Pub Date : 2020-02-01DOI: 10.1109/AECT47998.2020.9194166
Muneeb Ullah, R. Ali, Abdullah, Mukhtiar Ahmad, T. Khan, Farhan Ul Mulk
Software Cost Estimation (SCE) is a hot area of research in the field of development of software projects. Precise estimation of the efforts put on development of software projects, in term of person-month (PM) and development time, is an essential earlier startup of projects. There are several software cost estimation techniques, such as algorithmic and non-algorithmic. This study presents a comparison among two algorithmic methods, namely Baily-Based model and Constructive Cost Models (COCOMO-II). The simulation is conducted on Turkish and Nasa datasets. From the excremental results, it is evident that COCOMO-II is better than the Bailey-Basili in term of Mean Magnitude of Relative Error (MMRE)
{"title":"Software Cost Estimation – A Comparative Study of COCOMO-II and Bailey-Basili Models","authors":"Muneeb Ullah, R. Ali, Abdullah, Mukhtiar Ahmad, T. Khan, Farhan Ul Mulk","doi":"10.1109/AECT47998.2020.9194166","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194166","url":null,"abstract":"Software Cost Estimation (SCE) is a hot area of research in the field of development of software projects. Precise estimation of the efforts put on development of software projects, in term of person-month (PM) and development time, is an essential earlier startup of projects. There are several software cost estimation techniques, such as algorithmic and non-algorithmic. This study presents a comparison among two algorithmic methods, namely Baily-Based model and Constructive Cost Models (COCOMO-II). The simulation is conducted on Turkish and Nasa datasets. From the excremental results, it is evident that COCOMO-II is better than the Bailey-Basili in term of Mean Magnitude of Relative Error (MMRE)","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114678338","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194207
Sadaf Jabeen, M. Awais, Basit Shafiq
A standard fuzzy rule relates variables with different linguistic labels where each linguistic label is defined through a membership function having membership value in a range from 0-1. The research presented in this paper extends this concept by associating each linguistic variable with intervals within the scope of its membership value to different classes. Thus, making a fuzzy rule more comprehensive and complete. The introduction of this concept has resulted in achieving better and at least comparable results with the standard fuzzy rule generation systems. The real task in implementing the proposed algorithm has been to determine these intervals. The present paper proposes the use of genetic algorithm with extended chromosome encoding to determine the interval of linguistic variables automatically. One of the main applications in which the proposed algorithm has been tested, is the forest inventory management and estimation. The forest inventory measurement includes vegetation cover, deforestation rate, crop degradation rate or vegetation index calculation. The key measurement in this regard is the amount of vegetation present. Generally, expensive equipment such as LIDAR and multispectral cameras are employed. With the use of the proposed approach vegetation estimation has been achieved using simple RGB cameras that are much cheaper. The proposed algorithm is not just limited to vegetation segmentation problem but is generic enough to be applied to datasets of different types and complexities. In order to establish this claim multiple datasets from UCI machine learning repository have been used to evaluate the proposed algorithm.
{"title":"A Generic Interval of Linguistic Variable based Genetic Fuzzy Inference System; A utility in Forestry Application","authors":"Sadaf Jabeen, M. Awais, Basit Shafiq","doi":"10.1109/AECT47998.2020.9194207","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194207","url":null,"abstract":"A standard fuzzy rule relates variables with different linguistic labels where each linguistic label is defined through a membership function having membership value in a range from 0-1. The research presented in this paper extends this concept by associating each linguistic variable with intervals within the scope of its membership value to different classes. Thus, making a fuzzy rule more comprehensive and complete. The introduction of this concept has resulted in achieving better and at least comparable results with the standard fuzzy rule generation systems. The real task in implementing the proposed algorithm has been to determine these intervals. The present paper proposes the use of genetic algorithm with extended chromosome encoding to determine the interval of linguistic variables automatically. One of the main applications in which the proposed algorithm has been tested, is the forest inventory management and estimation. The forest inventory measurement includes vegetation cover, deforestation rate, crop degradation rate or vegetation index calculation. The key measurement in this regard is the amount of vegetation present. Generally, expensive equipment such as LIDAR and multispectral cameras are employed. With the use of the proposed approach vegetation estimation has been achieved using simple RGB cameras that are much cheaper. The proposed algorithm is not just limited to vegetation segmentation problem but is generic enough to be applied to datasets of different types and complexities. In order to establish this claim multiple datasets from UCI machine learning repository have been used to evaluate the proposed algorithm.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132904192","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194175
Mohd Daniel Azraff Rozmi, D. R. A. Rambli, S. Sulaiman, N. Zamin, Nadia Diyana Mohd Muhaiyuddin, Foong Oi Mean
This paper presents the design and development of virtual reality-based nature therapy application as an alternative tool for stress relaxation. Forest therapy, a type nature therapy, supports healing of individuals through immersing oneself in the forest environments. Based on this concept, a simulation of a virtual reality forest therapy application is developed. According to the forest therapy, users will experience the therapeutic and relaxation effect of the forests when they immersed themselves in the forest atmosphere. For users to be fully immersed in the virtual forest environment and have similar experience to the actual forest therapy, design considerations in terms of the image realism, navigation methods and aids were discussed and highlighted. Essential nature elements such as types of forests, vegetation and natural habitat were suggested. The overall application design was presented in using a game concept. Positive results from users in a preliminary study indicate the potential of virtual reality as a tool in the field of therapy.
