Pub Date : 2020-11-16DOI: 10.1109/LCN48667.2020.9314850
Samuel Brack, Leonie Reichert, B. Scheuermann
Contact tracing is a promising approach to combat the COVID-19 pandemic. Various systems have been proposed to automatise the process. Many designs rely heavily on a centralised server or reveal significant amounts of private data to health authorities. We propose CAUDHT, a decentralized peer-to-peer system for contact tracing. The central health authority can focus on providing and operating tests for the disease while contact tracing is done by the system’s users themselves. We use a distributed hash table to build a decentral messaging system for infected patients and their contacts. With blind signatures, we ensure that messages about infections are authentic and unchanged. A strong privacy focus enables data integrity, confidentiality, and privacy.
{"title":"CAUDHT: Decentralized Contact Tracing Using a DHT and Blind Signatures","authors":"Samuel Brack, Leonie Reichert, B. Scheuermann","doi":"10.1109/LCN48667.2020.9314850","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314850","url":null,"abstract":"Contact tracing is a promising approach to combat the COVID-19 pandemic. Various systems have been proposed to automatise the process. Many designs rely heavily on a centralised server or reveal significant amounts of private data to health authorities. We propose CAUDHT, a decentralized peer-to-peer system for contact tracing. The central health authority can focus on providing and operating tests for the disease while contact tracing is done by the system’s users themselves. We use a distributed hash table to build a decentral messaging system for infected patients and their contacts. With blind signatures, we ensure that messages about infections are authentic and unchanged. A strong privacy focus enables data integrity, confidentiality, and privacy.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932910","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314816
Sina Rafati Niya, Benjamin Jeffrey, B. Stiller
The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.
{"title":"KYoT: Self-sovereign IoT Identification with a Physically Unclonable Function","authors":"Sina Rafati Niya, Benjamin Jeffrey, B. Stiller","doi":"10.1109/LCN48667.2020.9314816","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314816","url":null,"abstract":"The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125959563","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314852
Cheng-Hua Lee, H. Nakazato
Named Data Networking (NDN) is a new networking mechanism to amend problems in the current Internet. There are many features to improve the efficiency of network in NDN, and one of them is that routers can cache data conveyed by packets. This results in network node to client communication unlike the client to client principle in TCP/IP architecture. Therefore, end-to-end flow control cannot be applied to NDN unlike the Internet. Inspired from thermal diffusion, researchers proposed hop-by-hop flow control to solve this problem. However, the congestion control in hop-by-hop method is still under research. In this paper, a diffusion-based flow control method for NDN is proposed. We conducted simulations to verify its performance.
{"title":"Congestion Control Using Diffusion Method in Named Data Networking","authors":"Cheng-Hua Lee, H. Nakazato","doi":"10.1109/LCN48667.2020.9314852","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314852","url":null,"abstract":"Named Data Networking (NDN) is a new networking mechanism to amend problems in the current Internet. There are many features to improve the efficiency of network in NDN, and one of them is that routers can cache data conveyed by packets. This results in network node to client communication unlike the client to client principle in TCP/IP architecture. Therefore, end-to-end flow control cannot be applied to NDN unlike the Internet. Inspired from thermal diffusion, researchers proposed hop-by-hop flow control to solve this problem. However, the congestion control in hop-by-hop method is still under research. In this paper, a diffusion-based flow control method for NDN is proposed. We conducted simulations to verify its performance.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121611727","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314808
Nuray Baltaci Akhuseyinoglu, Maryam Karimi, Mai Abdelhakim, P. Krishnamurthy
Developing automated trust mechanisms has become crucial for overcoming perceptions of uncertainty and risk by people using IoT services. Things are increasingly communicating with each other and trust in the data they deliver depends on several factors such as the links they use to communicate and the environment. This points to a need for a trust management method for "things" that considers the communication among them, environmental and security-related factors, and the net-work topology but without human intervention. To address these challenges, we propose a trust management framework that automatically computes the trust of "things". We use Multi-Attribute Decision Making (MADM) and Evidence-Based Subjective Logic (EBSL) in a trust network of "things" to take into account the uncertainty in trust values. We propose new normalization for non-monotonic attributes in MADM. We present an algorithm for automatic trust computation and evaluate its effectiveness using synthetic data and sampling from real datasets.
