Pub Date : 2022-06-08DOI: 10.4108/eetinis.v9i31.1059
Lal Verda Çakır, Khayal Huseynov, Elif Ak, B. Canberk
Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process is required. But this brings overhead concerns that decrease the throughput. Moreover, mitigation of interference relies on the selection of transmit powers. In other words, the control scheme should select the optimum configuration relative to other possibilities based on the total interference, and this requires an extensive search. Furthermore, bidirectional communication in real-time needs to exist to control the transmit powers based on the current situation. Based on these challenges, we propose a complete solution with Digital Twin WiFi Networks (DTWN). Contrarily to other studies, with the agent programs installed on the APs in the physical layer of this architecture, we enable information-gathering without causing overhead to the wireless medium. Additionally, we employ Q-learning-based TPC in the Brain Layer to find the best configuration given the current situation. Consequently, we accomplish real-time monitoring and management thanks to the digital twin. Then, we evaluate the performance of the proposed approach through total interference and throughput metrics over the increasing number of users. Furthermore, we show that the proposed DTWN model outperforms existing schemes.
{"title":"DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks","authors":"Lal Verda Çakır, Khayal Huseynov, Elif Ak, B. Canberk","doi":"10.4108/eetinis.v9i31.1059","DOIUrl":"https://doi.org/10.4108/eetinis.v9i31.1059","url":null,"abstract":"Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process is required. But this brings overhead concerns that decrease the throughput. Moreover, mitigation of interference relies on the selection of transmit powers. In other words, the control scheme should select the optimum configuration relative to other possibilities based on the total interference, and this requires an extensive search. Furthermore, bidirectional communication in real-time needs to exist to control the transmit powers based on the current situation. Based on these challenges, we propose a complete solution with Digital Twin WiFi Networks (DTWN). Contrarily to other studies, with the agent programs installed on the APs in the physical layer of this architecture, we enable information-gathering without causing overhead to the wireless medium. Additionally, we employ Q-learning-based TPC in the Brain Layer to find the best configuration given the current situation. Consequently, we accomplish real-time monitoring and management thanks to the digital twin. Then, we evaluate the performance of the proposed approach through total interference and throughput metrics over the increasing number of users. Furthermore, we show that the proposed DTWN model outperforms existing schemes.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74309006","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 : 2022-05-25DOI: 10.4108/eetinis.v9i31.1181
Ľ. Slušná, M. Balog
INTRODUCTION: Industry 4.0 is a concept covering various research areas. Their development depends on the cooperation among several stakeholders, particularly public R&D (Research and Development) organisations.OBJECTIVES: This article aims to provide a mapping of informal strategic R&D partnerships of public R&D organisations in an ambiguously defined area of Industry 4.0.METHODS: Scientific collaboration mapping method based on self-identification is used. Moreover, social network analysis is used to discuss patterns and specific characteristics of this network. Empirical data are gathered through a questionnaire survey focused on managers of RD teams in the Slovak Republic.RESULTS: The resulting network of public R&D organisations operating in the field of Industry 4.0 in the Slovak Republic is connected, though characterised by low density. Intra-regional cooperation prevailed only in the region of the capital city. In other regions, cross-regional cooperation was dominant. Most cooperations occur between universities; cooperation between faculties and within one faculty is less frequent. Key teams of the network were identified based on their performance in three selected indicators of centrality. Three of them represented the first layer or core of the network.CONCLUSION: Within the network, active actors with a high number of cooperation and those located in its network centre who can support knowledge transfer across the identified R&D network are crucial. Our results confirmed that several variables are important to creating new collaborations and thus not limited to geographical proximity, institutional affinity and size of the workplace.
{"title":"Identification of key actors in Industry 4.0 informal R&D network","authors":"Ľ. Slušná, M. Balog","doi":"10.4108/eetinis.v9i31.1181","DOIUrl":"https://doi.org/10.4108/eetinis.v9i31.1181","url":null,"abstract":"INTRODUCTION: Industry 4.0 is a concept covering various research areas. Their development depends on the cooperation among several stakeholders, particularly public R&D (Research and Development) organisations.OBJECTIVES: This article aims to provide a mapping of informal strategic R&D partnerships of public R&D organisations in an ambiguously defined area of Industry 4.0.METHODS: Scientific collaboration mapping method based on self-identification is used. Moreover, social network analysis is used to discuss patterns and specific characteristics of this network. Empirical data are gathered through a questionnaire survey focused on managers of RD teams in the Slovak Republic.RESULTS: The resulting network of public R&D organisations operating in the field of Industry 4.0 in the Slovak Republic is connected, though characterised by low density. Intra-regional cooperation prevailed only in the region of the capital city. In other regions, cross-regional cooperation was dominant. Most cooperations occur between universities; cooperation between faculties and within one faculty is less frequent. Key teams of the network were identified based on their performance in three selected indicators of centrality. Three of them represented the first layer or core of the network.CONCLUSION: Within the network, active actors with a high number of cooperation and those located in its network centre who can support knowledge transfer across the identified R&D network are crucial. Our results confirmed that several variables are important to creating new collaborations and thus not limited to geographical proximity, institutional affinity and size of the workplace.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74238336","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 : 2022-05-16DOI: 10.4108/eetinis.v9i31.985
Daniel Suarez-Mash, A. Ghani, C. See, Simeon Keates, Hongnian Yu
To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate nonessential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively.
