With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results.
{"title":"A Hint-Based Random Access Protocol for mMTC in 5G Mobile Network","authors":"Yining Xu, Yi Ren, K. Lin, Y. Tseng","doi":"10.1109/MASS.2018.00062","DOIUrl":"https://doi.org/10.1109/MASS.2018.00062","url":null,"abstract":"With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123138310","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}
{"title":"Publisher's Information","authors":"","doi":"10.1109/mass.2018.00089","DOIUrl":"https://doi.org/10.1109/mass.2018.00089","url":null,"abstract":"","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692306","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}
The recent proliferation of smartphones has been the primary driving factor behind the booming of voice-based mobile applications. However, the human voice is often exposed to the public in many different scenarios, and an adversary can easily "steal" a person's voice and attack voice-based applications with the help of state-of-the-art voice synthesis/conversion softwares. In this paper, we propose a robust software-based voice liveness detection system for defending against voice spoofing attack. The proposed system is tailored for mobile platforms and can be easily integrated with existing mobile applications. We propose three approaches based on leveraging the vibration of human vocal cords, the motion of the human vocal system, and the functionality of vibration motor inside the smartphone. Experimental results show that our system can detect a live speaker with a mean accuracy of 94.38% and detect an attacker with a mean accuracy of 88.89% by combining three approaches we proposed.
{"title":"Defending Against Voice Spoofing: A Robust Software-Based Liveness Detection System","authors":"Jiacheng Shang, Si Chen, Jie Wu","doi":"10.1109/MASS.2018.00016","DOIUrl":"https://doi.org/10.1109/MASS.2018.00016","url":null,"abstract":"The recent proliferation of smartphones has been the primary driving factor behind the booming of voice-based mobile applications. However, the human voice is often exposed to the public in many different scenarios, and an adversary can easily \"steal\" a person's voice and attack voice-based applications with the help of state-of-the-art voice synthesis/conversion softwares. In this paper, we propose a robust software-based voice liveness detection system for defending against voice spoofing attack. The proposed system is tailored for mobile platforms and can be easily integrated with existing mobile applications. We propose three approaches based on leveraging the vibration of human vocal cords, the motion of the human vocal system, and the functionality of vibration motor inside the smartphone. Experimental results show that our system can detect a live speaker with a mean accuracy of 94.38% and detect an attacker with a mean accuracy of 88.89% by combining three approaches we proposed.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130420026","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}
Lingjing Yu, Tao Liu, Zhaoyu Zhou, Yujia Zhu, Qingyun Liu, Jianlong Tan
Wireless devices have been widely adopted across all domains. With the convenience brought by wireless communication technology, increasing number of conventional (wired) devices are evolving to become wireless. However, significant security issues arise with the popularity of wireless devices. To start an attack, the adversary usually performs a network reconnaissance to discover exposed devices, identify device manufacturers and types, and then scan for vulnerabilities. From the defense side, network administrators are expected to identify the potential vulnerabilities/risks and enforce Network Access Control (or Network Admission Control, NAC) on all the connecting devices. To do this, it is essential to accurately identify the make/model/type of each device that attempts to connect to the network, e.g., MacBooks, Samsung smart phones (Android), Amazon kindles, DLink surveillance cameras, TP-Link smart plugs, etc. In this paper, we present a novel approach, namely WDMTI, for the identification of wireless device manufacturer and type. We tackle the challenge from two aspects: the features and the classification model. First, we claim that it is critical to discover the device manufacturer and type as soon as the device requests to join the WLAN, and it is unrealistic to make other assumptions on the status of the device, e.g., assuming that the device is booting up or initializing a new connection to corresponding servers/clouds. We primarily depend on the features extracted from the network connection phase, while features from device booting are considered "bonus". In particular, we propose to utilize features from the raw HDCP packets, which is shown to be sufficient for device manufacturer and type recognition with high accuracy. Meanwhile, in the WDMTI system, we employ the Hierarchical Dirichlet Process (HDP), which is a nonparametric Bayesian model for grouped data. HDP allows new groups to be introduced with new data being added, i.e. previously unknown devices connect to the network and the extracted features receive new labels. The WDMTI mechanism is dynamically retrained on-line, instead of requiring a time-consuming off-line retraining process. Our experiments show that WDMTI identifies known types of devices with average accuracy of 0.89, and new types of devices with average accuracy of 0.96, both of which is higher than the state-of-art approaches. In summary, we present a wireless device manufacturer and type identification (WDMTI) system that is both scalable and accurate, and capable of adapting to unknown types of devices on-the-fly.
