Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651952
G. Bianchi, A. L. Rosa, Gabriele Restuccia
Many Internet-connected appliances are often moved to different environments, such as when they are re-located. And even when they are buried in a given physical environment, their ownership "moves", such as when a home or smart space changes hands. This calls for roaming-friendly IoT authentication devised to circumvent the need to deploy long-term authentication credentials across different visited domains. Noting that this issue has been very extensively addressed since at least three decades in cellular network, in this paper we integrate, within the RIOT IoT Operating system, an authentication and key agreement protocol designed to be as close as possible to the standard one used by 4G/5G cellular systems. Our design accounts for a few technical improvements made possible since, unlike the case of cellular networks, we are here free from back-ward compatibility issues. Our proof-of-concept implementation is built on COAP for the radio interface, and on HTTPS for the core network signaling parts, and can be further configured to use two different types of secret keys: pre-shared or on-demand, (re)generated via a SRAM-PUF API available in RIOT.
{"title":"RIOT-AKA: cellular-like authentication over IoT devices","authors":"G. Bianchi, A. L. Rosa, Gabriele Restuccia","doi":"10.1109/ICNP52444.2021.9651952","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651952","url":null,"abstract":"Many Internet-connected appliances are often moved to different environments, such as when they are re-located. And even when they are buried in a given physical environment, their ownership \"moves\", such as when a home or smart space changes hands. This calls for roaming-friendly IoT authentication devised to circumvent the need to deploy long-term authentication credentials across different visited domains. Noting that this issue has been very extensively addressed since at least three decades in cellular network, in this paper we integrate, within the RIOT IoT Operating system, an authentication and key agreement protocol designed to be as close as possible to the standard one used by 4G/5G cellular systems. Our design accounts for a few technical improvements made possible since, unlike the case of cellular networks, we are here free from back-ward compatibility issues. Our proof-of-concept implementation is built on COAP for the radio interface, and on HTTPS for the core network signaling parts, and can be further configured to use two different types of secret keys: pre-shared or on-demand, (re)generated via a SRAM-PUF API available in RIOT.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128823471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICNP52444.2021.9651976
Felipe Valle, M. Cooney, Konstantin Mikhaylov, A. Vinel
In this paper, we conceptualise and propose integrating UAVs with Intelligent Transportation Systems (ITS) based on using the Cooperative-ITS (C-ITS) framework. We start by discussing the state of the art and pinpointing some of the reasons for integration and the applications that the envisaged integration would enable. Next, we recall the critical aspects of the state of the art C-ITS connectivity and discuss how seamless integration of UAVs into C-ITS can be achieved. Notably, we show that encapsulation of UAVs in C-ITS does not imply significant changes for the currently existing mechanisms and data formats. Finally, we discuss some of the open research challenges related to the integration and operation of the integrated systems and pinpoint some mechanisms which can help to address these.
{"title":"The integration of UAVs to the C-ITS Stack","authors":"Felipe Valle, M. Cooney, Konstantin Mikhaylov, A. Vinel","doi":"10.1109/ICNP52444.2021.9651976","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651976","url":null,"abstract":"In this paper, we conceptualise and propose integrating UAVs with Intelligent Transportation Systems (ITS) based on using the Cooperative-ITS (C-ITS) framework. We start by discussing the state of the art and pinpointing some of the reasons for integration and the applications that the envisaged integration would enable. Next, we recall the critical aspects of the state of the art C-ITS connectivity and discuss how seamless integration of UAVs into C-ITS can be achieved. Notably, we show that encapsulation of UAVs in C-ITS does not imply significant changes for the currently existing mechanisms and data formats. Finally, we discuss some of the open research challenges related to the integration and operation of the integrated systems and pinpoint some mechanisms which can help to address these.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129640248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1109/ICNP52444.2021.9651934
Qiang Liu, Nakjung Choi, Tao Han
Network slicing is a promising technology that allows mobile network operators to efficiently serve various emerging use cases in 5G. It is challenging to optimize the utilization of network infrastructures while guaranteeing the performance of network slices according to service level agreements (SLAs). To solve this problem, we propose SafeSlicing that introduces a new constraint-aware deep reinforcement learning (CaDRL) algorithm to learn the optimal resource orchestration policy within two steps, i.e., offline training in a simulated environment and online learning with the real network system. On optimizing the resource orchestration, we incorporate the constraints on the statistical performance of slices in the reward function using Lagrangian multipliers, and solve the Lagrangian relaxed problem via a policy network. To satisfy the constraints on the system capacity, we design a constraint network to map the latent actions generated from the policy network to the orchestration actions such that the total resources allocated to network slices do not exceed the system capacity. We prototype SafeSlicing on an end-to-end testbed developed by using OpenAirInterface LTE, OpenDayLight-based SDN, and CUDA GPU computing platform. The experimental results show that SafeSlicing reduces more than 20% resource usage while meeting SLAs of network slices as compared with other solutions.
