Pub Date : 2020-04-01DOI: 10.1109/fmec49853.2020.9144801
Hiroyuki Sato, A. Grieco
{"title":"The Fifth International Conference on Fog and Mobile Edge Computing (FMEC) Committee","authors":"Hiroyuki Sato, A. Grieco","doi":"10.1109/fmec49853.2020.9144801","DOIUrl":"https://doi.org/10.1109/fmec49853.2020.9144801","url":null,"abstract":"","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121897209","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 : 2020-04-01DOI: 10.1109/fmec49853.2020.9144793
M. Alsmirat, Y. Jararweh, E. Benkhelifa, Imed Saleh, Hiroyuki Sato, L. Boubchir
{"title":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","authors":"M. Alsmirat, Y. Jararweh, E. Benkhelifa, Imed Saleh, Hiroyuki Sato, L. Boubchir","doi":"10.1109/fmec49853.2020.9144793","DOIUrl":"https://doi.org/10.1109/fmec49853.2020.9144793","url":null,"abstract":"","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127711630","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144887
O. Flauzac, J. Hérard, F. Nolot
Many organizations plan to collect remote data to perform incidents prediction, or just to store or analyze them. To gather some data, it is necessary to set up a sensor architecture. In this paper, we address several issues we have to consider when we design a dynamic wireless sensor network architecture aiming to collect and gather distributed data. We propose an original solution to the possible lack of an operator infrastructure covering the sensor deployment area. This solution also takes into account the communication capabilities of the different sensors, as well as the energy constraint. The dynamic management protocol we propose to combine a local synchronization solution, and a relay protocol. The efficiency of our solution, which can be applied to any wireless protocol, is shown by the results of a simulation campaign.
{"title":"Synchronization Solution to Optimize Power Consumption in Linear Sensor Network","authors":"O. Flauzac, J. Hérard, F. Nolot","doi":"10.1109/FMEC49853.2020.9144887","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144887","url":null,"abstract":"Many organizations plan to collect remote data to perform incidents prediction, or just to store or analyze them. To gather some data, it is necessary to set up a sensor architecture. In this paper, we address several issues we have to consider when we design a dynamic wireless sensor network architecture aiming to collect and gather distributed data. We propose an original solution to the possible lack of an operator infrastructure covering the sensor deployment area. This solution also takes into account the communication capabilities of the different sensors, as well as the energy constraint. The dynamic management protocol we propose to combine a local synchronization solution, and a relay protocol. The efficiency of our solution, which can be applied to any wireless protocol, is shown by the results of a simulation campaign.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130022869","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144814
Hamza Baniata, W. Almobaideen, A. Kertész
Privacy issues in Cloud Computing are one of the major concerns for many individuals and companies around the world, and still a challenge for research and industry. The appearance of the 5G technology and the latest advances in Mobile Cloud Computing gave way to fog-enabled systems that represent the future of current cloud systems. Accordingly, the Fog concept had been proposed in 2013 to enhance the IoT-Cloud operations in terms of latency and reliability. In this paper we analyze and categorize surveys addressing privacy issues of these complex systems, and propose PFMCC, a model for preserving Privacy in Fog-enabled Mobile Cloud Computing systems using 5G connection. The PFMCC model consists of three components interacting with each other to preserve data privacy, usage privacy, location privacy and high mobility. We also present three algorithms to perform privacy-aware computation offloading to the fog.
{"title":"A Privacy Preserving Model for Fog-enabled MCC systems using 5G Connection","authors":"Hamza Baniata, W. Almobaideen, A. Kertész","doi":"10.1109/FMEC49853.2020.9144814","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144814","url":null,"abstract":"Privacy issues in Cloud Computing are one of the major concerns for many individuals and companies around the world, and still a challenge for research and industry. The appearance of the 5G technology and the latest advances in Mobile Cloud Computing gave way to fog-enabled systems that represent the future of current cloud systems. Accordingly, the Fog concept had been proposed in 2013 to enhance the IoT-Cloud operations in terms of latency and reliability. In this paper we analyze and categorize surveys addressing privacy issues of these complex systems, and propose PFMCC, a model for preserving Privacy in Fog-enabled Mobile Cloud Computing systems using 5G connection. The PFMCC model consists of three components interacting with each other to preserve data privacy, usage privacy, location privacy and high mobility. We also present three algorithms to perform privacy-aware computation offloading to the fog.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126429332","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144808
Khalid A. Darabkh, Jumana N. Zomot, Z. Al-qudah, A. Khalifeh
In this paper, we propose an Improved Energy and Distance Based Cluster Head Selection with Balanced Objective Function (IEDB-CHS-BOF) protocol for wireless sensor networks that addresses the main drawback of the EDB-CHS-BOF protocol. The primary objective of the IEDB-CHS-BOF protocol is to reduce the amount of re-clustering and accelerate the clustering process. To achieve this objective, the IEDB-CHS-BOF protocol affirms that cluster heads do not have to overturn each round but more preferably each batch of rounds. Strictly speaking, sensor nodes stay serving as cluster heads as long as their remaining energy levels are higher than a predefined threshold energy. The proposed protocol not only diminishes the control overhead that is needed for setting up the clusters, but also is helpful to extend the lifetime of the entire network. MATLAB simulation results show superior performance of the IEDB-CHS-BOF protocol over existing protocols in terms of the network lifetime.