{"title":"WDMTI: Wireless Device Manufacturer and Type Identification Using Hierarchical Dirichlet Process","authors":"Lingjing Yu, Tao Liu, Zhaoyu Zhou, Yujia Zhu, Qingyun Liu, Jianlong Tan","doi":"10.1109/MASS.2018.00015","DOIUrl":"https://doi.org/10.1109/MASS.2018.00015","url":null,"abstract":"Wireless devices have been widely adopted across all domains. With the convenience brought by wireless communication technology, increasing number of conventional (wired) devices are evolving to become wireless. However, significant security issues arise with the popularity of wireless devices. To start an attack, the adversary usually performs a network reconnaissance to discover exposed devices, identify device manufacturers and types, and then scan for vulnerabilities. From the defense side, network administrators are expected to identify the potential vulnerabilities/risks and enforce Network Access Control (or Network Admission Control, NAC) on all the connecting devices. To do this, it is essential to accurately identify the make/model/type of each device that attempts to connect to the network, e.g., MacBooks, Samsung smart phones (Android), Amazon kindles, DLink surveillance cameras, TP-Link smart plugs, etc. In this paper, we present a novel approach, namely WDMTI, for the identification of wireless device manufacturer and type. We tackle the challenge from two aspects: the features and the classification model. First, we claim that it is critical to discover the device manufacturer and type as soon as the device requests to join the WLAN, and it is unrealistic to make other assumptions on the status of the device, e.g., assuming that the device is booting up or initializing a new connection to corresponding servers/clouds. We primarily depend on the features extracted from the network connection phase, while features from device booting are considered \"bonus\". In particular, we propose to utilize features from the raw HDCP packets, which is shown to be sufficient for device manufacturer and type recognition with high accuracy. Meanwhile, in the WDMTI system, we employ the Hierarchical Dirichlet Process (HDP), which is a nonparametric Bayesian model for grouped data. HDP allows new groups to be introduced with new data being added, i.e. previously unknown devices connect to the network and the extracted features receive new labels. The WDMTI mechanism is dynamically retrained on-line, instead of requiring a time-consuming off-line retraining process. Our experiments show that WDMTI identifies known types of devices with average accuracy of 0.89, and new types of devices with average accuracy of 0.96, both of which is higher than the state-of-art approaches. In summary, we present a wireless device manufacturer and type identification (WDMTI) system that is both scalable and accurate, and capable of adapting to unknown types of devices on-the-fly.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121590471","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}
Shaojun Zhang, Wei Li, Yongwei Wu, P. Watson, Albert Y. Zomaya
In recent years, Edge computing has emerged as a new paradigm that can reduce communication delays over the Internet by moving computation power from far-end cloud servers to be closer to data sources. It is natural to shift the design of cloud-based IoT applications to Edge-based ones. Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift. In this work, we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives, including architecture, algorithm design and system implementation. First, the Edge computing architecture is introduced and several critical management tasks are also investigated. Second, a realization of the Edge computing system is presented by using open source software and low-cost hardware. The consistency and scalability of running jobs on Edge devices are also addressed in our approach. Last, we propose a convolutional neural network model to perform activity recognition tasks on Edge devices. Preliminary experiments are conducted to compare our model with existing machine learning methods, and the results demonstrate that the performance of our model is promising.