{"title":"Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks","authors":"Qiang Liu, Nakjung Choi, Tao Han","doi":"10.1109/ICNP52444.2021.9651934","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651934","url":null,"abstract":"Network slicing is a promising technology that allows mobile network operators to efficiently serve various emerging use cases in 5G. It is challenging to optimize the utilization of network infrastructures while guaranteeing the performance of network slices according to service level agreements (SLAs). To solve this problem, we propose SafeSlicing that introduces a new constraint-aware deep reinforcement learning (CaDRL) algorithm to learn the optimal resource orchestration policy within two steps, i.e., offline training in a simulated environment and online learning with the real network system. On optimizing the resource orchestration, we incorporate the constraints on the statistical performance of slices in the reward function using Lagrangian multipliers, and solve the Lagrangian relaxed problem via a policy network. To satisfy the constraints on the system capacity, we design a constraint network to map the latent actions generated from the policy network to the orchestration actions such that the total resources allocated to network slices do not exceed the system capacity. We prototype SafeSlicing on an end-to-end testbed developed by using OpenAirInterface LTE, OpenDayLight-based SDN, and CUDA GPU computing platform. The experimental results show that SafeSlicing reduces more than 20% resource usage while meeting SLAs of network slices as compared with other solutions.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-08DOI: 10.1109/ICNP52444.2021.9651936
Kai Sun, Zhimeng Yin, Weiwei Chen, Shuai Wang, Zeyu Zhang, Tian He
Recent years have witnessed the proliferation of Low-power Wide Area Networks (LPWANs) in the unlicensed band for various Internet-of-Things (IoT) applications. Due to the ultra-low transmission power and long transmission duration, LPWAN devices inevitably suffer from high power Cross Technology Interference (CTI), such as interference from Wi-Fi, coexisting in the same spectrum. To alleviate this issue, this paper introduces the Partial Symbol Recovery (PSR) scheme for improving the CTI resilience of LPWAN. We verify our idea on LoRa, a widely adopted LPWAN technique, as a proof of concept.At the PHY layer, although CTI has much higher power, its duration is relatively shorter compared with LoRa symbols, leaving part of a LoRa symbol uncorrupted. Moreover, due to its high redundancy, LoRa chips within a symbol are highly correlated. This opens the possibility of detecting a LoRa symbol with only part of the chips. By examining the unique frequency patterns in LoRa symbols with time-frequency analysis, our design effectively detects the clean LoRa chips that are free of CTI. This enables PSR to only rely on clean LoRa chips for successfully recovering from communication failures. We evaluate our PSR design with real-world testbeds, including SX1280 LoRa chips and USRP B210, under Wi-Fi interference in various scenarios. Extensive experiments demonstrate that our design offers reliable packet recovery performance, successfully boosting the LoRa packet reception ratio from 45.2% to 82.2% with a performance gain of 1.8×.
{"title":"Partial Symbol Recovery for Interference Resilience in Low-Power Wide Area Networks","authors":"Kai Sun, Zhimeng Yin, Weiwei Chen, Shuai Wang, Zeyu Zhang, Tian He","doi":"10.1109/ICNP52444.2021.9651936","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651936","url":null,"abstract":"Recent years have witnessed the proliferation of Low-power Wide Area Networks (LPWANs) in the unlicensed band for various Internet-of-Things (IoT) applications. Due to the ultra-low transmission power and long transmission duration, LPWAN devices inevitably suffer from high power Cross Technology Interference (CTI), such as interference from Wi-Fi, coexisting in the same spectrum. To alleviate this issue, this paper introduces the Partial Symbol Recovery (PSR) scheme for improving the CTI resilience of LPWAN. We verify our idea on LoRa, a widely adopted LPWAN technique, as a proof of concept.At the PHY layer, although CTI has much higher power, its duration is relatively shorter compared with LoRa symbols, leaving part of a LoRa symbol uncorrupted. Moreover, due to its high redundancy, LoRa chips within a symbol are highly correlated. This opens the possibility of detecting a LoRa symbol with only part of the chips. By examining the unique frequency patterns in LoRa symbols with time-frequency analysis, our design effectively detects the clean LoRa chips that are free of CTI. This enables PSR to only rely on clean LoRa chips for successfully recovering from communication failures. We evaluate our PSR design with real-world testbeds, including SX1280 LoRa chips and USRP B210, under Wi-Fi interference in various scenarios. Extensive experiments demonstrate that our design offers reliable packet recovery performance, successfully boosting the LoRa packet reception ratio from 45.2% to 82.2% with a performance gain of 1.8×.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122069646","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}
Mobile edge computing (MEC) is proposed to boost high-efficient and time-sensitive 5G applications. However, the "microburst" may occur even in lightly-loaded scenarios, which leads to the indeterministic service latency, hence hindering the deployment of MEC. Deterministic IP networking (DIP) has been proposed to provide bounds on latency, and high reliability in the large-scale networks. Nevertheless, the direct migration of DIP into the MEC network is non-trivial owing to its original design for the Ethernet with homogeneous devices. Meanwhile, DIP also faces the challenges on the network throughput and scheduling flexibility. In this paper, we delve into the adoption of DIP for the MEC networks and some of the relevant aspects. A deterministic MEC (D-MEC) network is proposed to deliver the deterministic MEC service. In the D-MEC network, the cycle mapping and cycle shifting are designed to enable: (i) seamless and deterministic transmission with heterogeneous underlaid resources; and (ii) traffic shaping on the edges to improve the resource utilization. We also formulate a joint configuration to maximize the network throughput with deterministic QoS guarantees. Extensive simulations verify that the proposed D-MEC network can achieve a deterministic MEC service, even in the highly-loaded scenarios.