{"title":"IEDB-CHS-BOF: Improved Energy and Distance Based CH Selection with Balanced Objective Function for Wireless Sensor Networks","authors":"Khalid A. Darabkh, Jumana N. Zomot, Z. Al-qudah, A. Khalifeh","doi":"10.1109/FMEC49853.2020.9144808","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144808","url":null,"abstract":"In this paper, we propose an Improved Energy and Distance Based Cluster Head Selection with Balanced Objective Function (IEDB-CHS-BOF) protocol for wireless sensor networks that addresses the main drawback of the EDB-CHS-BOF protocol. The primary objective of the IEDB-CHS-BOF protocol is to reduce the amount of re-clustering and accelerate the clustering process. To achieve this objective, the IEDB-CHS-BOF protocol affirms that cluster heads do not have to overturn each round but more preferably each batch of rounds. Strictly speaking, sensor nodes stay serving as cluster heads as long as their remaining energy levels are higher than a predefined threshold energy. The proposed protocol not only diminishes the control overhead that is needed for setting up the clusters, but also is helpful to extend the lifetime of the entire network. MATLAB simulation results show superior performance of the IEDB-CHS-BOF protocol over existing protocols in terms of the network lifetime.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756572","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144882
I. Satoh
The notion of edge computing, including fog computing, is to shift tasks to process data generated from sensing systems from the server-side to the network edge. Data directly measured by sensing systems tend to contain noise and loss and be in a non-canonical representation. Pre-processing for such data is often needed to reduce noise and to translate the data to a canonical representation. MapReduce processing, which was originally designed to be executed on a cluster of high-performance servers, is also useful for pre-processing data generated at the edge of a network. To support edge computing, we previously developed an approach to enable processing in embedded computers connected through wired or wireless local area networks in a peer-to-peer manner. The purpose of this paper is to extend our existing approach to give it the ability to work 5G networks. The extended approach connects nodes at the edge to base stations but not directly nodes. This paper describes the extension and its performance. The extension is based on our previous approach but has several contributions in common with other embedded computing systems for 5G networks.
{"title":"5G-enabled Edge Computing for MapReduce-based Data Pre-processing","authors":"I. Satoh","doi":"10.1109/FMEC49853.2020.9144882","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144882","url":null,"abstract":"The notion of edge computing, including fog computing, is to shift tasks to process data generated from sensing systems from the server-side to the network edge. Data directly measured by sensing systems tend to contain noise and loss and be in a non-canonical representation. Pre-processing for such data is often needed to reduce noise and to translate the data to a canonical representation. MapReduce processing, which was originally designed to be executed on a cluster of high-performance servers, is also useful for pre-processing data generated at the edge of a network. To support edge computing, we previously developed an approach to enable processing in embedded computers connected through wired or wireless local area networks in a peer-to-peer manner. The purpose of this paper is to extend our existing approach to give it the ability to work 5G networks. The extended approach connects nodes at the edge to base stations but not directly nodes. This paper describes the extension and its performance. The extension is based on our previous approach but has several contributions in common with other embedded computing systems for 5G networks.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921167","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144881
M. García, João Rodrigues, Joaquim Silva, Eduardo R. B. Marques, Luís M. B. Lopes
We present RAMBLE11ramble: (verb) wander about; travel aimlessly. (source: Thesaurus.com), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. Ramble makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.
