Pub Date : 2019-08-01DOI: 10.1109/FiCloud.2019.00065
P. Balco, M. Drahosová, Juraj Zelenay, M. Greguš
The market with data and information is constantly increasing, changing requirements for functionality, technical parameters for storage or transmission capacities. Naturally, topics such as safety, quality, flexibility, productivity, or optimal costs are discussed. For this purpose, the market offers a wide range of intelligent or less progressive technologies and solutions. Because such solutions are very complex with a large number of variables. The users are looking for independent comparisons that can deliver the required answers and recommendations. We respond with our contribution to market requirements and try to answer the questions of the owners and users of the data. We present the results of testing intelligent solutions based on cloud technologies, their advantages and disadvantages. We then present a set of recommendations that are related to secure communication, collaboration and data storage. The outcomes of this contribution will find application in a wide range of institutions, but especially in SMEs or for individuals who are aware of the value of their data and the possible risks associated with their estrangement. These recommendations are platform independent and are for general use.
{"title":"Intelligent Solutions for Secure Communication and Collaboration Based on Cloud Technologies","authors":"P. Balco, M. Drahosová, Juraj Zelenay, M. Greguš","doi":"10.1109/FiCloud.2019.00065","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00065","url":null,"abstract":"The market with data and information is constantly increasing, changing requirements for functionality, technical parameters for storage or transmission capacities. Naturally, topics such as safety, quality, flexibility, productivity, or optimal costs are discussed. For this purpose, the market offers a wide range of intelligent or less progressive technologies and solutions. Because such solutions are very complex with a large number of variables. The users are looking for independent comparisons that can deliver the required answers and recommendations. We respond with our contribution to market requirements and try to answer the questions of the owners and users of the data. We present the results of testing intelligent solutions based on cloud technologies, their advantages and disadvantages. We then present a set of recommendations that are related to secure communication, collaboration and data storage. The outcomes of this contribution will find application in a wide range of institutions, but especially in SMEs or for individuals who are aware of the value of their data and the possible risks associated with their estrangement. These recommendations are platform independent and are for general use.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114494175","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00027
J. Casademont, A. C. Augé, David Quiñones, M. Navarro, J. Arribas, Miguel Catalan-Cid
Cooperative-Intelligent Transport Systems (C-ITS) are one of the flagships of new coming automotive and communication industries. Mobility needs to be safer and more efficient and C-ITS are the key for this new era. On the one hand, organizations like ETSI, IEEE, SAE, 3GPP or ISO are developing the standards for the new technologies and protocols and, on the other, manufacturers and operators are deploying and testing their first pilots. In this paper, we present a pilot developed by a group of stakeholders, in which vehicles will alert drivers of potential collisions with vulnerable road users riding bicycles. It is a multidisciplinary project where there are different architecture components: C-ITS stations integrated in vehicles, vehicles provided with digital cockpits that show warning messages to the driver, low-cost C-ITS stations attached to bicycles which are equipped with a high precision location system based on the fusion of different information sources as GPS, inertial sensors and Ultra Wide Band ranging and finally communication between C-ITS stations is provided by a network that supports low delay C-V2X communications with a Multi-access Edge Computing which takes routing decisions.
{"title":"Cooperative-Intelligent Transport Systems for Vulnerable Road Users Safety","authors":"J. Casademont, A. C. Augé, David Quiñones, M. Navarro, J. Arribas, Miguel Catalan-Cid","doi":"10.1109/FiCloud.2019.00027","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00027","url":null,"abstract":"Cooperative-Intelligent Transport Systems (C-ITS) are one of the flagships of new coming automotive and communication industries. Mobility needs to be safer and more efficient and C-ITS are the key for this new era. On the one hand, organizations like ETSI, IEEE, SAE, 3GPP or ISO are developing the standards for the new technologies and protocols and, on the other, manufacturers and operators are deploying and testing their first pilots. In this paper, we present a pilot developed by a group of stakeholders, in which vehicles will alert drivers of potential collisions with vulnerable road users riding bicycles. It is a multidisciplinary project where there are different architecture components: C-ITS stations integrated in vehicles, vehicles provided with digital cockpits that show warning messages to the driver, low-cost C-ITS stations attached to bicycles which are equipped with a high precision location system based on the fusion of different information sources as GPS, inertial sensors and Ultra Wide Band ranging and finally communication between C-ITS stations is provided by a network that supports low delay C-V2X communications with a Multi-access Edge Computing which takes routing decisions.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122130726","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00031
Nitirajsingh Sandu, E. Gide
The aim of this research is to develop a model for successful adoption of Cloud based services in Indian SMEs. This research paper investigates the key factors determining the adoption of Cloud-based services by Indian SMEs. Cloud computing has been a priority for consideration, especially with regard to IT-based organisational strategy management techniques. Small and medium-sized enterprises are considered the most critical aspect of economic growth in the developing world because they improve national competition. This research paper explains how developed model would help Indian SMEs to understand the importance of organisational and technological factors for Cloud-centric service adoption. By discerning the importance of Cloud-based services among business organisations, this study will also be contributing to the business community in India. It could also act as a basis for future researchers, decision-makers and others to improve their competitive advantages gained by using Cloud-based services.