{"title":"Enabling Edge Intelligence for Activity Recognition in Smart Homes","authors":"Shaojun Zhang, Wei Li, Yongwei Wu, P. Watson, Albert Y. Zomaya","doi":"10.1109/MASS.2018.00044","DOIUrl":"https://doi.org/10.1109/MASS.2018.00044","url":null,"abstract":"In recent years, Edge computing has emerged as a new paradigm that can reduce communication delays over the Internet by moving computation power from far-end cloud servers to be closer to data sources. It is natural to shift the design of cloud-based IoT applications to Edge-based ones. Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift. In this work, we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives, including architecture, algorithm design and system implementation. First, the Edge computing architecture is introduced and several critical management tasks are also investigated. Second, a realization of the Edge computing system is presented by using open source software and low-cost hardware. The consistency and scalability of running jobs on Edge devices are also addressed in our approach. Last, we propose a convolutional neural network model to perform activity recognition tasks on Edge devices. Preliminary experiments are conducted to compare our model with existing machine learning methods, and the results demonstrate that the performance of our model is promising.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115837852","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}
Data aggregation scheduling is a critical issue in wireless sensor networks (WSNs). This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing researches, nevertheless, either focus on non-sleeping scenarios, or assume that nodes communicate on one single channel, and thus have poor performance in multi-channel duty-cycled scenarios. In this paper, we first show that DDACD problem is NP-hard. We then propose two new concepts of Candidate Active Conflict Graphs (CACG) and Feasible Active Conflict Graphs (FACG) to depict the relationship of the data aggregation links, and present two coloring methods to well separate the links at different time-slots or on different channels. Based on these two new concepts and two coloring methods, we propose an Efficient Data Aggregation Scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. We theoretically prove that our proposed EDAS algorithm can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithm.
{"title":"Delay Efficient Data Aggregation Scheduling in Multi-channel Duty-Cycled WSNs","authors":"Xianlong Jiao, W. Lou, Xinxin Feng, Xiaodong Wang, Libin Yang, Guirong Chen","doi":"10.1109/MASS.2018.00055","DOIUrl":"https://doi.org/10.1109/MASS.2018.00055","url":null,"abstract":"Data aggregation scheduling is a critical issue in wireless sensor networks (WSNs). This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing researches, nevertheless, either focus on non-sleeping scenarios, or assume that nodes communicate on one single channel, and thus have poor performance in multi-channel duty-cycled scenarios. In this paper, we first show that DDACD problem is NP-hard. We then propose two new concepts of Candidate Active Conflict Graphs (CACG) and Feasible Active Conflict Graphs (FACG) to depict the relationship of the data aggregation links, and present two coloring methods to well separate the links at different time-slots or on different channels. Based on these two new concepts and two coloring methods, we propose an Efficient Data Aggregation Scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. We theoretically prove that our proposed EDAS algorithm can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithm.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129988231","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}
Recently, non-intrusive respiration monitoring has attracted much attention. Many respiration monitoring systems using the commercial off-the-shelf WiFi devices have been developed. However, these systems mainly have difficulties in the presence of multiple persons. The difficulty generally comes from the separation of the effects of multiple persons' respiration on the received WiFi signals. Another problem is that even though the separation can be feasible with some complicated algorithms, it is still impossible to map the multiple identified respiration states to the corresponding persons. In this paper, we study the problem of multi-person sleeping respiration monitoring and try to address the above challenges. Instead of focusing on developing complicated signal processing algorithms, we take another approach: via the deployment of WiFi transceivers. The key insight comes from the WiFi Fresnel zone model, which indicates that a carefully placed WiFi transceiver may only be affected by the person in a certain location. Furthermore, we consider the sleeping movements of people as well as the sleeping posture change to improve the robustness of the system. Extensive experiments show that we can successfully estimate the respiration rate of multiple persons, with the Mean Absolute Error (MAE) of 0.5 bpm - 1 bpm.