{"title":"Design Considerations for a Virtual Reality-Based Nature Therapy to Release Stress","authors":"Mohd Daniel Azraff Rozmi, D. R. A. Rambli, S. Sulaiman, N. Zamin, Nadia Diyana Mohd Muhaiyuddin, Foong Oi Mean","doi":"10.1109/AECT47998.2020.9194175","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194175","url":null,"abstract":"This paper presents the design and development of virtual reality-based nature therapy application as an alternative tool for stress relaxation. Forest therapy, a type nature therapy, supports healing of individuals through immersing oneself in the forest environments. Based on this concept, a simulation of a virtual reality forest therapy application is developed. According to the forest therapy, users will experience the therapeutic and relaxation effect of the forests when they immersed themselves in the forest atmosphere. For users to be fully immersed in the virtual forest environment and have similar experience to the actual forest therapy, design considerations in terms of the image realism, navigation methods and aids were discussed and highlighted. Essential nature elements such as types of forests, vegetation and natural habitat were suggested. The overall application design was presented in using a game concept. Positive results from users in a preliminary study indicate the potential of virtual reality as a tool in the field of therapy.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834562","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194183
Roobaea Alroobaea
Our goal is to propose a suitable approach for validating blockchains. For this purpose, we intend to adopt formal methods which are based on strong mathematical foundations. More precisely, we follow a model-based testing approach. The latter consists in describing the behavior of the system using a specific formalism, deriving test cases from the obtained model and then executing the obtained tests on the implementation to check whether it is correct or not. The adopted formalism corresponds to the timed automaton Model. The generated tests may be either digital or analog. Moreover, we propose several techniques which allow to solve the state explosion which may be encountered during the verification and test generation phases.
{"title":"A Formal Approach To Validate Block-Chains","authors":"Roobaea Alroobaea","doi":"10.1109/AECT47998.2020.9194183","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194183","url":null,"abstract":"Our goal is to propose a suitable approach for validating blockchains. For this purpose, we intend to adopt formal methods which are based on strong mathematical foundations. More precisely, we follow a model-based testing approach. The latter consists in describing the behavior of the system using a specific formalism, deriving test cases from the obtained model and then executing the obtained tests on the implementation to check whether it is correct or not. The adopted formalism corresponds to the timed automaton Model. The generated tests may be either digital or analog. Moreover, we propose several techniques which allow to solve the state explosion which may be encountered during the verification and test generation phases.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452248","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194193
Shuaib Mohammed, F. Aliyu
Transmission Pipelines (TP) are large pipelines covering long distances that carry refined petroleum products for production sites to different states, countries, and continents. TPs are high-pressure pipelines, as such, they are subject to failure. Therefore, it is necessary to develop cost-effective techniques to monitor them with near real-time performance. In this paper, a Fog computing based leakage detection system is proposed. The system uses WSN to sense the pipeline while it uses the fog nodes to process and forward sensed data to the cloud. The proposed system, within the limit, is of our experiment reduces the average energy consumption of the sensor nodes in the network by a factor of 100, while the latency ranges from 0.025 s-120s depending on the satellite system used.
