Pub Date : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000668
João Pedro, H. Bock
The emergence of coherent pluggable interfaces is enabling to streamline the network architecture and is raising the prospect of extra reductions in both capital and operational expenditures. Nevertheless, the relatively long lifecycle of line systems (e.g., 10 years), when compared to the shorter lifecycle of line interfaces (e.g., 3 years), means a large fraction of network operators will be looking to leverage these pluggable devices in brownfield deployment scenarios. In this case, the legacy line system can hamper the effectiveness of exploiting the pluggable interfaces. This paper describes a comprehensive framework to estimate the impact of the line system on key network capacity and cost-related metrics when pluggable line interfaces are deployed. The framework is used on a reference optical transport network with growing demand for capacity and assuming a set of possible line systems, including legacy and modern ones. The simulation results highlight that although there are clear benefits in terms of usable capacity when these interfaces are deployed over modern line systems (greenfield scenario), it is still possible to reach competitive capacity figures with legacy line systems (brownfield scenario), particularly via exploiting a trade-off between capacity and the number of line interfaces used as regenerators.
{"title":"Effectiveness of Coherent Pluggable Interfaces in Brownfield Optical Network Deployments","authors":"João Pedro, H. Bock","doi":"10.1109/LATINCOM56090.2022.10000668","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000668","url":null,"abstract":"The emergence of coherent pluggable interfaces is enabling to streamline the network architecture and is raising the prospect of extra reductions in both capital and operational expenditures. Nevertheless, the relatively long lifecycle of line systems (e.g., 10 years), when compared to the shorter lifecycle of line interfaces (e.g., 3 years), means a large fraction of network operators will be looking to leverage these pluggable devices in brownfield deployment scenarios. In this case, the legacy line system can hamper the effectiveness of exploiting the pluggable interfaces. This paper describes a comprehensive framework to estimate the impact of the line system on key network capacity and cost-related metrics when pluggable line interfaces are deployed. The framework is used on a reference optical transport network with growing demand for capacity and assuming a set of possible line systems, including legacy and modern ones. The simulation results highlight that although there are clear benefits in terms of usable capacity when these interfaces are deployed over modern line systems (greenfield scenario), it is still possible to reach competitive capacity figures with legacy line systems (brownfield scenario), particularly via exploiting a trade-off between capacity and the number of line interfaces used as regenerators.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124828304","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000544
Diego Abreu, A. Abelém
Severa1 Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.
{"title":"OMINACS: Online ML-Based IoT Network Attack Detection and Classification System","authors":"Diego Abreu, A. Abelém","doi":"10.1109/LATINCOM56090.2022.10000544","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000544","url":null,"abstract":"Severa1 Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123832394","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000503
B. V. D. Santos, Andressa Vergütz, R. Macedo, M. N. Lima
The Internet of Things (IoT) has revolutionized how people interact with their living spaces. However, attackers can perform traffic-based attacks to reveal the behavior of legitimate users, seriously compromising their privacy. Studies have proposed to obfuscate network traffic to avoid these attacks. However, there is still the challenge of ensuring a trade-off between privacy and network overhead. This work introduces the MITRA method to protect user privacy in smart homes while keeping IoT network overhead low. The method relies on the dummy traffic injection following different levels of obfuscation. The different levels mask network traffic, improving privacy without harming network performance unnecessarily. Results show that the obfuscation of IoT device traffic reduces the traffic identification accuracy by up to 42%.
{"title":"A Dynamic Method to Protect User Privacy Against Traffic-based Attacks on Smart Home","authors":"B. V. D. Santos, Andressa Vergütz, R. Macedo, M. N. Lima","doi":"10.1109/LATINCOM56090.2022.10000503","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000503","url":null,"abstract":"The Internet of Things (IoT) has revolutionized how people interact with their living spaces. However, attackers can perform traffic-based attacks to reveal the behavior of legitimate users, seriously compromising their privacy. Studies have proposed to obfuscate network traffic to avoid these attacks. However, there is still the challenge of ensuring a trade-off between privacy and network overhead. This work introduces the MITRA method to protect user privacy in smart homes while keeping IoT network overhead low. The method relies on the dummy traffic injection following different levels of obfuscation. The different levels mask network traffic, improving privacy without harming network performance unnecessarily. Results show that the obfuscation of IoT device traffic reduces the traffic identification accuracy by up to 42%.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130091586","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000455
Abdullah Alajmi, M. Fayaz, Waleed Ahsan, A. Nallanathan
With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine type communications networks are expected to connect large-scale Internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex hence, it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. Therefore, this work develops an efficient model-free BACNOMA system to assist the base station for complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, softactor critic. Numerical results show that the proposed algorithm obtained a higher reward and converges to an optimal solution with respect to a large number of iterations. The proposed algorithm increases the sum rate by 57.6% as compared to the conventional optimization (benchmark) approach. Moreover, we show that the proposed algorithm outperforms the conventional BAC-NOMA scheme and BAC with orthogonal multiple access in terms of average sum rate with the increasing number of backscatter devices.
