Pub Date : 2021-05-12DOI: 10.1108/IJPCC-11-2020-0201
N. Basjaruddin, Faris Rifqi Fakhrudin, Y. Sudarsa, F. Noor
Purpose In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method. Design/methodology/approach The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements. Findings The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively. Originality/value In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.
{"title":"NFC and IoT-based electronic health card for elementary students using sensor fusion method","authors":"N. Basjaruddin, Faris Rifqi Fakhrudin, Y. Sudarsa, F. Noor","doi":"10.1108/IJPCC-11-2020-0201","DOIUrl":"https://doi.org/10.1108/IJPCC-11-2020-0201","url":null,"abstract":"\u0000Purpose\u0000In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method.\u0000\u0000\u0000Design/methodology/approach\u0000The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements.\u0000\u0000\u0000Findings\u0000The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively.\u0000\u0000\u0000Originality/value\u0000In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128666077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-07DOI: 10.1108/IJPCC-10-2020-0158
K. Kiran, D. RajeswaraRao
Purpose Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method. Design/methodology/approach This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network. Findings The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively. Originality/value The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.
{"title":"5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management","authors":"K. Kiran, D. RajeswaraRao","doi":"10.1108/IJPCC-10-2020-0158","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0158","url":null,"abstract":"\u0000Purpose\u0000Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method.\u0000\u0000\u0000Design/methodology/approach\u0000This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network.\u0000\u0000\u0000Findings\u0000The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively.\u0000\u0000\u0000Originality/value\u0000The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-06DOI: 10.1108/IJPCC-10-2020-0165
N. L, Manoj Priyatham M.
Purpose The purpose of this paper is to improve the lifetime ratio of wireless sensor networks for maintaining the battery level at a desired point for better improvement of network health. Design/methodology/approach Sensor point network (SPN) is used for variety of applications like weather check, tracking of undesirable vehicles and delivery of data to end points. The proposed special high health sensing point (SHHSP) scheme will overcome several limitations of existing game theory approaches with respect to delay, health and overall throughput. Findings The simulation results of the proposed SHHSP scheme confirms the excellence over the existing works examined with respect to delay, hops, energy consumed, nutrition SP, harmful SP, throughput and overhead. Practical implications It is proposed for a smart communication system in IoT, where in the communication between the sensing point network to its neighbouring sensing network is carried out by selection of SHHSP, this is implemented by using the remaining energy and distance vector with respect to control station. The system is applicable to weather check and can also be used in tracking of vehicles in a vehicle ad hoc networks. Originality/value It is subsidized to the IoT system and vehicle-to-vehicle communication system where in the safety is of utmost concern. The system is concentrated on the battery concern of SPN in a pool of SPNs.
{"title":"Lifetime ratio improvement technique using special fixed sensing points in wireless sensor network","authors":"N. L, Manoj Priyatham M.","doi":"10.1108/IJPCC-10-2020-0165","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0165","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to improve the lifetime ratio of wireless sensor networks for maintaining the battery level at a desired point for better improvement of network health.\u0000\u0000\u0000Design/methodology/approach\u0000Sensor point network (SPN) is used for variety of applications like weather check, tracking of undesirable vehicles and delivery of data to end points. The proposed special high health sensing point (SHHSP) scheme will overcome several limitations of existing game theory approaches with respect to delay, health and overall throughput.\u0000\u0000\u0000Findings\u0000The simulation results of the proposed SHHSP scheme confirms the excellence over the existing works examined with respect to delay, hops, energy consumed, nutrition SP, harmful SP, throughput and overhead.\u0000\u0000\u0000Practical implications\u0000It is proposed for a smart communication system in IoT, where in the communication between the sensing point network to its neighbouring sensing network is carried out by selection of SHHSP, this is implemented by using the remaining energy and distance vector with respect to control station. The system is applicable to weather check and can also be used in tracking of vehicles in a vehicle ad hoc networks.\u0000\u0000\u0000Originality/value\u0000It is subsidized to the IoT system and vehicle-to-vehicle communication system where in the safety is of utmost concern. The system is concentrated on the battery concern of SPN in a pool of SPNs.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123047753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-04DOI: 10.1108/IJPCC-08-2020-0117
Avinash Pawar, A. Kolte, Balkrishan Sangvikar
Purpose The purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities. Design/methodology/approach This research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment. Findings The outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India. Originality/value The paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.
