Pub Date : 2023-11-28DOI: 10.47164/ijngc.v14i4.1314
Zahra Azizi, Navid Zoghi, Saeed Behzadi
The Urban Heat Island phenomenon happens due to the differences in the thermal behavior between urban and rural areas which many factors such as vegetated, water, impervious and built-up areas could affect this phenomenon. Urban Heat Island consists of three types: Canopy heat island, Boundary heat island, and surface heat island. In this study, the surface type of urban heat island is analyzed. In this paper, 13 TM/ETM+ images have been obtained from 1990 to 2015(an image biennially). Urban Heat Islands effects are much more severe in summer; therefore, all images have been taken in summer. NDVI, IBI, albedo, and also land surface temperature were derived from images. Various neural network topologies have been used to identify the best model for predicting the urban heat island intensity. The LST of 2016 has been considered as validation data, thus the best result from fitting structures was obtained from Cascade which the Bayesian Regularization was its training algorithm (R-squared=0.62, RMSE=1.839 K).
{"title":"Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran","authors":"Zahra Azizi, Navid Zoghi, Saeed Behzadi","doi":"10.47164/ijngc.v14i4.1314","DOIUrl":"https://doi.org/10.47164/ijngc.v14i4.1314","url":null,"abstract":"The Urban Heat Island phenomenon happens due to the differences in the thermal behavior between urban and rural areas which many factors such as vegetated, water, impervious and built-up areas could affect this phenomenon. Urban Heat Island consists of three types: Canopy heat island, Boundary heat island, and surface heat island. In this study, the surface type of urban heat island is analyzed. In this paper, 13 TM/ETM+ images have been obtained from 1990 to 2015(an image biennially). Urban Heat Islands effects are much more severe in summer; therefore, all images have been taken in summer. NDVI, IBI, albedo, and also land surface temperature were derived from images. Various neural network topologies have been used to identify the best model for predicting the urban heat island intensity. The LST of 2016 has been considered as validation data, thus the best result from fitting structures was obtained from Cascade which the Bayesian Regularization was its training algorithm (R-squared=0.62, RMSE=1.839 K).","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"26 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139223955","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 : 2023-08-03DOI: 10.47164/ijngc.v14i3.767
Lata A. Bhavnani, Dr. Narendra Patel
Visual Question Answering (VQA) is an emerging AI research problem that combines computer vision, natural language processing, knowledge representation & reasoning (KR). Given image and question related to the image as input, it requires analysis of visual components of the image, type of question, and common sense or general knowledge to predict the right answer. VQA is useful in different real-time applications like blind person assistance, autonomous driving, solving trivial tasks like spotting empty tables in hotels, parks, or picnic places, etc. Since its introduction in 2014, many researchers have worked and applied different techniques for Visual question answering. Also, different datasets have been introduced. This paper presents an overview of available datasets and evaluation metrices used in the VQA area. Further paper presents different techniques used in the VQA domain. Techniques are categorized based on the mechanism used. Based on the detailed discussion and performance comparison we discuss various challenges in the VQA domain and provide directions for future work.
{"title":"Investigation of Available Datasets and Techniques for Visual Question Answering","authors":"Lata A. Bhavnani, Dr. Narendra Patel","doi":"10.47164/ijngc.v14i3.767","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.767","url":null,"abstract":"Visual Question Answering (VQA) is an emerging AI research problem that combines computer vision, natural language processing, knowledge representation & reasoning (KR). Given image and question related to the image as input, it requires analysis of visual components of the image, type of question, and common sense or general knowledge to predict the right answer. VQA is useful in different real-time applications like blind person assistance, autonomous driving, solving trivial tasks like spotting empty tables in hotels, parks, or picnic places, etc. Since its introduction in 2014, many researchers have worked and applied different techniques for Visual question answering. Also, different datasets have been introduced. This paper presents an overview of available datasets and evaluation metrices used in the VQA area. Further paper presents different techniques used in the VQA domain. Techniques are categorized based on the mechanism used. Based on the detailed discussion and performance comparison we discuss various challenges in the VQA domain and provide directions for future work.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"418 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139351894","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 : 2023-07-31DOI: 10.47164/ijngc.v14i3.1301
Miral J. Patel, Hasmukh P Koringa
Roads are the foundation of human civilisation and one of the most important routes of transportation. For the city planning, vehicle traffic control, road network monitoring, map updating and GPS navigation, the study of road extraction is extremely important. Due to similar spectral characteristics, occlusion of buildings and trees present in remote sensing images makes to extract the road surface is challenging task. This paper address the road network detection based on deep learning sementic segmentation architecture such as U-Net and SegNet from Remote Sensing Images (RSI). Publically available dataset is used to train the U-Net and SegNet. These methods are tuned with various hyper parameters such as learning rate, batch size and epochs. The performance of the methods is also observed under various optimization algorithm like SGD and ADAM. The suggested method performance is measured by training and testing accuracy, total training time, inference time, average iou score and average dice score.