{"title":"Soft Actor Critic Framework for Resource Allocation in Backscatter-NOMA Networks","authors":"Abdullah Alajmi, M. Fayaz, Waleed Ahsan, A. Nallanathan","doi":"10.1109/LATINCOM56090.2022.10000455","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000455","url":null,"abstract":"With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine type communications networks are expected to connect large-scale Internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex hence, it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. Therefore, this work develops an efficient model-free BACNOMA system to assist the base station for complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, softactor critic. Numerical results show that the proposed algorithm obtained a higher reward and converges to an optimal solution with respect to a large number of iterations. The proposed algorithm increases the sum rate by 57.6% as compared to the conventional optimization (benchmark) approach. Moreover, we show that the proposed algorithm outperforms the conventional BAC-NOMA scheme and BAC with orthogonal multiple access in terms of average sum rate with the increasing number of backscatter devices.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134062709","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000542
C. Dias, Lucas D. de Mendonça, Karoline Ferreira Tornisiello, A. S. Guerreiro, E. Lima, G. Fraidenraich
With the growing power grid needs in recent years, several different Smart Meters (SMs) have evolved to address diverse challenges. However, interoperability for SMs suppliers is challenging due to the diversity of protocols, data models, and interfaces. In this way, the Wireless Smart Ubiquitous Network (Wi-SUN) is the straightforward solution to address such issue.In this work, we present a simple, low-cost, open-source platform to assess the interoperability with Wi-SUN Field Area Network (FAN) devices. This platform allows the testing of devices under different conditions to check their conformance with the Wi-SUN standard.
{"title":"A Straightforward Method to Promote Effective Interoperability in Wi-SUN FAN Smart Grid Networks","authors":"C. Dias, Lucas D. de Mendonça, Karoline Ferreira Tornisiello, A. S. Guerreiro, E. Lima, G. Fraidenraich","doi":"10.1109/LATINCOM56090.2022.10000542","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000542","url":null,"abstract":"With the growing power grid needs in recent years, several different Smart Meters (SMs) have evolved to address diverse challenges. However, interoperability for SMs suppliers is challenging due to the diversity of protocols, data models, and interfaces. In this way, the Wireless Smart Ubiquitous Network (Wi-SUN) is the straightforward solution to address such issue.In this work, we present a simple, low-cost, open-source platform to assess the interoperability with Wi-SUN Field Area Network (FAN) devices. This platform allows the testing of devices under different conditions to check their conformance with the Wi-SUN standard.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793865","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000569
A. Famili, M. Foruhandeh, Tolga O. Atalay, A. Stavrou, Haining Wang
Global Positioning System (GPS) is the most predominant non-authenticated navigation system used in transportation networks for geolocation and timing. The security of GPS is not addressed at the design level, and its implementation is public knowledge, making all GPS-equipped devices susceptible to GPS spoofing attacks. Existing solutions such as cryptography are either not backward compatible or too expensive to implement. Here, we propose an approach without such drawbacks. We present a novel technique to detect GPS spoofing attacks by comparing the final estimated location based on the GPS measurements with that derived by 5G New Radio positioning signals. In case of discrepancy, we detect the GPS spoofing attack and bypass the attacker by replacing the GPS-based localization with 5G-based localization. Our experiments show a detection rate above 98%.
{"title":"GPS Spoofing Detection by Leveraging 5G Positioning Capabilities","authors":"A. Famili, M. Foruhandeh, Tolga O. Atalay, A. Stavrou, Haining Wang","doi":"10.1109/LATINCOM56090.2022.10000569","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000569","url":null,"abstract":"Global Positioning System (GPS) is the most predominant non-authenticated navigation system used in transportation networks for geolocation and timing. The security of GPS is not addressed at the design level, and its implementation is public knowledge, making all GPS-equipped devices susceptible to GPS spoofing attacks. Existing solutions such as cryptography are either not backward compatible or too expensive to implement. Here, we propose an approach without such drawbacks. We present a novel technique to detect GPS spoofing attacks by comparing the final estimated location based on the GPS measurements with that derived by 5G New Radio positioning signals. In case of discrepancy, we detect the GPS spoofing attack and bypass the attacker by replacing the GPS-based localization with 5G-based localization. Our experiments show a detection rate above 98%.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130816408","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000566
R. Lent
Challenged networks are characterized by a time-varying operational environment that constrains the optimality of standard routing algorithms. This work investigates a deep learning method that tackles the bundle routing problem by taking advantage of the available network metrics and performance data through a graph neural network (GNN). A cognitive routing decision unit is formulated by defining a GNN structure that accepts both edge and node input features, and that is trained with reinforcement learning. The GNN allows the inputs to be permutation invariant and independent of the network size and connectivity. Simulation results demonstrate that the proposed cognitive routing method is able to learn how to optimize the next-hop for each data bundle of a flow to achieve lower end-to-end delivery latency than the standard Contact Graph Routing algorithm. The GNN achieves the optimization by detecting and avoiding the extended wait times caused by both butter congestion and the stall times for the next contact when long link disruptions occur.