{"title":"Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles","authors":"Daniel Suarez-Mash, A. Ghani, C. See, Simeon Keates, Hongnian Yu","doi":"10.4108/eetinis.v9i31.985","DOIUrl":"https://doi.org/10.4108/eetinis.v9i31.985","url":null,"abstract":"To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate nonessential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86397260","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 : 2022-04-29DOI: 10.4108/eetinis.v9i31.707
Ngoc-Bich Le, T. Pham, Q. Phan, N. Debnath, Huan Ngoc Le
This paper presents a solution to automatically measure the T-shirt dimensions in the garment industry. To address this goal, the paper focuses on utilizing image processing to determine the T-shirt's dimensions. The processing algorithm was provided along with the proposed recognition regions novel approach that was expected to deliver faster processing speed and enhance accuracy. The feasibility was demonstrated by characterizing the accuracy and processing speed. Specifically, five distinctive dimensions were successfully identified and measured; with the replication of 30, the discrepancy varies from 0.095% (for chest) to 2.088% (for collar). The divergence is insignificant compared with the granted tolerances. Finally, the processing time and the mechanical structure of the system deliver productivity of 22 products/minute which is approximately 10 times more rapidly than manual measurement (25 seconds).
{"title":"Application of Computer Vision in T-Shirt Dimensions Measurement","authors":"Ngoc-Bich Le, T. Pham, Q. Phan, N. Debnath, Huan Ngoc Le","doi":"10.4108/eetinis.v9i31.707","DOIUrl":"https://doi.org/10.4108/eetinis.v9i31.707","url":null,"abstract":"This paper presents a solution to automatically measure the T-shirt dimensions in the garment industry. To address this goal, the paper focuses on utilizing image processing to determine the T-shirt's dimensions. The processing algorithm was provided along with the proposed recognition regions novel approach that was expected to deliver faster processing speed and enhance accuracy. The feasibility was demonstrated by characterizing the accuracy and processing speed. Specifically, five distinctive dimensions were successfully identified and measured; with the replication of 30, the discrepancy varies from 0.095% (for chest) to 2.088% (for collar). The divergence is insignificant compared with the granted tolerances. Finally, the processing time and the mechanical structure of the system deliver productivity of 22 products/minute which is approximately 10 times more rapidly than manual measurement (25 seconds).","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83846013","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 : 2022-04-21DOI: 10.4108/eetinis.v9i30.561
N. Dinh
This paper proposes a traffic-aware caching mechanism (TCM) for resource-constrained nodes in information-centric WSNs. TCM pushes up popular upstream content objects to be cached nearby the sink. Less popular content objects are cached farther from the sink compared to popular content objects. TCM also pushes down popular downstream content objects to be cached inside the network. The objective of TCM is to reduce the number of interest messages required forwarding in multiple hops inside WSNs to optimize stretch ratio, energy efficiency, and content retrieval latency. We implement TCM on the top of existing popularity-based caching schemes to improve their performance. Through analysis and experimental results, we show that TCM achieves a significant improvement in terms of energy efficiency, content retrieval latency, cache hit ratio, and stretch ratio compared to state-of-the-art popularity-based caching schemes.
{"title":"An Efficient Traffic-aware Caching Mechanism For Information-centric Wireless Sensor Networks","authors":"N. Dinh","doi":"10.4108/eetinis.v9i30.561","DOIUrl":"https://doi.org/10.4108/eetinis.v9i30.561","url":null,"abstract":"This paper proposes a traffic-aware caching mechanism (TCM) for resource-constrained nodes in information-centric WSNs. TCM pushes up popular upstream content objects to be cached nearby the sink. Less popular content objects are cached farther from the sink compared to popular content objects. TCM also pushes down popular downstream content objects to be cached inside the network. The objective of TCM is to reduce the number of interest messages required forwarding in multiple hops inside WSNs to optimize stretch ratio, energy efficiency, and content retrieval latency. We implement TCM on the top of existing popularity-based caching schemes to improve their performance. Through analysis and experimental results, we show that TCM achieves a significant improvement in terms of energy efficiency, content retrieval latency, cache hit ratio, and stretch ratio compared to state-of-the-art popularity-based caching schemes.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83097606","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 : 2022-04-14DOI: 10.4108/eetinis.v8i30.478
Truong-Minh Le, Tat-Bao-Thien Nguyen, V. M. Ngo
INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources.OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets.METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before.RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones.CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.