{"title":"Achieving Deterministic Service in Mobile Edge Computing (MEC) Networks","authors":"Binwei Wu, Jiasen Wang, Yanyan Wang, Weiqiang Tan, Yudong Huang","doi":"10.1109/ICNP52444.2021.9651958","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651958","url":null,"abstract":"Mobile edge computing (MEC) is proposed to boost high-efficient and time-sensitive 5G applications. However, the \"microburst\" may occur even in lightly-loaded scenarios, which leads to the indeterministic service latency, hence hindering the deployment of MEC. Deterministic IP networking (DIP) has been proposed to provide bounds on latency, and high reliability in the large-scale networks. Nevertheless, the direct migration of DIP into the MEC network is non-trivial owing to its original design for the Ethernet with homogeneous devices. Meanwhile, DIP also faces the challenges on the network throughput and scheduling flexibility. In this paper, we delve into the adoption of DIP for the MEC networks and some of the relevant aspects. A deterministic MEC (D-MEC) network is proposed to deliver the deterministic MEC service. In the D-MEC network, the cycle mapping and cycle shifting are designed to enable: (i) seamless and deterministic transmission with heterogeneous underlaid resources; and (ii) traffic shaping on the edges to improve the resource utilization. We also formulate a joint configuration to maximize the network throughput with deterministic QoS guarantees. Extensive simulations verify that the proposed D-MEC network can achieve a deterministic MEC service, even in the highly-loaded scenarios.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115637707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-03DOI: 10.1109/ICNP52444.2021.9651930
Guillermo Bernárdez, Jos'e Su'arez-Varela, Albert Lopez, Bo-Xi Wu, Shihan Xiao, Xiangle Cheng, P. Barlet-Ros, A. Cabellos-Aparicio
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).
{"title":"Is Machine Learning Ready for Traffic Engineering Optimization?","authors":"Guillermo Bernárdez, Jos'e Su'arez-Varela, Albert Lopez, Bo-Xi Wu, Shihan Xiao, Xiangle Cheng, P. Barlet-Ros, A. Cabellos-Aparicio","doi":"10.1109/ICNP52444.2021.9651930","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651930","url":null,"abstract":"Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764973","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}
Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded to the network on programmable switches. These tasks may require the support of on-the-fly floatingpoint operations. Unfortunately, the computational capacity of programmable switches is limited to simple integer arithmetic operations. To address this issue, prior approaches either adopt a float-to-integer method or rely on local CPUs of switches, incurring accuracy loss and delayed processing.To this end, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. NetFC adopts a divide-and-conquer mechanism that converts the original huge table into several much smaller tables that are operated by the built-in integer operations. NetFC further leverages a scaling-factor mechanism for improving computational accuracy, and a prefix-based lossless table compression method to reduce memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.94% with only 448KB memory consumption. Furthermore, we integrate NetFC into Sonata [12] for detecting Slowloris attack, yielding significant decrease of detection delay.
{"title":"NetFC: Enabling Accurate Floating-point Arithmetic on Programmable Switches","authors":"Penglai Cui, H. Pan, Zhenyu Li, Jiaoren Wu, Shengzhuo Zhang, Xingwu Yang, Hongtao Guan, Gaogang Xie","doi":"10.1109/ICNP52444.2021.9651946","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651946","url":null,"abstract":"Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded to the network on programmable switches. These tasks may require the support of on-the-fly floatingpoint operations. Unfortunately, the computational capacity of programmable switches is limited to simple integer arithmetic operations. To address this issue, prior approaches either adopt a float-to-integer method or rely on local CPUs of switches, incurring accuracy loss and delayed processing.To this end, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. NetFC adopts a divide-and-conquer mechanism that converts the original huge table into several much smaller tables that are operated by the built-in integer operations. NetFC further leverages a scaling-factor mechanism for improving computational accuracy, and a prefix-based lossless table compression method to reduce memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.94% with only 448KB memory consumption. Furthermore, we integrate NetFC into Sonata [12] for detecting Slowloris attack, yielding significant decrease of detection delay.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131861151","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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