{"title":"Techno-managerial implications towards communication in internet of things for smart cities","authors":"Avinash Pawar, A. Kolte, Balkrishan Sangvikar","doi":"10.1108/IJPCC-08-2020-0117","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0117","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities.\u0000\u0000\u0000Design/methodology/approach\u0000This research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment.\u0000\u0000\u0000Findings\u0000The outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India.\u0000\u0000\u0000Originality/value\u0000The paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127076325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-24DOI: 10.1108/IJPCC-08-2020-0113
C. R. Sahara, A. Aamer
Purpose Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing. Design/methodology/approach The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT. Findings The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission. Research limitations/implications While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process. Originality/value The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.
{"title":"Real-time data integration of an internet-of-things-based smart warehouse: a case study","authors":"C. R. Sahara, A. Aamer","doi":"10.1108/IJPCC-08-2020-0113","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0113","url":null,"abstract":"\u0000Purpose\u0000Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.\u0000\u0000\u0000Design/methodology/approach\u0000The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT.\u0000\u0000\u0000Findings\u0000The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission.\u0000\u0000\u0000Research limitations/implications\u0000While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process.\u0000\u0000\u0000Originality/value\u0000The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-11DOI: 10.1108/IJPCC-09-2020-0130
R. Krithiga, E. Ilavarasan
Purpose The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters. Design/methodology/approach In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm. Findings The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency. Originality/value The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.
{"title":"Hyperparameter tuning of AdaBoost algorithm for social spammer identification","authors":"R. Krithiga, E. Ilavarasan","doi":"10.1108/IJPCC-09-2020-0130","DOIUrl":"https://doi.org/10.1108/IJPCC-09-2020-0130","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.\u0000\u0000\u0000Design/methodology/approach\u0000In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.\u0000\u0000\u0000Findings\u0000The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.\u0000\u0000\u0000Originality/value\u0000The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-08DOI: 10.1108/IJPCC-06-2020-0052
A. R. Reddy, Dr Narayana Rao Appini
Purpose In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods. Design/methodology/approach This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem. Findings In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services. Originality/value The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.
{"title":"An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs","authors":"A. R. Reddy, Dr Narayana Rao Appini","doi":"10.1108/IJPCC-06-2020-0052","DOIUrl":"https://doi.org/10.1108/IJPCC-06-2020-0052","url":null,"abstract":"\u0000Purpose\u0000In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.\u0000\u0000\u0000Design/methodology/approach\u0000This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem.\u0000\u0000\u0000Findings\u0000In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services.\u0000\u0000\u0000Originality/value\u0000The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128343254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-08DOI: 10.1108/IJPCC-08-2020-0120
H. Valiveti, B. Santhosh Kumar, Lakshmi Chaitanya Duggineni, Swetha Namburu, Swaraja Kuraparthi
Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques. Findings Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested. Practical implications Denoising of the audio samples for perfect feature extraction was a tedious chore. Originality/value The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.
{"title":"Soft computing based audio signal analysis for accident prediction","authors":"H. Valiveti, B. Santhosh Kumar, Lakshmi Chaitanya Duggineni, Swetha Namburu, Swaraja Kuraparthi","doi":"10.1108/IJPCC-08-2020-0120","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0120","url":null,"abstract":"\u0000Purpose\u0000Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.\u0000\u0000\u0000Design/methodology/approach\u0000The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.\u0000\u0000\u0000Findings\u0000Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.\u0000\u0000\u0000Practical implications\u0000Denoising of the audio samples for perfect feature extraction was a tedious chore.\u0000\u0000\u0000Originality/value\u0000The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128961867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-07DOI: 10.1108/IJPCC-08-2020-0109
Sengathir Janakiraman, Deva Priya M., Christy Jeba Malar A., Karthick S., Anitha Rajakumari P.