{"title":"A Model for Successful Adoption of Cloud-Based Services in Indian SMEs","authors":"Nitirajsingh Sandu, E. Gide","doi":"10.1109/FiCloud.2019.00031","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00031","url":null,"abstract":"The aim of this research is to develop a model for successful adoption of Cloud based services in Indian SMEs. This research paper investigates the key factors determining the adoption of Cloud-based services by Indian SMEs. Cloud computing has been a priority for consideration, especially with regard to IT-based organisational strategy management techniques. Small and medium-sized enterprises are considered the most critical aspect of economic growth in the developing world because they improve national competition. This research paper explains how developed model would help Indian SMEs to understand the importance of organisational and technological factors for Cloud-centric service adoption. By discerning the importance of Cloud-based services among business organisations, this study will also be contributing to the business community in India. It could also act as a basis for future researchers, decision-makers and others to improve their competitive advantages gained by using Cloud-based services.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124060124","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00060
T. Serif, Osman Kerem Perente, Yusuf Dalan
With the ever-increasing numbers of mobile devices, location-based services became a crucial part of mobile development. Many indoor location detection systems are developed to solve positioning problem where satellite-based solutions prone to failure. Among many proposed solutions, fingerprinting technique proved to be the most reliable approach for indoor location. However, it comes with a cost; it entails a time-consuming learning phase which should be repeated many times during the system's life time to preserve system accuracy. Thus, we propose an automated signal mapping robot called RoboMapper to alleviate time-consuming nature of the learning phase of fingerprinting technique. With the help of its accurate distance keeping mechanisms, RoboMapper can construct the signal map of the environment so that the created map can be used for user positioning. Our findings indicate that using RoboMapper 2.68-meter positioning accuracy with 70% probability can be achieved.
{"title":"RoboMapper: An Automated Signal Mapping Robot for RSSI Fingerprinting","authors":"T. Serif, Osman Kerem Perente, Yusuf Dalan","doi":"10.1109/FiCloud.2019.00060","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00060","url":null,"abstract":"With the ever-increasing numbers of mobile devices, location-based services became a crucial part of mobile development. Many indoor location detection systems are developed to solve positioning problem where satellite-based solutions prone to failure. Among many proposed solutions, fingerprinting technique proved to be the most reliable approach for indoor location. However, it comes with a cost; it entails a time-consuming learning phase which should be repeated many times during the system's life time to preserve system accuracy. Thus, we propose an automated signal mapping robot called RoboMapper to alleviate time-consuming nature of the learning phase of fingerprinting technique. With the help of its accurate distance keeping mechanisms, RoboMapper can construct the signal map of the environment so that the created map can be used for user positioning. Our findings indicate that using RoboMapper 2.68-meter positioning accuracy with 70% probability can be achieved.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127135164","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00038
Eihab SaatiAlsoruji
Processing video data is becoming more useful in a wide range of applications. However, video data are demanding for computing resources, such as processor, memory, and disk. This is because the data size is huge in nature and growing exponentially. Change detection is a commonly used method in a variety of video processing applications, so it has been attracting the attention of many researchers. The goal of improving the speed of change detection could be to satisfy real-time performance or to process larger data in a timely manner. This study proposes an approach based on MapReduce and sampling to improve the performance of using change detection to process large video data on Hadoop clusters. The experiments, conducted on an outdoor scene dataset, show significant improvement in the execution time.