{"title":"On Automated Trust Computation in IoT with Multiple Attributes and Subjective Logic","authors":"Nuray Baltaci Akhuseyinoglu, Maryam Karimi, Mai Abdelhakim, P. Krishnamurthy","doi":"10.1109/LCN48667.2020.9314808","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314808","url":null,"abstract":"Developing automated trust mechanisms has become crucial for overcoming perceptions of uncertainty and risk by people using IoT services. Things are increasingly communicating with each other and trust in the data they deliver depends on several factors such as the links they use to communicate and the environment. This points to a need for a trust management method for \"things\" that considers the communication among them, environmental and security-related factors, and the net-work topology but without human intervention. To address these challenges, we propose a trust management framework that automatically computes the trust of \"things\". We use Multi-Attribute Decision Making (MADM) and Evidence-Based Subjective Logic (EBSL) in a trust network of \"things\" to take into account the uncertainty in trust values. We propose new normalization for non-monotonic attributes in MADM. We present an algorithm for automatic trust computation and evaluate its effectiveness using synthetic data and sampling from real datasets.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129188101","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314810
A. Sen, K. Sivalingam
Rate adaptation (RA) is used in IEEE 802.11 WLANs to determine the optimal datarate for a particular channel condition. It becomes especially difficult to determine the optimal datarate for the new High-Throughput WLANs since the number of available datarates in these standards are very high. Moreover, a mobile environment poses additional challenge in RA as the channel conditions will keep on changing from time to time. In this paper, we propose a Contextual Bandits based Rate Adaptation (ContRA) algorithm for mobile users in IEEE 802.11ac standard. Based on the Received Signal Strength Indicator (RSSI) range that the receiver is currently in, the RA algorithm tries to determine the optimal rate from the rate set suitable for packet transmission in that RSSI range. Performance studies show that the proposed RA algorithm is able to adapt to changing channel conditions and quickly choose a suitable datarate for those channel conditions.
速率自适应(RA)在IEEE 802.11 wlan中用于确定特定信道条件下的最佳数据速率。由于这些标准中可用数据的数量非常高，因此确定新的高吞吐量wlan的最佳数据变得特别困难。此外，由于信道条件不断变化，移动环境对RA提出了额外的挑战。在本文中，我们提出了一种基于上下文强盗速率自适应(ContRA)的IEEE 802.11ac标准移动用户算法。RA算法根据接收方当前所处的RSSI (Received Signal Strength Indicator)范围，尝试从该RSSI范围内适合数据包传输的速率集中确定最优速率。性能研究表明，所提出的RA算法能够适应不断变化的信道条件，并快速选择适合这些信道条件的数据。
{"title":"Rate Adaptation Techniques Using Contextual Bandit Approach for Mobile Wireless LAN Users","authors":"A. Sen, K. Sivalingam","doi":"10.1109/LCN48667.2020.9314810","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314810","url":null,"abstract":"Rate adaptation (RA) is used in IEEE 802.11 WLANs to determine the optimal datarate for a particular channel condition. It becomes especially difficult to determine the optimal datarate for the new High-Throughput WLANs since the number of available datarates in these standards are very high. Moreover, a mobile environment poses additional challenge in RA as the channel conditions will keep on changing from time to time. In this paper, we propose a Contextual Bandits based Rate Adaptation (ContRA) algorithm for mobile users in IEEE 802.11ac standard. Based on the Received Signal Strength Indicator (RSSI) range that the receiver is currently in, the RA algorithm tries to determine the optimal rate from the rate set suitable for packet transmission in that RSSI range. Performance studies show that the proposed RA algorithm is able to adapt to changing channel conditions and quickly choose a suitable datarate for those channel conditions.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129267222","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314853
M. Arghavani, Haibo Zhang, D. Eyers, Abbas Arghavani
TCP slow-start grows the congestion window exponentially, aims to quickly probe the throughput of the network path. Stopping this growth at the wrong time can affect the overall network performance. In this paper, we introduce StopEG, an efficient mechanism to accurately and quickly detect when to stop this exponential growth. StopEG reacts to the changes on congestion window size rather than traditional congestion signals such as packet loss. We show that theoretically the number of inflight packets in the forward path is no more than 56.8% of all the inflight packets when the bottleneck link is unsaturated, and use this value as the threshold to stop the exponential growth. StopEG is evaluated through simulations in ns-3 by incorporating it into Google’s BBR congestion control algorithm. Simulation results demonstrate its effectiveness in BBR, with a reduction of ≈68% in the length of the bottleneck queue when new connections are initiated.