{"title":"Deep Learning Architecture U-Net Based Road Network Detection from Remote Sensing Images","authors":"Miral J. Patel, Hasmukh P Koringa","doi":"10.47164/ijngc.v14i3.1301","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1301","url":null,"abstract":"Roads are the foundation of human civilisation and one of the most important routes of transportation. For the city planning, vehicle traffic control, road network monitoring, map updating and GPS navigation, the study of road extraction is extremely important. Due to similar spectral characteristics, occlusion of buildings and trees present in remote sensing images makes to extract the road surface is challenging task. This paper address the road network detection based on deep learning sementic segmentation architecture such as U-Net and SegNet from Remote Sensing Images (RSI). Publically available dataset is used to train the U-Net and SegNet. These methods are tuned with various hyper parameters such as learning rate, batch size and epochs. The performance of the methods is also observed under various optimization algorithm like SGD and ADAM. The suggested method performance is measured by training and testing accuracy, total training time, inference time, average iou score and average dice score.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"18 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353556","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 : 2023-07-31DOI: 10.47164/ijngc.v14i3.1150
Amit Choksi, Mehul Shah
Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.
{"title":"Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks","authors":"Amit Choksi, Mehul Shah","doi":"10.47164/ijngc.v14i3.1150","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1150","url":null,"abstract":"Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"66 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353661","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 : 2023-07-31DOI: 10.47164/ijngc.v14i3.1287
Yavuz Selim Şahi̇n, Atilla Erdi̇nç, Alperen Kaan Bütüner, Hilal Erdoğan
Plant pests cause significant economic losses to the agricultural sector. Tuta absoluta is one of the most important pests of the tomato plant, which has a high financial return. Accurate and rapid identification of tomato plant pests is essential to increase sustainable agricultural productivity. Computer vision and machine learning techniques such as deep learning and especially Convolutional Neural Networks (CNN) have effectively identified various plant pests. Training CNN-based algorithms that allow us to classify and identify objects can enable faster and more accurate pest detection than human observation. We used YOLOv5 (You Look Only Once), a CNN-based object detection algorithm. One thousand two hundred photos of tomato leaves infested by the T. absoluta pest were edited to train the YOLOv5 algorithm. Images include T. absoluta larvae and galleries created in leaves by these larvae. Experimental results showed that using the YOLOv5 algorithm could categorize tomato plant leaves correctly and detect T. absoluta larvae, galleries with 80% and 70-90% accuracy (mAP), respectively. The research is promising that deep learning-based object identification algorithms can be significantly effective in detecting agricultural pests early and preventing excessive use of pesticides.
植物害虫给农业部门造成了巨大的经济损失。Tuta absoluta 是番茄植物最重要的害虫之一，具有很高的经济回报率。准确、快速地识别番茄植物害虫对于提高可持续农业生产率至关重要。计算机视觉和机器学习技术，如深度学习，特别是卷积神经网络（CNN），已经有效地识别了各种植物害虫。通过训练基于 CNN 的算法，我们可以对物体进行分类和识别，从而实现比人工观察更快、更准确的害虫检测。我们使用了基于 CNN 的物体检测算法 YOLOv5（只看一次）。为了训练 YOLOv5 算法，我们编辑了一千二百张受 T. absoluta 害虫侵染的番茄叶片照片。图片包括 T. absoluta 幼虫和这些幼虫在叶片上形成的长廊。实验结果表明，使用 YOLOv5 算法可以对番茄植株叶片进行正确分类，检测 T. absoluta 幼虫和长廊的准确率（mAP）分别为 80% 和 70-90%。该研究表明，基于深度学习的物体识别算法在早期检测农业害虫和防止过度使用农药方面具有显著效果。
{"title":"Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm","authors":"Yavuz Selim Şahi̇n, Atilla Erdi̇nç, Alperen Kaan Bütüner, Hilal Erdoğan","doi":"10.47164/ijngc.v14i3.1287","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1287","url":null,"abstract":"Plant pests cause significant economic losses to the agricultural sector. Tuta absoluta is one of the most important pests of the tomato plant, which has a high financial return. Accurate and rapid identification of tomato plant pests is essential to increase sustainable agricultural productivity. Computer vision and machine learning techniques such as deep learning and especially Convolutional Neural Networks (CNN) have effectively identified various plant pests. Training CNN-based algorithms that allow us to classify and identify objects can enable faster and more accurate pest detection than human observation. We used YOLOv5 (You Look Only Once), a CNN-based object detection algorithm. One thousand two hundred photos of tomato leaves infested by the T. absoluta pest were edited to train the YOLOv5 algorithm. Images include T. absoluta larvae and galleries created in leaves by these larvae. Experimental results showed that using the YOLOv5 algorithm could categorize tomato plant leaves correctly and detect T. absoluta larvae, galleries with 80% and 70-90% accuracy (mAP), respectively. The research is promising that deep learning-based object identification algorithms can be significantly effective in detecting agricultural pests early and preventing excessive use of pesticides.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"14 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353503","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 : 2023-07-31DOI: 10.47164/ijngc.v14i3.1078