{"title":"A Change Detection Approach for Processing Outdoor Scenes on Hadoop Clusters","authors":"Eihab SaatiAlsoruji","doi":"10.1109/FiCloud.2019.00038","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00038","url":null,"abstract":"Processing video data is becoming more useful in a wide range of applications. However, video data are demanding for computing resources, such as processor, memory, and disk. This is because the data size is huge in nature and growing exponentially. Change detection is a commonly used method in a variety of video processing applications, so it has been attracting the attention of many researchers. The goal of improving the speed of change detection could be to satisfy real-time performance or to process larger data in a timely manner. This study proposes an approach based on MapReduce and sampling to improve the performance of using change detection to process large video data on Hadoop clusters. The experiments, conducted on an outdoor scene dataset, show significant improvement in the execution time.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131768800","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00034
Gaith Rjoub, J. Bentahar, O. A. Wahab, A. Bataineh
With the widespread adoption of Internet of Thing (IoT) and the exponential growth in the volumes of generated data, cloud providers tend to receive massive waves of demands on their storage and computing resources. To help providers deal with such demands without sacrificing performance, the concept of cloud automation had recently arisen to improve the performance and reduce the manual efforts related to the management of cloud computing workloads. In this context, we propose in this paper, Deep learning Smart Scheduling (DSS), an automated big data task scheduling approach in cloud computing environments. DSS combines Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) to automatically predict the Virtual Machines (VMs) to which each incoming big data task should be scheduled to so as to improve the performance of big data analytics and reduce their resource execution cost. Experiments conducted using real-world datasets from Google Cloud Platform show that our solution minimizes the CPU usage cost by 28.8% compared to the Shortest Job First (SJF), and by 14% compared to both the Round Robin (RR) and improved Particle Swarm Optimization (PSO) approaches. Moreover, our solution decreases the RAM memory usage cost by 31.25% compared to the SJF, by 25% compared to the RR, and by 18.78% compared to the improved PSO.
{"title":"Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud","authors":"Gaith Rjoub, J. Bentahar, O. A. Wahab, A. Bataineh","doi":"10.1109/FiCloud.2019.00034","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00034","url":null,"abstract":"With the widespread adoption of Internet of Thing (IoT) and the exponential growth in the volumes of generated data, cloud providers tend to receive massive waves of demands on their storage and computing resources. To help providers deal with such demands without sacrificing performance, the concept of cloud automation had recently arisen to improve the performance and reduce the manual efforts related to the management of cloud computing workloads. In this context, we propose in this paper, Deep learning Smart Scheduling (DSS), an automated big data task scheduling approach in cloud computing environments. DSS combines Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) to automatically predict the Virtual Machines (VMs) to which each incoming big data task should be scheduled to so as to improve the performance of big data analytics and reduce their resource execution cost. Experiments conducted using real-world datasets from Google Cloud Platform show that our solution minimizes the CPU usage cost by 28.8% compared to the Shortest Job First (SJF), and by 14% compared to both the Round Robin (RR) and improved Particle Swarm Optimization (PSO) approaches. Moreover, our solution decreases the RAM memory usage cost by 31.25% compared to the SJF, by 25% compared to the RR, and by 18.78% compared to the improved PSO.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117056727","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00015
Tayyebe Emadinia, Faraz Fatemi Moghaddam, P. Wieder, Shirin Dabbaghi Varnosfaderani, R. Yahyapour
Cloud computing is getting universally in production and hence, comprising more valuable information resources. Therefore, providing a secure infrastructure to exchange information is more vital and inevitable. Attackers are looking for a way to penetrate these systems and exploit various techniques, such as account hijack and denial of service attacks, to obtain the information or develop a disorder in the system. The creation of treat models helps to identify the vulnerabilities, prevent potential attacks, and consider appropriate mechanisms to mitigate them. One of these mechanisms is the access control list and the role-based access control list is one of its variants that defines some user groups and allocates resources to specific groups. Token-based authentication is another mechanism which helps to maintain security. When a user requests to access a resource, initially it must be authenticated by a username and represents a token. In this effort, by using the concept of role-based access control and JSON web token framework, a customized framework is proposed and implemented with a higher level of security in comparison to a standard JSON web token framework. Moreover, this proposal has the ability to update the status of access to resources in case of changing access policies by the policy engine.
