Pub Date : 2023-08-14DOI: 10.13052/jmm1550-4646.1957
A. M. Hafiz
Today with the substantial increase in the computing power of small devices and systems new challenges are emerging. For example, how to control a small handheld device which has the computing capabilities of a desktop Personal computer (PC) used five years ago. Devolving decision-making power to the device in order to make it more intelligent e.g. in the case of autonomous driving, is an interesting area. Deep learning has paved the way for this task due to its reliable decision-making capabilities which are quite popular. However for small devices there are constraints like availability of limited computation hardware, less power due to small batteries, need for real-time as well as accurate decision-making abilities, etc. In this regard, light-weight Convolutional Neural Networks (CNNs) are a valuable tool. Lightweight CNNs like MobileNets, ShuffleNets, CondenseNets, etc. are deep networks which have a much lesser number of layers and a much smaller number of parameters as compared to their larger CNN counterparts like GoogLeNet, Inception, ResNets, etc. Due to their unique advantages for small stand-alone systems, light-weight CNNs are used in these systems. In this literature survey the notable light-weight CNNs along with their architecture, design features, performance metrics, advantages, etc are discussed. The trends, issues and future scope in the area are also discussed. It is hoped that by studying this survey, the reader will engage in research in this interesting area.
{"title":"A Survey on Light-weight Convolutional Neural Networks: Trends, Issues and Future Scope","authors":"A. M. Hafiz","doi":"10.13052/jmm1550-4646.1957","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1957","url":null,"abstract":"Today with the substantial increase in the computing power of small devices and systems new challenges are emerging. For example, how to control a small handheld device which has the computing capabilities of a desktop Personal computer (PC) used five years ago. Devolving decision-making power to the device in order to make it more intelligent e.g. in the case of autonomous driving, is an interesting area. Deep learning has paved the way for this task due to its reliable decision-making capabilities which are quite popular. However for small devices there are constraints like availability of limited computation hardware, less power due to small batteries, need for real-time as well as accurate decision-making abilities, etc. In this regard, light-weight Convolutional Neural Networks (CNNs) are a valuable tool. Lightweight CNNs like MobileNets, ShuffleNets, CondenseNets, etc. are deep networks which have a much lesser number of layers and a much smaller number of parameters as compared to their larger CNN counterparts like GoogLeNet, Inception, ResNets, etc. Due to their unique advantages for small stand-alone systems, light-weight CNNs are used in these systems. In this literature survey the notable light-weight CNNs along with their architecture, design features, performance metrics, advantages, etc are discussed. The trends, issues and future scope in the area are also discussed. It is hoped that by studying this survey, the reader will engage in research in this interesting area.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1277-1298"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116396","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1959
Raja Varma Pamba, Rahul Bhandari, A. Asha, A. Bist
In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.
{"title":"An Optimal Resource Allocation in 5G Environment Using Novel Deep Learning Approach","authors":"Raja Varma Pamba, Rahul Bhandari, A. Asha, A. Bist","doi":"10.13052/jmm1550-4646.1959","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1959","url":null,"abstract":"In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1331-1356"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116454","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1955
Shams Tabrez Siddiqui, Hamzullah Khan, Md. Imran Alam, K. Upreti, S. Panwar, Sheela N. Hundekari
Blockchain is one of the most revolutionary technologies in the past decade due to its decentralisation, data integrity, reliability, and security. Blockchain technology is the next popular topic, and it has the potential to significantly alter the educational environment in many ways. Blockchain technology must be used in the education sector despite its challenges. Education is one of the sectors where blockchain-based solutions are still in use. Many academics possess extensive knowledge of the societal benefits blockchain technology might bring. The vast potential of blockchain can only be realised if education expands its knowledge of the technology. The primary goal of this research is to identify current problems related to educational institutions and identify blockchain features that could assist in addressing them. This research article will provide an overview of existing activities and address several perspectives on how blockchain can revolutionize the education sector. In prolongation, this article investigates the categories of blockchain technology applications, especially in the field of education. An in-depth discussion on the benefits and impact that blockchain brings to education is explored. Further, deliberate the abundant challenges of adopting blockchain in education. This study will direct the organizations/institutions to decide which blockchain application will benefit the most based on their requisites. The analysis will also provide information about other educational fields that may benefit from blockchain technology.
