Pub Date : 2020-06-01DOI: 10.1109/ICC40277.2020.9149248
Antonios Pitarokoilis, M. Skoglund
The problem of communication in Rayleigh fading channels with estimated channel state information at the receiver (CSIR) is investigated. Based on a related hypothesis testing problem in the Neyman-Pearson formulation, a non-asymptotic– in the codeword block-length–converse on the maximal coding rate is derived. The bound summarizes succinctly the effect of various system parameters that include the length of channel coherence interval, the length of the training and data intervals and the power allocated to training and data transmission. The bound is also studied in the asymptotic–in the codeword blocklength–regime and a particularly simple, non-trivial upper bound on the ergodic capacity of Raleigh fading channels with estimated CSIR is obtained. Finally, a second-order asymptotic expansion of the non-asymptotic converse is provided, which can be very useful in the study of latency-constrained communication systems.
{"title":"A Non-Asymptotic Converse on the Maximal Coding Rate of Fading Channels with Partial CSIR","authors":"Antonios Pitarokoilis, M. Skoglund","doi":"10.1109/ICC40277.2020.9149248","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149248","url":null,"abstract":"The problem of communication in Rayleigh fading channels with estimated channel state information at the receiver (CSIR) is investigated. Based on a related hypothesis testing problem in the Neyman-Pearson formulation, a non-asymptotic– in the codeword block-length–converse on the maximal coding rate is derived. The bound summarizes succinctly the effect of various system parameters that include the length of channel coherence interval, the length of the training and data intervals and the power allocated to training and data transmission. The bound is also studied in the asymptotic–in the codeword blocklength–regime and a particularly simple, non-trivial upper bound on the ergodic capacity of Raleigh fading channels with estimated CSIR is obtained. Finally, a second-order asymptotic expansion of the non-asymptotic converse is provided, which can be very useful in the study of latency-constrained communication systems.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132683809","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149215
Yue Wang, V. Vokkarane
Due to the modern bandwidth-intensive, ever heterogeneous, and evolving network traffic, exhaustion of network resources under current technologies is foreseeable. In order to provision high quality network services for the next-generation network users, emerging network technologies must be relied on. Elastic optical networks (EON) and space division multiplexing (SDM) are the two preferred emerging optical network architectures to solve the future challenge of network demands. However, the spectrum contiguity constraint introduced by EON may lead to significant fragmentation. Slice-ability is an effective allocation framework that can mitigate spectrum fragmentation by dividing the lightpath into a set of sub-lightpaths, where each sub-lightpath consists of a fraction of the original lightpath bandwidth for the entire duration of the original request. Sliceability is conventionally used to solve routing, modulation, and spectrum assignment (RMSA) problems in EON. In this paper, we propose SDM sliceable framework that is crosstalk-and modulation-aware for solving the routing, modulation, core, and spectrum assignment (RMCSA) problems in SDM-EON. We refer each sub-lightpath as tight-segment. SDM slice-ability framework is compatible with all core and spectrum assignment (CSA) algorithms in literature. We evaluate light-segment based RMCSA algorithms using three existing CSA algorithms: First-Fit, Largest-First, and Best-Fit. Based on extensive performance evaluations, we observe significant improvement of RMCSA algorithms with SDM slice-ability compared to conventional approaches without SDM slice-ability.
{"title":"Light-segment: Crosstalk-and Modulation-Aware Spectrum Allocation with Segmentation in SDM-EON","authors":"Yue Wang, V. Vokkarane","doi":"10.1109/ICC40277.2020.9149215","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149215","url":null,"abstract":"Due to the modern bandwidth-intensive, ever heterogeneous, and evolving network traffic, exhaustion of network resources under current technologies is foreseeable. In order to provision high quality network services for the next-generation network users, emerging network technologies must be relied on. Elastic optical networks (EON) and space division multiplexing (SDM) are the two preferred emerging optical network architectures to solve the future challenge of network demands. However, the spectrum contiguity constraint introduced by EON may lead to significant fragmentation. Slice-ability is an effective allocation framework that can mitigate spectrum fragmentation by dividing the lightpath into a set of sub-lightpaths, where each sub-lightpath consists of a fraction of the original lightpath bandwidth for the entire duration of the original request. Sliceability is conventionally used to solve routing, modulation, and spectrum assignment (RMSA) problems in EON. In this paper, we propose SDM sliceable framework that is crosstalk-and modulation-aware for solving the routing, modulation, core, and spectrum assignment (RMCSA) problems in SDM-EON. We refer each sub-lightpath as tight-segment. SDM slice-ability framework is compatible with all core and spectrum assignment (CSA) algorithms in literature. We evaluate light-segment based RMCSA algorithms using three existing CSA algorithms: First-Fit, Largest-First, and Best-Fit. Based on extensive performance evaluations, we observe significant improvement of RMCSA algorithms with SDM slice-ability compared to conventional approaches without SDM slice-ability.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127847494","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148912
Ron Andrews, Dalton A. Hahn, Alexandru G. Bardas
Securing and hardening network protocols and services is a resource-consuming and continuous effort. Thus, it is important to question how prolific known, mitigable features of those protocols are. The Secure Shell (SSH) protocol is a good example due to its known vulnerability in using password based authentication. We take a closer look at these configurations to identify how prevalent the use of password authentication is at an internet scale. We show that current scanning tools and services provide a starting point in evaluating prevalence, but need to be validated for specific implementations. We also demonstrate that it is possible to augment some of these tools and services to determine the prevalence of password authentication in SSH specifically. As part of our evaluation, we propose a novel method for probing an SSH service to establish if password authentication is allowed, without being intrusive or causing harm to the host. Finally, we show that our analysis has resulted in determining that more than 65% of the over 20 million SSH servers on the public internet allow password authentication.