{"title":"An Updateable Token-Based Schema for Authentication and Access Management in Clouds","authors":"Tayyebe Emadinia, Faraz Fatemi Moghaddam, P. Wieder, Shirin Dabbaghi Varnosfaderani, R. Yahyapour","doi":"10.1109/FiCloud.2019.00015","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00015","url":null,"abstract":"Cloud computing is getting universally in production and hence, comprising more valuable information resources. Therefore, providing a secure infrastructure to exchange information is more vital and inevitable. Attackers are looking for a way to penetrate these systems and exploit various techniques, such as account hijack and denial of service attacks, to obtain the information or develop a disorder in the system. The creation of treat models helps to identify the vulnerabilities, prevent potential attacks, and consider appropriate mechanisms to mitigate them. One of these mechanisms is the access control list and the role-based access control list is one of its variants that defines some user groups and allocates resources to specific groups. Token-based authentication is another mechanism which helps to maintain security. When a user requests to access a resource, initially it must be authenticated by a username and represents a token. In this effort, by using the concept of role-based access control and JSON web token framework, a customized framework is proposed and implemented with a higher level of security in comparison to a standard JSON web token framework. Moreover, this proposal has the ability to update the status of access to resources in case of changing access policies by the policy engine.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133394066","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00049
P. Lipnicki, D. Lewandowski, M. Syfert, Anna Sztyber, P. Wnuk
This article presents a description of the project and implementation of the system for the execution of on-line diagnostics of compressors using the methods of artificial intelligence and the Tensorflow library. The main tasks of the system are: on-line acquisition of process data from the compressor set, on-line state monitoring (fault detection) of the compressor set based on the analysis of process data and using the classifiers modelled using the Tensorflow library. The system is intended to be a proof of concept, it should show the possibility of using Tensorflow library models running on the Jetson platform for on-line monitoring of compressor faults. The sample models proposed and prepared during previous research and development projects were used for testing. The algorithms used to identify and detect failures are based on MLP, CNN, SVM and LSTM - keras.
{"title":"Inteligent IoTSP - Implementation of Embedded ML AI Tensorflow Algorithms on the NVIDIA Jetson Tx Chip","authors":"P. Lipnicki, D. Lewandowski, M. Syfert, Anna Sztyber, P. Wnuk","doi":"10.1109/FiCloud.2019.00049","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00049","url":null,"abstract":"This article presents a description of the project and implementation of the system for the execution of on-line diagnostics of compressors using the methods of artificial intelligence and the Tensorflow library. The main tasks of the system are: on-line acquisition of process data from the compressor set, on-line state monitoring (fault detection) of the compressor set based on the analysis of process data and using the classifiers modelled using the Tensorflow library. The system is intended to be a proof of concept, it should show the possibility of using Tensorflow library models running on the Jetson platform for on-line monitoring of compressor faults. The sample models proposed and prepared during previous research and development projects were used for testing. The algorithms used to identify and detect failures are based on MLP, CNN, SVM and LSTM - keras.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033921","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00036
Areeg Samir, C. Pahl
Container-based microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performance-related anomalies in container-based microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.
{"title":"DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models","authors":"Areeg Samir, C. Pahl","doi":"10.1109/FiCloud.2019.00036","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00036","url":null,"abstract":"Container-based microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performance-related anomalies in container-based microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117182874","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 : 2019-08-01DOI: 10.1109/FiCloud.2019.00017
Ayoub Bouheroum, Zakaria Benzadri, F. Belala
Fog has the advantage of reducing service latency and improving perceived quality, as well as the benefit of total data distribution. However, its security or privacy issues pose major challenges due to the heterogeneity, hierarchical structure, and very large scale infrastructure of Fog architecture. CA-BRS model, an extension of Bigraphical Reactive Systems with Control Agents, is showed to be an appropriate formal semantic framework for Fog architecture specification. It separates, for a complex system, the concerns of computation and physical entities distribution, respectively into two different models: 1) computational or virtual agents' model and 2) physical Bigraph model. The main objective of this paper is twofold; first, we propose a multi-views architecture to Fog computing taking into account security level, then we define, according to this semantic framework, the physical entities of Fog architecture and their geographical dispersion, as well as the virtual entities devoted to represent security functionalities. Our proposal is illustrated through a realistic case study of Oil and Gas Refinery Plant.
{"title":"Towards a Formal Approach Based on Bigraphs for Fog Security: Case of Oil and Gas Refinery Plant","authors":"Ayoub Bouheroum, Zakaria Benzadri, F. Belala","doi":"10.1109/FiCloud.2019.00017","DOIUrl":"https://doi.org/10.1109/FiCloud.2019.00017","url":null,"abstract":"Fog has the advantage of reducing service latency and improving perceived quality, as well as the benefit of total data distribution. However, its security or privacy issues pose major challenges due to the heterogeneity, hierarchical structure, and very large scale infrastructure of Fog architecture. CA-BRS model, an extension of Bigraphical Reactive Systems with Control Agents, is showed to be an appropriate formal semantic framework for Fog architecture specification. It separates, for a complex system, the concerns of computation and physical entities distribution, respectively into two different models: 1) computational or virtual agents' model and 2) physical Bigraph model. The main objective of this paper is twofold; first, we propose a multi-views architecture to Fog computing taking into account security level, then we define, according to this semantic framework, the physical entities of Fog architecture and their geographical dispersion, as well as the virtual entities devoted to represent security functionalities. Our proposal is illustrated through a realistic case study of Oil and Gas Refinery Plant.","PeriodicalId":268882,"journal":{"name":"2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125922346","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：