{"title":"A Systematic Review of the Future of Education in Perspective of Block Chain","authors":"Shams Tabrez Siddiqui, Hamzullah Khan, Md. Imran Alam, K. Upreti, S. Panwar, Sheela N. Hundekari","doi":"10.13052/jmm1550-4646.1955","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1955","url":null,"abstract":"Blockchain is one of the most revolutionary technologies in the past decade due to its decentralisation, data integrity, reliability, and security. Blockchain technology is the next popular topic, and it has the potential to significantly alter the educational environment in many ways. Blockchain technology must be used in the education sector despite its challenges. Education is one of the sectors where blockchain-based solutions are still in use. Many academics possess extensive knowledge of the societal benefits blockchain technology might bring. The vast potential of blockchain can only be realised if education expands its knowledge of the technology.\u0000The primary goal of this research is to identify current problems related to educational institutions and identify blockchain features that could assist in addressing them. This research article will provide an overview of existing activities and address several perspectives on how blockchain can revolutionize the education sector. In prolongation, this article investigates the categories of blockchain technology applications, especially in the field of education. An in-depth discussion on the benefits and impact that blockchain brings to education is explored. Further, deliberate the abundant challenges of adopting blockchain in education. This study will direct the organizations/institutions to decide which blockchain application will benefit the most based on their requisites. The analysis will also provide information about other educational fields that may benefit from blockchain technology.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1221-1254"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116342","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.19510
A. Ivanov, Ivaylo Bozhilov
The continual development of advanced networks within the Fifth Generation (5G) of wireless systems, and beyond, has seen the rise of multiple important research directions. These include cognitive radio (CR) and ultra-dense networks (UDNs), which are the focus of this article. The CR systems rely on an accurate assessment of the radio environment, which is provided by the spectrum sensing functionality. A review of such algorithms that are characterized by the detection of miscellaneous features of the received signal, together with their performance comparison, is presented. In addition, the application of a simple and adequate solution is assessed through its probability of detection, for a relevant UDN system model under the critical density limitation for the access point (AP) deployment
{"title":"A Review of Miscellaneous Spectrum Sensing Algorithms in 5G Ultra-dense Networks","authors":"A. Ivanov, Ivaylo Bozhilov","doi":"10.13052/jmm1550-4646.19510","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.19510","url":null,"abstract":"The continual development of advanced networks within the Fifth Generation (5G) of wireless systems, and beyond, has seen the rise of multiple important research directions. These include cognitive radio (CR) and ultra-dense networks (UDNs), which are the focus of this article. The CR systems rely on an accurate assessment of the radio environment, which is provided by the spectrum sensing functionality. A review of such algorithms that are characterized by the detection of miscellaneous features of the received signal, together with their performance comparison, is presented. In addition, the application of a simple and adequate solution is assessed through its probability of detection, for a relevant UDN system model under the critical density limitation for the access point (AP) deployment","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1357-1370"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116748","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1956
Haritha Akkineni, Madhubala Myneni, B. Padmaja, Ananda Ravuri, CH. V. K. N. S. N. Moorthy, Raviteja Cms
Privacy and anonymity aspects are playing a vital role in accessing smartphone apps. This is more evident in unexpected epidemic situations like COVID-19 while working with contact tracing apps. A human connectivity model is essential to analyse the widespread cases of viruses and vaccination patterns during the timeframe of March 2020 to May 2021. Smartphone apps that are supported by technologies like IoT and blockchain have already proven effective in tracing the Ebola epidemic. Thus, this technology, coupled with privacy-preserving features, would help to discover clusters with infectious contacts and alert the respective authorities. Besides, this can also allow us to understand the human connectivity model and the effectiveness of vaccines, which can aid in developing a plan of action for future epidemics. Hence, this article focuses on the analysis of data collected from contact tracing apps and a number of affected cases. It includes a study on early solutions with existing technologies, an overview and analysis of existing COVID-19 apps with vulnerabilities, proposed solutions, and data analysis on privacy and anonymity aspects of smartphone apps using the ARIMA model. It is evaluated by correlating it with the usage of contact tracing apps. The results assured a positive correlation between the number of downloads and the number of cases. This infers that even though the Indian government released these contact tracing apps, it all depends on the citizens to utilise them to their fullest. As a policy suggestion, it is stated that regardless of the prevalence of contact tracing apps, people must follow the rules and regulations suggested by the local health authorities and maintain social distancing in public places.