Pankaj Sharma, L. Awasthi
The proliferation of the Internet of Things devices in today’s environment generates huge amount of information about users and surroundings. Data produced by IoT devices attracts cybercriminals to perform malicious activity. The technologies like cloud and fog computing are emerging as the next-generation infrastructure for Internet of Things which may be challenging for digital investigation. In this paper, IoT and fog-based frameworks for digital forensics of IoT devices are explained and tools used in different levels of IoT such as physical level, cloud level, network level, and mobile application level are briefly discussed. The process of evidence collection and challenges in IoT forensics paradigms are well studied. For securing the extracted artifacts IoT evidence preservation framework is proposed (IoT-EvPF). Furthermore, the forensic challenges in a cloud computing environment and anti-forensics techniques used by cybercriminals to hide their identity and malicious activity are discussed. We have identified research gaps and provided a framework to encourage more thought and conversation about the difficulties of retrieving digital evidence from Fog Computing systems.
{"title":"Next-generation Digital Forensics Challenges and Evidence Preservation Framework for IoT Devices","authors":"Pankaj Sharma, L. Awasthi","doi":"10.47164/ijngc.v14i3.1078","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1078","url":null,"abstract":"The proliferation of the Internet of Things devices in today’s environment generates huge amount of information about users and surroundings. Data produced by IoT devices attracts cybercriminals to perform malicious activity. The technologies like cloud and fog computing are emerging as the next-generation infrastructure for Internet of Things which may be challenging for digital investigation. In this paper, IoT and fog-based frameworks for digital forensics of IoT devices are explained and tools used in different levels of IoT such as physical level, cloud level, network level, and mobile application level are briefly discussed. The process of evidence collection and challenges in IoT forensics paradigms are well studied. For securing the extracted artifacts IoT evidence preservation framework is proposed (IoT-EvPF). Furthermore, the forensic challenges in a cloud computing environment and anti-forensics techniques used by cybercriminals to hide their identity and malicious activity are discussed. We have identified research gaps and provided a framework to encourage more thought and conversation about the difficulties of retrieving digital evidence from Fog Computing systems.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"77 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353468","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}
Holter monitors are used to record Electrocardiogram (ECG) data which is extremely hard to analyze manually. Convolutional Neural Network (CNN) are known to be efficient for classification of image data. Hence, in this study, we are using Deep Convolutional Neural Network to classify the ECG data into various types of Arrhythmias. Denoising, segmentation and data augmentation techniques are used for pre-processing of the data. The proposed model uses the MIT-BIH Arrhythmia Dataset for training and evaluation purpose this dataset has much imbalance which has been removed using data augmentation techniques. The proposed approach shows an overall accuracy 99.67% along with 99.68% precision and 99.66% recall. Further, we have also compared the state-of-the-art models like 2D CNN, genetic ensemble of classifiers, Long Short-Term Memory (LSTM) Networks, etc results with proposed model. And the introduced approach is outperforming when compared to these models.
{"title":"Novel Deep Convolutional Neural Network based Classification of Arrhythmia","authors":"Priyanka Rathee, Mahesh Shirsath, Lalit Kumar Awasthi, Naveen Chauhan","doi":"10.47164/ijngc.v14i2.1153","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.1153","url":null,"abstract":"\u0000Holter monitors are used to record Electrocardiogram (ECG) data which is extremely hard to analyze manually. Convolutional Neural Network (CNN) are known to be efficient for classification of image data. Hence, in this study, we are using Deep Convolutional Neural Network to classify the ECG data into various types of Arrhythmias. Denoising, segmentation and data augmentation techniques are used for pre-processing of the data. The proposed model uses the MIT-BIH Arrhythmia Dataset for training and evaluation purpose this dataset has much imbalance which has been removed using data augmentation techniques. The proposed approach shows an overall accuracy 99.67% along with 99.68% precision and 99.66% recall. Further, we have also compared the state-of-the-art models like 2D CNN, genetic ensemble of classifiers, Long Short-Term Memory (LSTM) Networks, etc results with proposed model. And the introduced approach is outperforming when compared to these models. \u0000","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"39 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74814333","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 : 2023-03-31DOI: 10.47164/ijngc.v14i2.1195
Ena Jain, Debopam Acharaya
Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).