{"title":"Fog Computing for Leak Detection in On-shore Transmission Pipelines","authors":"Shuaib Mohammed, F. Aliyu","doi":"10.1109/AECT47998.2020.9194193","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194193","url":null,"abstract":"Transmission Pipelines (TP) are large pipelines covering long distances that carry refined petroleum products for production sites to different states, countries, and continents. TPs are high-pressure pipelines, as such, they are subject to failure. Therefore, it is necessary to develop cost-effective techniques to monitor them with near real-time performance. In this paper, a Fog computing based leakage detection system is proposed. The system uses WSN to sense the pipeline while it uses the fog nodes to process and forward sensed data to the cloud. The proposed system, within the limit, is of our experiment reduces the average energy consumption of the sensor nodes in the network by a factor of 100, while the latency ranges from 0.025 s-120s depending on the satellite system used.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414518","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194163
Faed Ahmed Arnob, A. Fuad, Abu Tahir Nizam, Shuvajit Barua, Ahnaf Atef Choudhury, Motaharul Islam
In recent years, there have been rise in the number of problems in the existing traffic management system particularly in the developing countries. Due to this, many agonizing accidents are occurring every now and then. Over speeding and violating the traffic rules such as unnecessary change of lanes are the two main reasons for the rise in the number of accidents. This problem needs to be solved immediately to reduce the number of unexpected deaths. In this paper, an attempt is made to solve the problem addressed using Raspberry-pi and OpenCV contour detection technology. We have developed a prototype device to solve this problem. The device will be installed in traffic surveillance camera near the traffic signal position, which will be connected to the metropolitan traffic servers. If any of the vehicle crosses the device violating the mentioned traffic rule, they will be detected and the data will be sent to the server immediately. Thus, the proposed traffic monitoring system will help to reduce the manual collection of data, resulting less time wastage and this will further reduce the cost. Furthermore, it will help to find the person responsible for traffic rule violation and will assist the traffic management department to apply the laws strictly. The proposed model has about 78.83% accuracy, which will help to reduce the number of accidents that are taking place every day.
{"title":"An Intelligent Traffic System for Detecting Lane Based Rule Violation","authors":"Faed Ahmed Arnob, A. Fuad, Abu Tahir Nizam, Shuvajit Barua, Ahnaf Atef Choudhury, Motaharul Islam","doi":"10.1109/AECT47998.2020.9194163","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194163","url":null,"abstract":"In recent years, there have been rise in the number of problems in the existing traffic management system particularly in the developing countries. Due to this, many agonizing accidents are occurring every now and then. Over speeding and violating the traffic rules such as unnecessary change of lanes are the two main reasons for the rise in the number of accidents. This problem needs to be solved immediately to reduce the number of unexpected deaths. In this paper, an attempt is made to solve the problem addressed using Raspberry-pi and OpenCV contour detection technology. We have developed a prototype device to solve this problem. The device will be installed in traffic surveillance camera near the traffic signal position, which will be connected to the metropolitan traffic servers. If any of the vehicle crosses the device violating the mentioned traffic rule, they will be detected and the data will be sent to the server immediately. Thus, the proposed traffic monitoring system will help to reduce the manual collection of data, resulting less time wastage and this will further reduce the cost. Furthermore, it will help to find the person responsible for traffic rule violation and will assist the traffic management department to apply the laws strictly. The proposed model has about 78.83% accuracy, which will help to reduce the number of accidents that are taking place every day.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131514068","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194156
A. Haque, Tariq Mahmood, S. Ghani
Medical Science and Healthcare has made significant developments for the provision of better and effective cures of diseases to people. Specially the engagement of body worn devices generating electronic health record (EHR) has made patient’s condition analysis very convenient for consultants in realtime. Currently the usefulness of these EHR are subjective to understand the current situation of patient and apply treatment against that. However this massive amount of data can further be used for predictive and forecasted analytics which will allow before hand cure and patient condition information to medical institutions. Generally the EHR contains time components and can be used for time series analysis. Since the generation of EHR is high in velocity and volume so simple time series will not yield effective and accurate results. For the purpose we have used Residual Recurrent Neural Network (R2N2) instead of simple time series analysis in our research work for forecasting patient’s cardiac behavior. The novelty in our model is that our R2N2 is a composition of VARMAX and LSTM. The model works on an extrapolative approach and uses last result as an input for next value forecast with an accuracy of 92.7%. We compare our result and outcome with all possible related work and found that the accuracy of forecast is higher than others and the response is in near realtime which is the requirement of medical institution. Our work can be used for medical institutions and healthcare sectors under surveillance as a support to consultants for their practice on patients.