{"title":"Automated evaluation of Tuberculosis using Deep Neural Networks","authors":"Truong-Minh Le, Tat-Bao-Thien Nguyen, V. M. Ngo","doi":"10.4108/eetinis.v8i30.478","DOIUrl":"https://doi.org/10.4108/eetinis.v8i30.478","url":null,"abstract":"INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources.OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets.METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before.RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones.CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74477121","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 : 2022-04-05DOI: 10.4108/eai.5-4-2022.173783
D. Tran, Hans-Jürgen Zepernick, T. Chu
Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, o ff ers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the di ff erent LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-o ff s between data hiding attributes.
{"title":"LSB Data Hiding in Digital Media: A Survey","authors":"D. Tran, Hans-Jürgen Zepernick, T. Chu","doi":"10.4108/eai.5-4-2022.173783","DOIUrl":"https://doi.org/10.4108/eai.5-4-2022.173783","url":null,"abstract":"Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, o ff ers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the di ff erent LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-o ff s between data hiding attributes.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83718925","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 : 2022-03-09DOI: 10.4108/eai.9-3-2022.173606
T. Bilen, B. Canberk
The Base Station based Mobile-CDN architecture redirects the content request of mobile users to other base stations during storage misses. These request redirections increase the latency of a mobile client through unbalanced load distributions among base stations. To solve the unbalanced load distribution and latency problems, we propose to deliver the content from the sky by deploying drones as aerial content delivery points. This drone based deployment enables a more e ff ective and inexpensive solution without changing Mobile-CDN architecture. Here, we select di ff erent queuing theoretical models for drones and base stations due to the drones’ small capacity. With base station modeling, we can decide the loaded base stations to transfer the drones by utilizing the Barabasi-Albert Model. With drone modeling, we can obtain blocking probabilities with the Erlang-B parameter to determine additional drone transfer. According to simulations, the latency of mobile client originating requests are reduced by 25% compared to conventional Base Station based Mobile-CDN architecture.
{"title":"Content Delivery From the Sky: Drone-Aided Load Balancing for Mobile-CDN","authors":"T. Bilen, B. Canberk","doi":"10.4108/eai.9-3-2022.173606","DOIUrl":"https://doi.org/10.4108/eai.9-3-2022.173606","url":null,"abstract":"The Base Station based Mobile-CDN architecture redirects the content request of mobile users to other base stations during storage misses. These request redirections increase the latency of a mobile client through unbalanced load distributions among base stations. To solve the unbalanced load distribution and latency problems, we propose to deliver the content from the sky by deploying drones as aerial content delivery points. This drone based deployment enables a more e ff ective and inexpensive solution without changing Mobile-CDN architecture. Here, we select di ff erent queuing theoretical models for drones and base stations due to the drones’ small capacity. With base station modeling, we can decide the loaded base stations to transfer the drones by utilizing the Barabasi-Albert Model. With drone modeling, we can obtain blocking probabilities with the Erlang-B parameter to determine additional drone transfer. According to simulations, the latency of mobile client originating requests are reduced by 25% compared to conventional Base Station based Mobile-CDN architecture.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80417051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.4108/eai.3-11-2021.171755
L. Nguyen, Duy Nguyen, Richard Wells, N. Tran
We study the information outage probability (IOP) and constrained capacity of moderate-length codes over AWGN channels based on M-ary phase-shift keying signals. The IOP provides an important benchmark for performance evaluation of moderate-length codes. We analytically compute the IOP and compare it with numerical simulations using the DVB-S2 error-correcting code with numerous code rates employed in Protected Tactical Waveform (PTW). Numerical results confirm the tightness of the analytical results. Received on 03 October 2021; accepted on 01 November 2021; published on 03 November 2021
{"title":"Information Outage Probability and Constrained Capacity of Moderate-Length Codes over AWGN Channels","authors":"L. Nguyen, Duy Nguyen, Richard Wells, N. Tran","doi":"10.4108/eai.3-11-2021.171755","DOIUrl":"https://doi.org/10.4108/eai.3-11-2021.171755","url":null,"abstract":"We study the information outage probability (IOP) and constrained capacity of moderate-length codes over AWGN channels based on M-ary phase-shift keying signals. The IOP provides an important benchmark for performance evaluation of moderate-length codes. We analytically compute the IOP and compare it with numerical simulations using the DVB-S2 error-correcting code with numerous code rates employed in Protected Tactical Waveform (PTW). Numerical results confirm the tightness of the analytical results. Received on 03 October 2021; accepted on 01 November 2021; published on 03 November 2021","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83499396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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