Purpose The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss. Design/methodology/approach The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process. Findings The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity. Research limitations/implications DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process. Practical implications The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis. Social implications This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture. Originality/value The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.
{"title":"Reliable IoT-based Health-care System for Diabetic Retinopathy Diagnosis to defend the Vision of Patients","authors":"Sengathir Janakiraman, Deva Priya M., Christy Jeba Malar A., Karthick S., Anitha Rajakumari P.","doi":"10.1108/IJPCC-08-2020-0109","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0109","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.\u0000\u0000\u0000Design/methodology/approach\u0000The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.\u0000\u0000\u0000Findings\u0000The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.\u0000\u0000\u0000Research limitations/implications\u0000DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.\u0000\u0000\u0000Practical implications\u0000The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.\u0000\u0000\u0000Social implications\u0000This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.\u0000\u0000\u0000Originality/value\u0000The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116999119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-13DOI: 10.1108/IJPCC-09-2020-0138
Ch Rajendra prasad, Polaiah Bojja
Purpose This paper aims to present a non-linear mathematical model-based routing protocol for wireless body area networks (WBANs). Two non-linear mathematical models for WBANs are used in the proposed protocols Model 1 and Model 2. Model 1 intends to improve the data transmission rate and Model 2 intends to reduce energy consumption in the WBANs. These models are simulated for fixed deployment and priority-based data transmission, and performance of the network is analyzed under four constraints on WBANs. Design/methodology/approach Advancements in wireless technology play a vital role in several applications such as electronic health care, entertainment and games. Though WBANs are widely used in digital health care, they have restricted battery capacity which affects network stability and data transmission. Therefore, several research studies focused on reducing energy consumption and maximizing the data transmission rate in WBANs. Findings Simulation results of the proposed protocol exhibit superior performance in terms of four network constraints such as residual energy, the stability of the network, path loss and data transmission rate in contrast with conventional routing protocols. The performance improvement of these parameters confirms that the proposed algorithm is more reliable and consumes less energy than traditional algorithms. Originality/value The Model 1 of the proposed work provides maximum data extraction, which ensures reliable data transmission in WBANs. The Model 2 allocates minimal hop count path between the sink and the sensor nodes, which minimizes energy consumption in the WBANs.
{"title":"A non-linear mathematical model-based routing protocol for WBAN-based health-care systems","authors":"Ch Rajendra prasad, Polaiah Bojja","doi":"10.1108/IJPCC-09-2020-0138","DOIUrl":"https://doi.org/10.1108/IJPCC-09-2020-0138","url":null,"abstract":"\u0000Purpose\u0000This paper aims to present a non-linear mathematical model-based routing protocol for wireless body area networks (WBANs). Two non-linear mathematical models for WBANs are used in the proposed protocols Model 1 and Model 2. Model 1 intends to improve the data transmission rate and Model 2 intends to reduce energy consumption in the WBANs. These models are simulated for fixed deployment and priority-based data transmission, and performance of the network is analyzed under four constraints on WBANs.\u0000\u0000\u0000Design/methodology/approach\u0000Advancements in wireless technology play a vital role in several applications such as electronic health care, entertainment and games. Though WBANs are widely used in digital health care, they have restricted battery capacity which affects network stability and data transmission. Therefore, several research studies focused on reducing energy consumption and maximizing the data transmission rate in WBANs.\u0000\u0000\u0000Findings\u0000Simulation results of the proposed protocol exhibit superior performance in terms of four network constraints such as residual energy, the stability of the network, path loss and data transmission rate in contrast with conventional routing protocols. The performance improvement of these parameters confirms that the proposed algorithm is more reliable and consumes less energy than traditional algorithms.\u0000\u0000\u0000Originality/value\u0000The Model 1 of the proposed work provides maximum data extraction, which ensures reliable data transmission in WBANs. The Model 2 allocates minimal hop count path between the sink and the sensor nodes, which minimizes energy consumption in the WBANs.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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