{"title":"A R2N2 Approach For Cardiac Behavior Forecast on Non-Trending Big HealthCare Data","authors":"A. Haque, Tariq Mahmood, S. Ghani","doi":"10.1109/AECT47998.2020.9194156","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194156","url":null,"abstract":"Medical Science and Healthcare has made significant developments for the provision of better and effective cures of diseases to people. Specially the engagement of body worn devices generating electronic health record (EHR) has made patient’s condition analysis very convenient for consultants in realtime. Currently the usefulness of these EHR are subjective to understand the current situation of patient and apply treatment against that. However this massive amount of data can further be used for predictive and forecasted analytics which will allow before hand cure and patient condition information to medical institutions. Generally the EHR contains time components and can be used for time series analysis. Since the generation of EHR is high in velocity and volume so simple time series will not yield effective and accurate results. For the purpose we have used Residual Recurrent Neural Network (R2N2) instead of simple time series analysis in our research work for forecasting patient’s cardiac behavior. The novelty in our model is that our R2N2 is a composition of VARMAX and LSTM. The model works on an extrapolative approach and uses last result as an input for next value forecast with an accuracy of 92.7%. We compare our result and outcome with all possible related work and found that the accuracy of forecast is higher than others and the response is in near realtime which is the requirement of medical institution. Our work can be used for medical institutions and healthcare sectors under surveillance as a support to consultants for their practice on patients.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122918256","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194210
Zahid Shah, Aftab Khan, Ali Khan
The research focuses on the detection of mobile phones that appear in the passenger baggage at airport arrivals. The aim of the research is to develop a method that detects mobile phones efficiently in passenger baggage at the custom scanner for the purpose to make sure that no mobile phone is passed undetected without payment of duties and taxes. It presents a machine learning based solution towards the airport security system by detecting mobile phones in a scanned image of passenger’s baggage at airport arrival. Classification is based on colour, density, size and pattern. It is challenging to ascertain if an electronic item is a cell phone or not from an x-ray image particularly when two objects are overlapping each other. The system’s performance is marred by the unavailability of high-quality x-ray images. The performance of the system increases manifolds when a high-quality image is provided as a test case. The system is able to classify the images correctly 80 percent of the time on average. The research project is of significant importance to the customs authorities as it helps them in profiling the passenger baggage at the arrival for imported mobile phones.
{"title":"Automatic Cell Phone Detection in Large Volume of Baggage Processing","authors":"Zahid Shah, Aftab Khan, Ali Khan","doi":"10.1109/AECT47998.2020.9194210","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194210","url":null,"abstract":"The research focuses on the detection of mobile phones that appear in the passenger baggage at airport arrivals. The aim of the research is to develop a method that detects mobile phones efficiently in passenger baggage at the custom scanner for the purpose to make sure that no mobile phone is passed undetected without payment of duties and taxes. It presents a machine learning based solution towards the airport security system by detecting mobile phones in a scanned image of passenger’s baggage at airport arrival. Classification is based on colour, density, size and pattern. It is challenging to ascertain if an electronic item is a cell phone or not from an x-ray image particularly when two objects are overlapping each other. The system’s performance is marred by the unavailability of high-quality x-ray images. The performance of the system increases manifolds when a high-quality image is provided as a test case. The system is able to classify the images correctly 80 percent of the time on average. The research project is of significant importance to the customs authorities as it helps them in profiling the passenger baggage at the arrival for imported mobile phones.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121958391","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194153
Khursheed Aurangzeb, Musaed A. Alhussein
Due to the seamless benefits of the integration of Distributed Energy Resources (DERs) for the residential customers, the forecasting of the short term power load of individual household energy customer is becoming an essential task for the future operation and planning of the smart grids. Recently, different studies concluded that due to lack of fast connectivity and awareness, the energy customer were not able to exploit the benefits of the DERs to the full extent. Nevertheless, with the rapid advancement in connectivity, data analytics, internet of things, artificial intelligence and machine/deep learning, the prospective benefits of the DERs can fully be explored. But both the short term power load of the individual energy customer and the power generated through DERs is dependent on the weather conditions and seasonality. In this paper, our focus is on forecasting the short term power load of the end energy customer using a deep learning framework. The proposed deep learning framework is based on a pyramid architecture of convolutional neural network. We developed and trained/evaluated the model for forecasting the short term power load of the individual household customer based on a large database of energy data from Australia. Our analysis indicates that forecasting the individual household power load is highly unpredictable. More than 57% of the customers (40 out 0f 69) have more than twenty outliers in the daily energy consumptions (which means highly unpredictable power load). The results show that our pyramid-CNN based deep learning approach is successful in predicting the individual household power consumption.