{"title":"Analysis of Data's Privacy and Anonymity Aspects of Contact Tracing Apps via Smartphones - A Use Case of COVID-19","authors":"Haritha Akkineni, Madhubala Myneni, B. Padmaja, Ananda Ravuri, CH. V. K. N. S. N. Moorthy, Raviteja Cms","doi":"10.13052/jmm1550-4646.1956","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1956","url":null,"abstract":"Privacy and anonymity aspects are playing a vital role in accessing smartphone apps. This is more evident in unexpected epidemic situations like COVID-19 while working with contact tracing apps. A human connectivity model is essential to analyse the widespread cases of viruses and vaccination patterns during the timeframe of March 2020 to May 2021. Smartphone apps that are supported by technologies like IoT and blockchain have already proven effective in tracing the Ebola epidemic. Thus, this technology, coupled with privacy-preserving features, would help to discover clusters with infectious contacts and alert the respective authorities. Besides, this can also allow us to understand the human connectivity model and the effectiveness of vaccines, which can aid in developing a plan of action for future epidemics. Hence, this article focuses on the analysis of data collected from contact tracing apps and a number of affected cases. It includes a study on early solutions with existing technologies, an overview and analysis of existing COVID-19 apps with vulnerabilities, proposed solutions, and data analysis on privacy and anonymity aspects of smartphone apps using the ARIMA model. It is evaluated by correlating it with the usage of contact tracing apps. The results assured a positive correlation between the number of downloads and the number of cases. This infers that even though the Indian government released these contact tracing apps, it all depends on the citizens to utilise them to their fullest. As a policy suggestion, it is stated that regardless of the prevalence of contact tracing apps, people must follow the rules and regulations suggested by the local health authorities and maintain social distancing in public places.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1255-1276"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116378","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1953
Itthiphol Eampoonga, A. Leelasantitham
The hybrid cloud ERP system is widely used in automobile companies in Thailand. It is a popular and effective strategic tool that aids in boosting organization’s competitiveness. However, because of their complexity, high risk, high resource requirements, and high investment costs, ERP projects still have a significant failure rate. It is widely acknowledged by academics and practitioners alike to be an extremely challenging endeavour. This study proposes a conceptual paradigm for postmodern ERP implementation across the entire life cycle. Mixed methodologies for this model’s theoretical development included case study observation, literature review, semi-structured interviews with ten IT experts and ERP consultants, and online questionnaires. Based on information gathered from 455 system users from 114 automobile industries sector, it was analysed by using Structural Equation Modelling (SEM). For data analysis, the partial least squares (PLS) method was employed. Out of the eighteen (18) hypotheses, fifteen (15) were supported by the PLS-SEM results. The conceptual model from the study that was presented can be put to use or helpful in the organization’s management, or project managers can utilize it as a framework and direction for hybrid cloud ERP implementation. The findings of the study can also be used to create a conceptual framework for the actual use of ERP systems for automobile industry, such as the incorporation of blockchain and postmodern ERP systems in many sectors of business. There is discussion of the findings’ implications for practical and research, and potential study areas are proposed.
{"title":"Overall Success Factors Affecting the Performances of Hybrid Cloud ERP: A Case Study of Automobile Industries in Thailand","authors":"Itthiphol Eampoonga, A. Leelasantitham","doi":"10.13052/jmm1550-4646.1953","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1953","url":null,"abstract":"The hybrid cloud ERP system is widely used in automobile companies in Thailand. It is a popular and effective strategic tool that aids in boosting organization’s competitiveness. However, because of their complexity, high risk, high resource requirements, and high investment costs, ERP projects still have a significant failure rate. It is widely acknowledged by academics and practitioners alike to be an extremely challenging endeavour. This study proposes a conceptual paradigm for postmodern ERP implementation across the entire life cycle. Mixed methodologies for this model’s theoretical development included case study observation, literature review, semi-structured interviews with ten IT experts and ERP consultants, and online questionnaires. Based on information gathered from 455 system users from 114 automobile industries sector, it was analysed by using Structural Equation Modelling (SEM). For data analysis, the partial least squares (PLS) method was employed. Out of the eighteen (18) hypotheses, fifteen (15) were supported by the PLS-SEM results. The conceptual model from the study that was presented can be put to use or helpful in the organization’s management, or project managers can utilize it as a framework and direction for hybrid cloud ERP implementation. The findings of the study can also be used to create a conceptual framework for the actual use of ERP systems for automobile industry, such as the incorporation of blockchain and postmodern ERP systems in many sectors of business. There is discussion of the findings’ implications for practical and research, and potential study areas are proposed.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1153-1194"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116314","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1954
Thanaporn Thitisawat, S. Kiattisin, Smitti Darakorn Na Ayuthaya
This research develops a location-based predictive model for distribution equipment failure for use in preventative maintenance scheduling and planning. This study focuses on equipment-related failures because they are one of the main causes of outages in Thailand. Geographic Information Systems (GIS) data was integrated with asset data to predict the equipment failure of distribution equipment. Data on assets and outages from the Provincial Electricity Authority (PEA) was merged with GIS data from multiple sources, including elevation data, weather data, natural landmarks, and points of interest (POIs). Data was split into four regional datasets, and Random Forests (RF) feature selection and structural equation modeling was used to identify and confirm the most important features in each region. Logistic regression and RF regression were then used to estimate failures. RF regression was more effective than logistic regression at estimating equipment failure. The asset age and electrical load were significant predictors of outages. There were also geographic features that were significant predictors in each region, but which features affected outages varied by region. Thus, the study concluded that the approach developed could be used in preventative maintenance planning with some modification for regional characteristics, including geographic location and patterns of urbanization and industrialization.
