion have been documented.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Wellness Protocol Development and Implementation. Smart Sensors, Measurement and Instrumentation 2017, 53 -91.
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Wellness Protocol Development and Implementation. Smart Sensors, Measurement and Instrumentation. 2017; ():53-91.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Wellness Protocol Development and Implementation." Smart Sensors, Measurement and Instrumentation , no. : 53-91.
This book focuses on the development of wellness protocols for smart home monitoring, aiming to forecast the wellness of individuals living in ambient assisted living (AAL) environments. It describes in detail the design and implementation of heterogeneous wireless sensors and networks as applied to data mining and machine learning, which the protocols are based on. Further, it shows how these sensor and actuator nodes are deployed in the home environment, generating real-time data on object usage and other movements inside the home, and therefore demonstrates that the protocols have proven to offer a reliable, efficient, flexible, and economical solution for smart home systems. Documenting the approach from sensor to decision making and information generation, the book addresses various issues concerning interference mitigation, errors, security and large data handling. As such, it offers a valuable resource for researchers, students and practitioners interested in interdisciplinary studies at the intersection of wireless sensing processing, radio communication, the Internet of Things and machine learning, and in how they can be applied to smart home monitoring and assisted living environments.
Hemant Ghayvat; Subhas Chandra Mukhopadhyay. Wellness Protocol for Smart Homes. Smart Sensors, Measurement and Instrumentation 2017, 1 .
AMA StyleHemant Ghayvat, Subhas Chandra Mukhopadhyay. Wellness Protocol for Smart Homes. Smart Sensors, Measurement and Instrumentation. 2017; ():1.
Chicago/Turabian StyleHemant Ghayvat; Subhas Chandra Mukhopadhyay. 2017. "Wellness Protocol for Smart Homes." Smart Sensors, Measurement and Instrumentation , no. : 1.
Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.
Sandeep Pirbhulal; Heye Zhang; Eshrat E Alahi; Hemant Ghayvat; Subhas Chandra Mukhopadhyay; Yuan-Ting Zhang; Wanqing Wu. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2016, 17, 69 .
AMA StyleSandeep Pirbhulal, Heye Zhang, Eshrat E Alahi, Hemant Ghayvat, Subhas Chandra Mukhopadhyay, Yuan-Ting Zhang, Wanqing Wu. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors. 2016; 17 (12):69.
Chicago/Turabian StyleSandeep Pirbhulal; Heye Zhang; Eshrat E Alahi; Hemant Ghayvat; Subhas Chandra Mukhopadhyay; Yuan-Ting Zhang; Wanqing Wu. 2016. "A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network." Sensors 17, no. 12: 69.
Constructing a smart home is not a task without intricate challenges due to involvement of various tools and technologies. Therefore, this research work presents a concept of context-aware low power intelligent SmartHome (CLPiSmartHome). For CLPiSmartHome, we propose a communication model, which provides a common medium for communication, i.e., same communication language. Moreover, an architecture is also proposed that welcomes all the electronic devices to communicate with each other using a single platform service. The proposed architecture describes the application, analysis and visualization aspects of the CLPiSmartHome. Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS coreTMi5 machine with 3.2 GHz processor and 4 GB memory. Sample medical sensory data sets and fire detection datasets are tested on the proposed system. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets and triggers actions to provide safety measurements for elderly age people, patients, and others.
Murad Khan; Sadia Din; Sohail Jabbar; Moneeb Gohar; Hemant Ghayvat; S.C. Mukhopadhyay. Context-aware low power intelligent SmartHome based on the Internet of things. Computers & Electrical Engineering 2016, 52, 208 -222.
AMA StyleMurad Khan, Sadia Din, Sohail Jabbar, Moneeb Gohar, Hemant Ghayvat, S.C. Mukhopadhyay. Context-aware low power intelligent SmartHome based on the Internet of things. Computers & Electrical Engineering. 2016; 52 ():208-222.
Chicago/Turabian StyleMurad Khan; Sadia Din; Sohail Jabbar; Moneeb Gohar; Hemant Ghayvat; S.C. Mukhopadhyay. 2016. "Context-aware low power intelligent SmartHome based on the Internet of things." Computers & Electrical Engineering 52, no. : 208-222.
The performance metrics of IEEE 802.15.4 standard make it an only dominant option in short-range environmental monitoring and control applications. The heterogeneous sensor and actuator nodes based on the wireless technology are deployed into the smart building environment. Wireless technology deployed in building environment suffers from interference from different communication protocols operating in the same unlicensed ISM band, apart from the attenuation loss. A designer could not ignore these factors in the smart building because the adverse effect of these issues on system performance is considerable. Most of the researchers reported this but not with the aspects of the smart building. This research paper reports on the detailed experimental analysis and mitigation for different types of interference, the direction of arrival and attenuation losses associated with smart building condition. This research also tries to find the mitigation by direction of arrival of the radio signal. Our research aims to generate the customized methodology that will support and assist the smart building system designer to evaluate and measure the on-site performance so that these assessments will be precise, efficient and accurate. A realistic, smart home solution is applied to the building.
H. Ghayvat; Subhas Mukhopadhyay; X. Gui. Issues and mitigation of interference, attenuation and direction of arrival in IEEE 802.15.4/ZigBee to wireless sensors and networks based smart building. Measurement 2016, 86, 209 -226.
AMA StyleH. Ghayvat, Subhas Mukhopadhyay, X. Gui. Issues and mitigation of interference, attenuation and direction of arrival in IEEE 802.15.4/ZigBee to wireless sensors and networks based smart building. Measurement. 2016; 86 ():209-226.
Chicago/Turabian StyleH. Ghayvat; Subhas Mukhopadhyay; X. Gui. 2016. "Issues and mitigation of interference, attenuation and direction of arrival in IEEE 802.15.4/ZigBee to wireless sensors and networks based smart building." Measurement 86, no. : 209-226.
Internet of Things (IoT) is nowadays increasingly becoming a worldwide network of interconnected devices uniquely addressable, via a standard communication protocol. Such devices generate a massive volume of heterogeneous data, which lead a system towards a major computational challenges, such as aggregation, storing, and processing. Also, a major problem arises when there is a need to extract useful information from this massive volume of data. Therefore, to address these needs, this paper proposes an architecture to analyze big data in the IoT. The basic concept involves the partitioning of dynamic data, i.e., big data with the complex magnitude is divided into subsets. These subsets are based on the theoretical model of data fusion, which works in the Hadoop processing server to enhance the computational efficiency. The proposed architecture is tested by analyzing healthcare data sets, mainly comprises of activities including walking, running, ECG. The feasibility and efficiency of the proposed architecture are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed architecture efficiently analyze the massive volume of data with a maximum throughput.
Sadia Din; Hemant Ghayvat; Anand Paul; Awais Ahmad; M. Mazhar Rathore; Imran Shafi. An architecture to analyze big data in the Internet of Things. 2015 9th International Conference on Sensing Technology (ICST) 2015, 677 -682.
AMA StyleSadia Din, Hemant Ghayvat, Anand Paul, Awais Ahmad, M. Mazhar Rathore, Imran Shafi. An architecture to analyze big data in the Internet of Things. 2015 9th International Conference on Sensing Technology (ICST). 2015; ():677-682.
Chicago/Turabian StyleSadia Din; Hemant Ghayvat; Anand Paul; Awais Ahmad; M. Mazhar Rathore; Imran Shafi. 2015. "An architecture to analyze big data in the Internet of Things." 2015 9th International Conference on Sensing Technology (ICST) , no. : 677-682.