{"title":"Deep learning framework for short term power load forecasting, a case study of individual household energy customer","authors":"Khursheed Aurangzeb, Musaed A. Alhussein","doi":"10.1109/AECT47998.2020.9194153","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194153","url":null,"abstract":"Due to the seamless benefits of the integration of Distributed Energy Resources (DERs) for the residential customers, the forecasting of the short term power load of individual household energy customer is becoming an essential task for the future operation and planning of the smart grids. Recently, different studies concluded that due to lack of fast connectivity and awareness, the energy customer were not able to exploit the benefits of the DERs to the full extent. Nevertheless, with the rapid advancement in connectivity, data analytics, internet of things, artificial intelligence and machine/deep learning, the prospective benefits of the DERs can fully be explored. But both the short term power load of the individual energy customer and the power generated through DERs is dependent on the weather conditions and seasonality. In this paper, our focus is on forecasting the short term power load of the end energy customer using a deep learning framework. The proposed deep learning framework is based on a pyramid architecture of convolutional neural network. We developed and trained/evaluated the model for forecasting the short term power load of the individual household customer based on a large database of energy data from Australia. Our analysis indicates that forecasting the individual household power load is highly unpredictable. More than 57% of the customers (40 out 0f 69) have more than twenty outliers in the daily energy consumptions (which means highly unpredictable power load). The results show that our pyramid-CNN based deep learning approach is successful in predicting the individual household power consumption.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123262385","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194223
M. Javed, Shahzaib Tahir
Cellular networking has entered the paradigm of next generation networking, Software Defined Mobile Networking (SDMN) and Cloud Radio Access Networks (CRAN). This paradigm shift has compelled to improve future cellular networks by making them more efficient and smart. The improved, future cellular networks could have a profound impact on the commercial and research segments in terms of capital expenditure and operational expenditure by presenting a unified robust technology for data connectivity. In future, 5G cellular networks will be progressed with LTE that will facilitate this technological transformation. Future 5G demands reliance on more flexible and dynamic technologies such as static Radio Access Networks (RAN), otherwise resources will be depleting and will be unable to meet the surging demands including wireless transmission connectivity and providing an efficient network bandwidth. To address this problem, the paper presents a study exploring the amalgamation of Software Defined Network (SDN) and cloud computing achieve Cloud RAN or RAN-as-a-Service (RANaaS). This helps to overcome the problems associated with handling enormous matrix of connecting devices and nodes. This paper explores existing research geared towards the deployment of 5G networks through Remote Radio Heads (RRH) and Virtual Base Stations (VBSs) backed by SDN. Furthermore, this research survey is an effort to unearth the security implications and challenges of CRAN while adopting LTE and SDMN services in CRAN based 5G networks. The security challenges of conventional wireless networks are framed to emphasize on the need to migrate from traditional RAN to CRAN. Subsequently the possible mitigation techniques are also discussed. Furthermore, the role of SDN and its security artifacts are also explored to embed security within the CRAN architecture.
{"title":"Roadmap for Security-as-a-Service CRAN in 5G Networks","authors":"M. Javed, Shahzaib Tahir","doi":"10.1109/AECT47998.2020.9194223","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194223","url":null,"abstract":"Cellular networking has entered the paradigm of next generation networking, Software Defined Mobile Networking (SDMN) and Cloud Radio Access Networks (CRAN). This paradigm shift has compelled to improve future cellular networks by making them more efficient and smart. The improved, future cellular networks could have a profound impact on the commercial and research segments in terms of capital expenditure and operational expenditure by presenting a unified robust technology for data connectivity. In future, 5G cellular networks will be progressed with LTE that will facilitate this technological transformation. Future 5G demands reliance on more flexible and dynamic technologies such as static Radio Access Networks (RAN), otherwise resources will be depleting and will be unable to meet the surging demands including wireless transmission connectivity and providing an efficient network bandwidth. To address this problem, the paper presents a study exploring the amalgamation of Software Defined Network (SDN) and cloud computing achieve Cloud RAN or RAN-as-a-Service (RANaaS). This helps to overcome the problems associated with handling enormous matrix of connecting devices and nodes. This paper explores existing research geared towards the deployment of 5G networks through Remote Radio Heads (RRH) and Virtual Base Stations (VBSs) backed by SDN. Furthermore, this research survey is an effort to unearth the security implications and challenges of CRAN while adopting LTE and SDMN services in CRAN based 5G networks. The security challenges of conventional wireless networks are framed to emphasize on the need to migrate from traditional RAN to CRAN. Subsequently the possible mitigation techniques are also discussed. Furthermore, the role of SDN and its security artifacts are also explored to embed security within the CRAN architecture.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121097236","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：