{"title":"Multi-person Sleeping Respiration Monitoring with COTS WiFi Devices","authors":"Yanni Yang, Jiannong Cao, Xuefeng Liu, Kai Xing","doi":"10.1109/MASS.2018.00017","DOIUrl":"https://doi.org/10.1109/MASS.2018.00017","url":null,"abstract":"Recently, non-intrusive respiration monitoring has attracted much attention. Many respiration monitoring systems using the commercial off-the-shelf WiFi devices have been developed. However, these systems mainly have difficulties in the presence of multiple persons. The difficulty generally comes from the separation of the effects of multiple persons' respiration on the received WiFi signals. Another problem is that even though the separation can be feasible with some complicated algorithms, it is still impossible to map the multiple identified respiration states to the corresponding persons. In this paper, we study the problem of multi-person sleeping respiration monitoring and try to address the above challenges. Instead of focusing on developing complicated signal processing algorithms, we take another approach: via the deployment of WiFi transceivers. The key insight comes from the WiFi Fresnel zone model, which indicates that a carefully placed WiFi transceiver may only be affected by the person in a certain location. Furthermore, we consider the sleeping movements of people as well as the sleeping posture change to improve the robustness of the system. Extensive experiments show that we can successfully estimate the respiration rate of multiple persons, with the Mean Absolute Error (MAE) of 0.5 bpm - 1 bpm.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128921817","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}
{"title":"Welcome Message from BigTrust 2018 Workshop Organizers","authors":"","doi":"10.1109/mass.2018.00010","DOIUrl":"https://doi.org/10.1109/mass.2018.00010","url":null,"abstract":"","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628153","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}
Chaochao Wang, Qin Xia, Xinwei Yao, Wanliang Wang, J. Jornet
Nanonetworks composed by communicating nano-devices enable new applications in the consumer, biomedical, and environmental fields. Three main characteristics introduce strict requirements for routing protocols design for nanonetworks, namely, short transmission range at Terahertz (THz) frequency (0.1-10 THz), fluctuations in the energy of nano-nodes due to the energy harvesting processes and very limited memory/buffer size of nano-nodes. In this paper, a multi-hop deflection routing algorithm based on Q-learning for energy-harvesting nanonetworks (MDRQEN) is proposed to guarantee the network energy efficiency, while ensuring a low packet loss probability. First, a deflection table is introduced to deflect the packets when the next hop nano-nodes are unavailable due to energy or memory/buffer constraints. Then, a Q-learning scheme is proposed to update the routing table and deflection table by utilizing the reward information contained in the forwarded packet from the previous nano-node. In the Q-learning update scheme, packet deflection ratio, packet loss ratio, packet hop count and node energy status of nano-nodes are taken into consideration. As numerically shown through extensive simulations in Network Simulator 3 (NS-3), the proposed MDRQEN algorithm can achieve a better packet delivery ratio and energy efficiency than random routing algorithm, flooding routing algorithm and the MDRQEN algorithm without the Q-learning update scheme.
{"title":"Multi-hop Deflection Routing Algorithm Based on Q-Learning for Energy-Harvesting Nanonetworks","authors":"Chaochao Wang, Qin Xia, Xinwei Yao, Wanliang Wang, J. Jornet","doi":"10.1109/MASS.2018.00059","DOIUrl":"https://doi.org/10.1109/MASS.2018.00059","url":null,"abstract":"Nanonetworks composed by communicating nano-devices enable new applications in the consumer, biomedical, and environmental fields. Three main characteristics introduce strict requirements for routing protocols design for nanonetworks, namely, short transmission range at Terahertz (THz) frequency (0.1-10 THz), fluctuations in the energy of nano-nodes due to the energy harvesting processes and very limited memory/buffer size of nano-nodes. In this paper, a multi-hop deflection routing algorithm based on Q-learning for energy-harvesting nanonetworks (MDRQEN) is proposed to guarantee the network energy efficiency, while ensuring a low packet loss probability. First, a deflection table is introduced to deflect the packets when the next hop nano-nodes are unavailable due to energy or memory/buffer constraints. Then, a Q-learning scheme is proposed to update the routing table and deflection table by utilizing the reward information contained in the forwarded packet from the previous nano-node. In the Q-learning update scheme, packet deflection ratio, packet loss ratio, packet hop count and node energy status of nano-nodes are taken into consideration. As numerically shown through extensive simulations in Network Simulator 3 (NS-3), the proposed MDRQEN algorithm can achieve a better packet delivery ratio and energy efficiency than random routing algorithm, flooding routing algorithm and the MDRQEN algorithm without the Q-learning update scheme.