{"title":"A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods","authors":"Ena Jain, Debopam Acharaya","doi":"10.47164/ijngc.v14i2.1195","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.1195","url":null,"abstract":"Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).\u0000 ","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"47 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80134780","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 : 2023-03-31DOI: 10.47164/ijngc.v14i2.892
Ajeet Singh, N D Patel
Internet of Things (IoT) applications consist mainly of a group of small devices with sensing and/ or actuationcapabilities, working collaboratively to provide a specific functionality. IoT applications are becoming vital part ofour daily lives in various areas such as home automation, industrial automation, energy sector, healthcare sectorand smart transportation. Security is a term that is used to encompass the notions such as integrity, confidentiality,and privacy. A more prominent understanding of the Internet of Things (IoT) is that – it transmits data over theglobal internet and gives many services in many domains. It facilitates the machines and gadgets to communicatewith each other. IoT appliances have been facing several issues, therefore we identify variety of service domainsand their vulnerabilities. The main focus is on protecting the security and privacy. This paper presents anoverview of IoT models, applications in different domains, vulnerabilities, security privacy goals, possible attacks,and their corresponding countermeasures. The objective of this paper is also to provide a survey on categorizedlayer-wise attacks and countermeasures in detail. In the object layer, connectivity link Layer, several attacksare discussed based on RFID, NFC, ZigBee, Bluetooth, and Wi-Fi protocols. In the Transport Network layer,we have classified variety of attacks based on RPL, 6loWPAN, TCP/UDP, and IPv4/IPv6. Similarly, In theSession Communication, Data Aggregation Storage, Business Model, and Application layers, we have discoveredconsiderable number of attacks for each layer.
{"title":"Security Issues, Attacks and Countermeasures in Layered IoT Ecosystem","authors":"Ajeet Singh, N D Patel","doi":"10.47164/ijngc.v14i2.892","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.892","url":null,"abstract":"Internet of Things (IoT) applications consist mainly of a group of small devices with sensing and/ or actuationcapabilities, working collaboratively to provide a specific functionality. IoT applications are becoming vital part ofour daily lives in various areas such as home automation, industrial automation, energy sector, healthcare sectorand smart transportation. Security is a term that is used to encompass the notions such as integrity, confidentiality,and privacy. A more prominent understanding of the Internet of Things (IoT) is that – it transmits data over theglobal internet and gives many services in many domains. It facilitates the machines and gadgets to communicatewith each other. IoT appliances have been facing several issues, therefore we identify variety of service domainsand their vulnerabilities. The main focus is on protecting the security and privacy. This paper presents anoverview of IoT models, applications in different domains, vulnerabilities, security privacy goals, possible attacks,and their corresponding countermeasures. The objective of this paper is also to provide a survey on categorizedlayer-wise attacks and countermeasures in detail. In the object layer, connectivity link Layer, several attacksare discussed based on RFID, NFC, ZigBee, Bluetooth, and Wi-Fi protocols. In the Transport Network layer,we have classified variety of attacks based on RPL, 6loWPAN, TCP/UDP, and IPv4/IPv6. Similarly, In theSession Communication, Data Aggregation Storage, Business Model, and Application layers, we have discoveredconsiderable number of attacks for each layer.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"36 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84182215","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 : 2023-03-31DOI: 10.47164/ijngc.v14i2.384
Saurabh Kumar, Shwetank
Remote sensing (RS) is crucial for geographical change studies such as vegetation, forestry, agriculture, urbanization, and other land use/land cover (LU/LC) applications. The RS satellite imagery provides crucial geospatial information for observation and analysis of the entire earth's surface. In the proposed study, Multitemporal and multispectral Landsat satellite imagery is used to feature extraction of LU/LC of the Haridwar region. The preprocessing of used imagery is essential for accurately classify the land cover features using image preprocessing methods (geometric correction, atmospheric correction, and image transform). It helps to classify and change detection of land cover features accurately. After preprocessing of imagery, land cover features are divided into seven feature classes using the region of interest (ROI) tool with google earth image and topographic map. The Support vector machine (SVM) is a supervised learning method used to classify the land cover features of the study area. SVM classifier accurately classifies the imagery of the different years 2017, 2010, 2003, and 1996 with 90.00%, 82.75%, 86.37%, and 83.38% accuracy. The post-classification method is used to detect changes in land cover features. From 1996 to 2017, orchards and vegetation are rapidly decreased by 13,698.36 ha and 1,638.81 ha due to unplanned development in urban and industrial areas of the Haridwar region. The resultant LU/LC change information is important for monitoring and analyzing land cover changes of the study area.
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