{"title":"Spatial Predictive Modeling of Power Outages Resulting from Distribution Equipment Failure: A Case of Thailand","authors":"Thanaporn Thitisawat, S. Kiattisin, Smitti Darakorn Na Ayuthaya","doi":"10.13052/jmm1550-4646.1954","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1954","url":null,"abstract":"This research develops a location-based predictive model for distribution equipment failure for use in preventative maintenance scheduling and planning. This study focuses on equipment-related failures because they are one of the main causes of outages in Thailand. Geographic Information Systems (GIS) data was integrated with asset data to predict the equipment failure of distribution equipment. Data on assets and outages from the Provincial Electricity Authority (PEA) was merged with GIS data from multiple sources, including elevation data, weather data, natural landmarks, and points of interest (POIs). Data was split into four regional datasets, and Random Forests (RF) feature selection and structural equation modeling was used to identify and confirm the most important features in each region. Logistic regression and RF regression were then used to estimate failures. RF regression was more effective than logistic regression at estimating equipment failure. The asset age and electrical load were significant predictors of outages. There were also geographic features that were significant predictors in each region, but which features affected outages varied by region. Thus, the study concluded that the approach developed could be used in preventative maintenance planning with some modification for regional characteristics, including geographic location and patterns of urbanization and industrialization.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1195-1220"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116328","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1951
Chayapol Kamyod
Embedded systems are increasingly being employed for a wide range of applications. Small and large enterprises can benefit from sensor networks and Internet of Things technologies. Farmers, particularly the next generation of farmers, are tremendously interested in the smart farm system. This is due to Thailand’s favorable geography, and young farmers are becoming more technologically literate. However, smart farm systems on the market are still too expensive and don’t meet small farms’ needs. Consequently, the study proposed a low-cost irrigation and fertilizer system for high-quality melon farms in Chiang Rai, Thailand. The system can properly carry out irrigation and fertilization operations on the melon farm, which require specific attention at various stages of production. As a result, the technique reduces human work while simultaneously supplying adequate water and nutrients to the entire plant. Moreover, farmers with Internet access can manually monitor or supervise the operation at any time, from any location, and on any device. The developed system may gather information from sensors and operational procedures for further analysis in order to increase output and reduce waste. The system is affordable since it was constructed primarily using open-source software and low-cost embedded components with an ergonomic architecture. The results of the comparison show that the automatic method outperforms the human approach in terms of quality and production yield.
{"title":"Smart Melon Farm System: Fertilizer IoT Solution","authors":"Chayapol Kamyod","doi":"10.13052/jmm1550-4646.1951","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1951","url":null,"abstract":"Embedded systems are increasingly being employed for a wide range of applications. Small and large enterprises can benefit from sensor networks and Internet of Things technologies. Farmers, particularly the next generation of farmers, are tremendously interested in the smart farm system. This is due to Thailand’s favorable geography, and young farmers are becoming more technologically literate. However, smart farm systems on the market are still too expensive and don’t meet small farms’ needs. Consequently, the study proposed a low-cost irrigation and fertilizer system for high-quality melon farms in Chiang Rai, Thailand. The system can properly carry out irrigation and fertilization operations on the melon farm, which require specific attention at various stages of production. As a result, the technique reduces human work while simultaneously supplying adequate water and nutrients to the entire plant. Moreover, farmers with Internet access can manually monitor or supervise the operation at any time, from any location, and on any device. The developed system may gather information from sensors and operational procedures for further analysis in order to increase output and reduce waste. The system is affordable since it was constructed primarily using open-source software and low-cost embedded components with an ergonomic architecture. The results of the comparison show that the automatic method outperforms the human approach in terms of quality and production yield.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1107-1128"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116736","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1958
Selvam Ravindran, Velliangiri Sarveshwaran
With the growth of numerous technological areas, including sensors, embedded computing, broadband Internet access, wireless communications, distributed services, automatic identification, and tracking, the potential for integrating smart objects into our daily activities through the Internet has increased. The Internet of Things (IoT) is the confluence of the Internet and intelligent objects that can converse and cooperate with one another. IoT is a brand-new example that unifies Cyberspace with actual physical objects from various areas, including, business processes, human health, home automation, and environmental monitoring. It intensifies the use of Internet-connected strategies in our regular lives, carrying with it several advantages as well as security challenges. Intrusion Detection Systems (IDS) have been a crucial device for the defence of systems and material schemes for more than 20 years. However, applying traditional IDS techniques was challenging due to the IoT’s inimitable features, like resource-constrained devices and particular protocol stacks and standards. As a result, this survey will focus on various Deep Learning (DL)-based intrusion detection techniques. This study makes use of 50 research papers that focused on different techniques, and a review of studies that used those techniques was given. This research enables categorizing the methods employed for intrusion detection in IoT based on Convolutional Neural Network (CNN)-based methods, Deep Neural Network (DNN)-based methods, Optimization-based methods, and so on. Moreover, the categorization of approaches, published year, the dataset used, tools used, and the performance metrics are measured for intrusion detection in IoT. On the basis of the software used for implementation, performance achievement, and other factors, a thorough analysis was conducted. The conclusion identifies the research gaps and issues in a way that makes it clear why should create an efficient method for enabling efficient enhancement.