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129973989","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}
The recent development of network function virtualization decouples network functions from dedicated hardware. Thus, virtual network functions (VNFs) can be distributed onto shared and virtualized platforms held by multiple data centers in various network locations. The architecture of the distributed VNFs interconnected by virtual links is denoted as the Service Function Chain (SFC). SFC has the potential to significantly reduce the opening and operating cost of the network services while improving the flexibility. However, the availability of SFC on chained up data centers is always inferior to that of network functions running on a single data center because the failure in any data center on the chain may affect its availability. To improve the availabilities of SFCs, in this paper we propose a local rerouting strategy to bypass the failed data centers on SFCs using locally rerouted paths (LRPs). Furthermore, we formulate an optimization model to minimize the maximum load on links while deploying SFCs and LRPs into the network. Thus, we reduce the potential risk of congested links caused by the local rerouting strategy. We then propose a randomized rounding approximation algorithm to solve the optimization problem, preserving the competitive ratio of O(logn) for the model. We also propose a fast heuristic algorithm to improve the efficiency. Our extensive simulation results show that the proposed algorithms can provide highly available SFCs with more balanced link load.
网络功能虚拟化的最新发展将网络功能与专用硬件解耦。因此，虚拟网络功能(VNFs)可以分布到由不同网络位置的多个数据中心所拥有的共享和虚拟化平台上。通过虚拟链路相互连接的分布式VNFs的体系结构称为SFC (Service Function Chain)。在提高灵活性的同时，SFC有可能大幅降低网络服务的开放和运营成本。然而，SFC在链接数据中心上的可用性总是不如在单个数据中心上运行的网络功能，因为链上任何数据中心的故障都可能影响其可用性。为了提高sfc的可用性，本文提出了一种本地重路由策略，使用本地重路由路径(lrp)绕过sfc上故障的数据中心。此外，我们制定了一个优化模型，以最小化在网络中部署sfc和lrp时链路上的最大负载。因此，我们减少了由本地重路由策略引起的链路拥塞的潜在风险。然后，我们提出了一种随机四舍五入近似算法来解决优化问题，保持模型的竞争比为O(logn)。为了提高效率，我们还提出了一种快速的启发式算法。大量的仿真结果表明，所提出的算法可以提供高可用性的sfc，并且链路负载更加均衡。
{"title":"Placement of Highly Available Virtual Network Functions Through Local Rerouting","authors":"Xiaojun Shang, Zhenhua Li, Yuanyuan Yang","doi":"10.1109/MASS.2018.00022","DOIUrl":"https://doi.org/10.1109/MASS.2018.00022","url":null,"abstract":"The recent development of network function virtualization decouples network functions from dedicated hardware. Thus, virtual network functions (VNFs) can be distributed onto shared and virtualized platforms held by multiple data centers in various network locations. The architecture of the distributed VNFs interconnected by virtual links is denoted as the Service Function Chain (SFC). SFC has the potential to significantly reduce the opening and operating cost of the network services while improving the flexibility. However, the availability of SFC on chained up data centers is always inferior to that of network functions running on a single data center because the failure in any data center on the chain may affect its availability. To improve the availabilities of SFCs, in this paper we propose a local rerouting strategy to bypass the failed data centers on SFCs using locally rerouted paths (LRPs). Furthermore, we formulate an optimization model to minimize the maximum load on links while deploying SFCs and LRPs into the network. Thus, we reduce the potential risk of congested links caused by the local rerouting strategy. We then propose a randomized rounding approximation algorithm to solve the optimization problem, preserving the competitive ratio of O(logn) for the model. We also propose a fast heuristic algorithm to improve the efficiency. Our extensive simulation results show that the proposed algorithms can provide highly available SFCs with more balanced link load.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130253871","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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