{"title":"Ramble: Opportunistic Crowdsourcing of User-Generated Data using Mobile Edge Clouds","authors":"M. García, João Rodrigues, Joaquim Silva, Eduardo R. B. Marques, Luís M. B. Lopes","doi":"10.1109/FMEC49853.2020.9144881","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144881","url":null,"abstract":"We present RAMBLE11ramble: (verb) wander about; travel aimlessly. (source: Thesaurus.com), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. Ramble makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913980","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144824
Georgios L. Stavrinides, H. Karatza
As fog computing continues to gain momentum, the effective orchestration of Internet of Things (IoT) workloads on such heterogeneous environments is an open challenge. In order to devise effective load balancing and scheduling strategies, it is crucial to gain a deeper understanding of how the locality of the workload initial input data affects the performance of such platforms. To this direction, in this paper we evaluate the performance of a heterogeneous fog environment where multiple data-intensive workflow jobs with deadline constraints arrive dynamically. Each entry component task of a workflow may require input data either from the IoT layer or from local fog resources (e.g., IoT data that have already been transferred to the fog layer or data processed by previous jobs). We investigate the impact of workflow entry task input data locality on the performance of the system, under different data location probabilities. This is the main goal and contribution of this work. The evaluation of the framework under study is performed via simulation, in an attempt to gain useful insights into how the locality of the workload input data affects the adopted performance metrics.
{"title":"Orchestration of Real-Time Workflows with Varying Input Data Locality in a Heterogeneous Fog Environment","authors":"Georgios L. Stavrinides, H. Karatza","doi":"10.1109/FMEC49853.2020.9144824","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144824","url":null,"abstract":"As fog computing continues to gain momentum, the effective orchestration of Internet of Things (IoT) workloads on such heterogeneous environments is an open challenge. In order to devise effective load balancing and scheduling strategies, it is crucial to gain a deeper understanding of how the locality of the workload initial input data affects the performance of such platforms. To this direction, in this paper we evaluate the performance of a heterogeneous fog environment where multiple data-intensive workflow jobs with deadline constraints arrive dynamically. Each entry component task of a workflow may require input data either from the IoT layer or from local fog resources (e.g., IoT data that have already been transferred to the fog layer or data processed by previous jobs). We investigate the impact of workflow entry task input data locality on the performance of the system, under different data location probabilities. This is the main goal and contribution of this work. The evaluation of the framework under study is performed via simulation, in an attempt to gain useful insights into how the locality of the workload input data affects the adopted performance metrics.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905566","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144876
Yashaswi Karnati, D. Mahajan, A. Rangarajan, S. Ranka
Detection of traffic interruptions is a critical aspect of managing traffic on urban road networks. This work outlines a semi-supervised strategy to automatically detect traffic interruptions occurring on arteries using high resolution data from widely deployed inductive loop detectors. The techniques highlighted in this paper are tested on data collected from detectors installed on more than 300 signalized intersections over a 6 month period. Our results show that we can detect interruptions with high precision and recall.
{"title":"Machine Learning Algorithms for Traffic Interruption Detection","authors":"Yashaswi Karnati, D. Mahajan, A. Rangarajan, S. Ranka","doi":"10.1109/FMEC49853.2020.9144876","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144876","url":null,"abstract":"Detection of traffic interruptions is a critical aspect of managing traffic on urban road networks. This work outlines a semi-supervised strategy to automatically detect traffic interruptions occurring on arteries using high resolution data from widely deployed inductive loop detectors. The techniques highlighted in this paper are tested on data collected from detectors installed on more than 300 signalized intersections over a 6 month period. Our results show that we can detect interruptions with high precision and recall.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123127766","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144763
David Kozhaya, T. Sivanthi, R. Eidenbenz
In order to ensure high availability, industrial automation systems such as substation automation systems often rely on IEC 62439 seamless redundancy protocols like PRP and HSR [1]. However, the high availability achieved by these protocols relies on the correct cabling of the network devices. Wrong cabling of such networks can happen with non-negligible likelihood, especially during network extensions or refurbishments. This paper proposes a new distributed solution that leverages the benefits offered by an IoT edge orchestration layer, namely dynamic and seamless deployment of context-aware applications on devices of an industrial automation system. Using the orchestration layer, our solution deploys system-wide distributed agents to not only discover but also mitigate wrong cabling in PRP and HSR networks in an automated fashion.
{"title":"Automatic Mitigation of Wrong Cabling in High Availability Industrial Networks","authors":"David Kozhaya, T. Sivanthi, R. Eidenbenz","doi":"10.1109/FMEC49853.2020.9144763","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144763","url":null,"abstract":"In order to ensure high availability, industrial automation systems such as substation automation systems often rely on IEC 62439 seamless redundancy protocols like PRP and HSR [1]. However, the high availability achieved by these protocols relies on the correct cabling of the network devices. Wrong cabling of such networks can happen with non-negligible likelihood, especially during network extensions or refurbishments. This paper proposes a new distributed solution that leverages the benefits offered by an IoT edge orchestration layer, namely dynamic and seamless deployment of context-aware applications on devices of an industrial automation system. Using the orchestration layer, our solution deploys system-wide distributed agents to not only discover but also mitigate wrong cabling in PRP and HSR networks in an automated fashion.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125286204","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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