{"title":"Dynamic Routing in Challenged Networks with Graph Neural Networks","authors":"R. Lent","doi":"10.1109/LATINCOM56090.2022.10000566","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000566","url":null,"abstract":"Challenged networks are characterized by a time-varying operational environment that constrains the optimality of standard routing algorithms. This work investigates a deep learning method that tackles the bundle routing problem by taking advantage of the available network metrics and performance data through a graph neural network (GNN). A cognitive routing decision unit is formulated by defining a GNN structure that accepts both edge and node input features, and that is trained with reinforcement learning. The GNN allows the inputs to be permutation invariant and independent of the network size and connectivity. Simulation results demonstrate that the proposed cognitive routing method is able to learn how to optimize the next-hop for each data bundle of a flow to achieve lower end-to-end delivery latency than the standard Contact Graph Routing algorithm. The GNN achieves the optimization by detecting and avoiding the extended wait times caused by both butter congestion and the stall times for the next contact when long link disruptions occur.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115443373","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000541
Penghan Yan, R. Meneguette, R. E. Grande
Vehicle Fog computing combines intelligent and connected vehicles to form a mobile cloud. Several works have modelled link stability for data delivery in light of solving the issues originating from unstable vehicle connectivity. However, results have shown that some mobility patterns potentially misguide the uncertainty-based estimation process. We thus propose a region-based connectivity ranking strategy. A fog management approach dynamically defines and supervises regions delimited by vehicles; such regions are mapped over an urban centre. In addition, the model develops a software-defined vehicular network (SDVN) controller to select data from the vehicular heterogeneous network environment through V2X and C-V2X. Our model admits four parameters to describe vehicular connectivity, which evaluates vehicles’ potential for communication and performs dynamic vehicular clustering. The 5G and DSRC heterogeneous networks support a more precise connectivity model for vehicular classification. Simulated analyses allow observing vehicular mobility and connectivity data in real-time scenarios where the management efficiency of vehicular fog regions is assessed in SDVN context.
{"title":"Connectivity-based Fog Structure Management for Software-defined Vehicular Networks","authors":"Penghan Yan, R. Meneguette, R. E. Grande","doi":"10.1109/LATINCOM56090.2022.10000541","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000541","url":null,"abstract":"Vehicle Fog computing combines intelligent and connected vehicles to form a mobile cloud. Several works have modelled link stability for data delivery in light of solving the issues originating from unstable vehicle connectivity. However, results have shown that some mobility patterns potentially misguide the uncertainty-based estimation process. We thus propose a region-based connectivity ranking strategy. A fog management approach dynamically defines and supervises regions delimited by vehicles; such regions are mapped over an urban centre. In addition, the model develops a software-defined vehicular network (SDVN) controller to select data from the vehicular heterogeneous network environment through V2X and C-V2X. Our model admits four parameters to describe vehicular connectivity, which evaluates vehicles’ potential for communication and performs dynamic vehicular clustering. The 5G and DSRC heterogeneous networks support a more precise connectivity model for vehicular classification. Simulated analyses allow observing vehicular mobility and connectivity data in real-time scenarios where the management efficiency of vehicular fog regions is assessed in SDVN context.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115845593","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 : 2022-11-30DOI: 10.1109/LATINCOM56090.2022.10000637
Asra Ashraf, J. Carlson, Jaap van de Beek
In this paper we present an analytical approach to the solid-state ultrasound communications channel. Channel reverberations and the long associated channel delay spreads pose the possibility that the channel length exceeds that of the moderate cyclic prefix in an orthogonal frequency division multiplexing (OFDM) system, resulting in intersymbol and intercarrier interference. We present a channel model based on the propagation material characteristics and evaluate the extent and impact of the intrinsic OFDM interferences. We derive an analytical expression and show with simulations that the intersymbol and intercarrier interference (ISI and ICI) are spectrally concentrated to the lower frequencies of the OFDM multiplex.
{"title":"The solid-body reverberating ultrasound communications channel and its OFDM interference","authors":"Asra Ashraf, J. Carlson, Jaap van de Beek","doi":"10.1109/LATINCOM56090.2022.10000637","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000637","url":null,"abstract":"In this paper we present an analytical approach to the solid-state ultrasound communications channel. Channel reverberations and the long associated channel delay spreads pose the possibility that the channel length exceeds that of the moderate cyclic prefix in an orthogonal frequency division multiplexing (OFDM) system, resulting in intersymbol and intercarrier interference. We present a channel model based on the propagation material characteristics and evaluate the extent and impact of the intrinsic OFDM interferences. We derive an analytical expression and show with simulations that the intersymbol and intercarrier interference (ISI and ICI) are spectrally concentrated to the lower frequencies of the OFDM multiplex.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114789772","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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