{"title":"Measuring the Prevalence of the Password Authentication Vulnerability in SSH","authors":"Ron Andrews, Dalton A. Hahn, Alexandru G. Bardas","doi":"10.1109/ICC40277.2020.9148912","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148912","url":null,"abstract":"Securing and hardening network protocols and services is a resource-consuming and continuous effort. Thus, it is important to question how prolific known, mitigable features of those protocols are. The Secure Shell (SSH) protocol is a good example due to its known vulnerability in using password based authentication. We take a closer look at these configurations to identify how prevalent the use of password authentication is at an internet scale. We show that current scanning tools and services provide a starting point in evaluating prevalence, but need to be validated for specific implementations. We also demonstrate that it is possible to augment some of these tools and services to determine the prevalence of password authentication in SSH specifically. As part of our evaluation, we propose a novel method for probing an SSH service to establish if password authentication is allowed, without being intrusive or causing harm to the host. Finally, we show that our analysis has resulted in determining that more than 65% of the over 20 million SSH servers on the public internet allow password authentication.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131403977","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149194
Greta Vallero, M. Deruyck, W. Joseph, M. Meo
In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.
{"title":"Caching at the edge in high energy-efficient wireless access networks","authors":"Greta Vallero, M. Deruyck, W. Joseph, M. Meo","doi":"10.1109/ICC40277.2020.9149194","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149194","url":null,"abstract":"In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133856807","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148733
Yifan Zhou, Huilin Zhou, Fuhui Zhou, D. W. K. Ng, R. Hu
Cognitive radio is a promising technology to improve spectral efficiency. However, communication security of a secondary network is limited by its transmit power and channel fading. In order to tackle this issue, by exploiting the high flexibility and the possibility of establishing line-of-sight links, a cognitive unmanned aerial vehicle (UAV) communication network is studied. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAVs trajectory and transmit power. Our formulated problem takes into account practical imperfect location estimation. To solve the non-convex problem, an iterative suboptimal algorithm based on the Bernstein-type inequalities is presented. Our simulation results demonstrate that the proposed scheme can improve the secure communication performance significantly compared to a benchmark scheme based on fixed trajectory.
{"title":"Robust Chance-Constrained Trajectory and Transmit Power Optimization for UAV-Enabled CR Networks","authors":"Yifan Zhou, Huilin Zhou, Fuhui Zhou, D. W. K. Ng, R. Hu","doi":"10.1109/ICC40277.2020.9148733","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148733","url":null,"abstract":"Cognitive radio is a promising technology to improve spectral efficiency. However, communication security of a secondary network is limited by its transmit power and channel fading. In order to tackle this issue, by exploiting the high flexibility and the possibility of establishing line-of-sight links, a cognitive unmanned aerial vehicle (UAV) communication network is studied. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAVs trajectory and transmit power. Our formulated problem takes into account practical imperfect location estimation. To solve the non-convex problem, an iterative suboptimal algorithm based on the Bernstein-type inequalities is presented. Our simulation results demonstrate that the proposed scheme can improve the secure communication performance significantly compared to a benchmark scheme based on fixed trajectory.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491001","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148817
Zhiyan Chen, Yueqian Zhang, Murat Simsek, B. Kantarci
Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm that emerged in the form of”sensed data as a service” model in the Internet of Things Era. Distributed nature of MCS results in vulnerabilities at the MCS platforms as well as participating devices that provide sensory data services. Submission of fake tasks with the aim of clogging sensing server resources and draining participating device batteries is a crucial threat that has not been investigated well. In this paper, we provide a detailed analysis by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. The experimental results show that the presented DBN-driven fake task mitigation detection of fake sensing tasks can ensure up to 0.92 accuracy, 0.943 precision and up to 0.928 F1 score outperforming prior work on MCS data with deep learning networks.