{"title":"Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities","authors":"Selvam Ravindran, Velliangiri Sarveshwaran","doi":"10.13052/jmm1550-4646.1958","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1958","url":null,"abstract":"With the growth of numerous technological areas, including sensors, embedded computing, broadband Internet access, wireless communications, distributed services, automatic identification, and tracking, the potential for integrating smart objects into our daily activities through the Internet has increased. The Internet of Things (IoT) is the confluence of the Internet and intelligent objects that can converse and cooperate with one another. IoT is a brand-new example that unifies Cyberspace with actual physical objects from various areas, including, business processes, human health, home automation, and environmental monitoring. It intensifies the use of Internet-connected strategies in our regular lives, carrying with it several advantages as well as security challenges. Intrusion Detection Systems (IDS) have been a crucial device for the defence of systems and material schemes for more than 20 years. However, applying traditional IDS techniques was challenging due to the IoT’s inimitable features, like resource-constrained devices and particular protocol stacks and standards. As a result, this survey will focus on various Deep Learning (DL)-based intrusion detection techniques. This study makes use of 50 research papers that focused on different techniques, and a review of studies that used those techniques was given. This research enables categorizing the methods employed for intrusion detection in IoT based on Convolutional Neural Network (CNN)-based methods, Deep Neural Network (DNN)-based methods, Optimization-based methods, and so on. Moreover, the categorization of approaches, published year, the dataset used, tools used, and the performance metrics are measured for intrusion detection in IoT. On the basis of the software used for implementation, performance achievement, and other factors, a thorough analysis was conducted. The conclusion identifies the research gaps and issues in a way that makes it clear why should create an efficient method for enabling efficient enhancement.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"19 1","pages":"1299-1330"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66116442","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 : 2023-08-14DOI: 10.13052/jmm1550-4646.1952
T. Yooyativong, Chayapol Kamyod
One-third of Thailand’s workers are in agriculture, but the country’s agricultural GDP is still less than 10% of its total GDP. Most Thai farmers are smallholders with limited land and low incomes. To improve the agricultural GDP and the economic situation of smallholder farmers, the Thai Government has been trying for decades to encourage and support smallholder farmers to adopt modern farming methods and smart farming equipment, including digital technologies. However, the improvement is still sluggish due to a lack of an effective approach to delivering essential digital knowledge and skills, as well as investment support for smart farming equipment. These have hindered smallholder farmers’ digital farming skill progress. To address this issue, the Broadcasting and Telecommunications Research and Development Fund for Public Interest has funded a project to develop the Digital Farmer Development Framework. This framework provides essential digital knowledge, training, coaching, and fundamental resources to upgrade smallholder digital-farming literacy to become digital farmers using problem- or project-based learning approaches and collaborative blended learning theories. Bloom’s taxonomy is used as a guideline for evaluating the framework’s effectiveness. Implementation of the Digital Farmer Development Framework has shown that farmers can significantly improve their digital farming literacy and are capable of using digital technology to improve farm management and productivity. Based on Bloom classification guidelines, 100% of the farms in the project can apply digital skills and utilize fundamental smart farming equipment as well as able to evaluate and analyze data from IoT devices. Moreover, 66% can create their own smart-system solution from fundamental smart farming tools for their farm. The project has also created a digital farmer community that shares knowledge and resources with others.
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