{"title":"Deep Belief Network-based Fake Task Mitigation for Mobile Crowdsensing under Data Scarcity","authors":"Zhiyan Chen, Yueqian Zhang, Murat Simsek, B. Kantarci","doi":"10.1109/ICC40277.2020.9148817","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148817","url":null,"abstract":"Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm that emerged in the form of”sensed data as a service” model in the Internet of Things Era. Distributed nature of MCS results in vulnerabilities at the MCS platforms as well as participating devices that provide sensory data services. Submission of fake tasks with the aim of clogging sensing server resources and draining participating device batteries is a crucial threat that has not been investigated well. In this paper, we provide a detailed analysis by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. The experimental results show that the presented DBN-driven fake task mitigation detection of fake sensing tasks can ensure up to 0.92 accuracy, 0.943 precision and up to 0.928 F1 score outperforming prior work on MCS data with deep learning networks.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115676507","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148945
Nolan H. Hamilton, Steve McKinney, Eddie Allan, E. Fulp
Domain shadowing is the introduction of an illegitimate subdomain under a preexisting legitimate domain. Attackers benefit not only from the inconspicuous nature of these subdomains, but also from the trust associated with the legitimate domain. Classifiers have been used to identify shadowed domains within the DNS namespace; however, most approaches rely on features created from a variety of sources, such as DNS data, Javascript inspection, and HTTP source. Unfortunately, the generation of these features is often highly time-consuming and the features themselves are not always effective in distinguishing current shadowing approaches.This paper introduces a new domain shadowing detection approach that leverages machine learning techniques (classifiers) distributed across multiple stages. Domain names are processed by later stages only if earlier stage findings are inconclusive; therefore, only domain names that require additional scrutiny undergo supplementary processing. Furthermore, features that can be quickly synthesized are located in earlier stages to further reduce detection time. Experimental results using the multi-stage detection system with data from recent domain shadowing campaigns results in 97.7% accuracy and 0.04% false positive rate, with an average classification time of 0.83 seconds per name.
{"title":"An Efficient Multi-Stage Approach for Identifying Domain Shadowing","authors":"Nolan H. Hamilton, Steve McKinney, Eddie Allan, E. Fulp","doi":"10.1109/ICC40277.2020.9148945","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148945","url":null,"abstract":"Domain shadowing is the introduction of an illegitimate subdomain under a preexisting legitimate domain. Attackers benefit not only from the inconspicuous nature of these subdomains, but also from the trust associated with the legitimate domain. Classifiers have been used to identify shadowed domains within the DNS namespace; however, most approaches rely on features created from a variety of sources, such as DNS data, Javascript inspection, and HTTP source. Unfortunately, the generation of these features is often highly time-consuming and the features themselves are not always effective in distinguishing current shadowing approaches.This paper introduces a new domain shadowing detection approach that leverages machine learning techniques (classifiers) distributed across multiple stages. Domain names are processed by later stages only if earlier stage findings are inconclusive; therefore, only domain names that require additional scrutiny undergo supplementary processing. Furthermore, features that can be quickly synthesized are located in earlier stages to further reduce detection time. Experimental results using the multi-stage detection system with data from recent domain shadowing campaigns results in 97.7% accuracy and 0.04% false positive rate, with an average classification time of 0.83 seconds per name.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800705","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 : 2020-06-01DOI: 10.1109/icc40277.2020.9149218
Walter Wong, Lorenzo Corneo, Aleksandr Zavodovski, Pengyuan Zhou, Nitinder Mohan, J. Kangasharju
AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.
{"title":"Bricklayer: Resource Composition on the Spot Market","authors":"Walter Wong, Lorenzo Corneo, Aleksandr Zavodovski, Pengyuan Zhou, Nitinder Mohan, J. Kangasharju","doi":"10.1109/icc40277.2020.9149218","DOIUrl":"https://doi.org/10.1109/icc40277.2020.9149218","url":null,"abstract":"AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115895559","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148986
Davide Callegaro, S. Baidya, M. Levorato
The analysis of information rich signals is at the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraints make the continuous execution of these algorithms challenging. Edge computing can mitigate such limitations and boost the system and mission performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, remote processing-control loops can quickly degrade. This paper presents Hydra, a framework for the dynamic selection of communication/computation resources in this challenging environment. A full - open-source - implementation of Hydra is discussed and tested via real-world experiments.
{"title":"Dynamic Distributed Computing for Infrastructure-Assisted Autonomous UAVs","authors":"Davide Callegaro, S. Baidya, M. Levorato","doi":"10.1109/ICC40277.2020.9148986","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148986","url":null,"abstract":"The analysis of information rich signals is at the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraints make the continuous execution of these algorithms challenging. Edge computing can mitigate such limitations and boost the system and mission performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, remote processing-control loops can quickly degrade. This paper presents Hydra, a framework for the dynamic selection of communication/computation resources in this challenging environment. A full - open-source - implementation of Hydra is discussed and tested via real-world experiments.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115907926","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148931
Aicha Dridi, Hatem Ibn-Khedher, Hassine Moungla, H. Afifi
With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.
{"title":"An Artificial Intelligence Approach for Time Series Next Generation Applications","authors":"Aicha Dridi, Hatem Ibn-Khedher, Hassine Moungla, H. Afifi","doi":"10.1109/ICC40277.2020.9148931","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148931","url":null,"abstract":"With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124245036","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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