{"title":"StopEG: Detecting when to stop exponential growth in TCP slow-start","authors":"M. Arghavani, Haibo Zhang, D. Eyers, Abbas Arghavani","doi":"10.1109/LCN48667.2020.9314853","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314853","url":null,"abstract":"TCP slow-start grows the congestion window exponentially, aims to quickly probe the throughput of the network path. Stopping this growth at the wrong time can affect the overall network performance. In this paper, we introduce StopEG, an efficient mechanism to accurately and quickly detect when to stop this exponential growth. StopEG reacts to the changes on congestion window size rather than traditional congestion signals such as packet loss. We show that theoretically the number of inflight packets in the forward path is no more than 56.8% of all the inflight packets when the bottleneck link is unsaturated, and use this value as the threshold to stop the exponential growth. StopEG is evaluated through simulations in ns-3 by incorporating it into Google’s BBR congestion control algorithm. Simulation results demonstrate its effectiveness in BBR, with a reduction of ≈68% in the length of the bottleneck queue when new connections are initiated.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906420","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314782
I. Fink, Martin Serror, Klaus Wehrle
The tremendous success of the IoT is overshadowed by severe security risks introduced by IoT devices and smartphone apps to control them. Therefore, academia and industry increasingly acknowledge the use of in-network security approaches, such as IETF Manufacturer Usage Description (MUD), to restrict undesired communication. However, actual communication patterns of smart homes are not sufficiently covered by such policy-based approaches. In this paper, we propose to enforce MUD on authenticated smartphones to efficiently filter malicious traffic close to its origin and hinder further spreading. Such enforcement allows us to successfully mitigate the threat of malicious apps and IoT devices in smart home networks.
{"title":"Extending MUD to Smartphones","authors":"I. Fink, Martin Serror, Klaus Wehrle","doi":"10.1109/LCN48667.2020.9314782","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314782","url":null,"abstract":"The tremendous success of the IoT is overshadowed by severe security risks introduced by IoT devices and smartphone apps to control them. Therefore, academia and industry increasingly acknowledge the use of in-network security approaches, such as IETF Manufacturer Usage Description (MUD), to restrict undesired communication. However, actual communication patterns of smart homes are not sufficiently covered by such policy-based approaches. In this paper, we propose to enforce MUD on authenticated smartphones to efficiently filter malicious traffic close to its origin and hinder further spreading. Such enforcement allows us to successfully mitigate the threat of malicious apps and IoT devices in smart home networks.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131998983","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314818
S. Jafarizadeh
Complex networks of coupled dynamical systems, are effective models of numerous distributed systems. The desire to control such systems has led to the pinning control techniques where a subset of nodes in the network is controlled. A problem of interest is to optimize the pinning node selection and control gain design, while minimizing the associated total control cost. Here this optimal control optimization problem has been reformulated as a standard semidefinite programming problem. Solving the resultant problem, the analytical solution for the optimal feedback gain and pinning nodes are derived. An algorithm for determining the optimal feedback gain for a set of pinned nodes is developed. For a number of topologies, closed-forms of the optimal results are provided. Interestingly, increasing the number of pinned nodes in the network reduces the total pinning cost, i.e., the performance index. For a network of Lorenz systems, optimal results for all possible pinning node selections are provided.
{"title":"Pinning Control of Dynamical Networks with Optimal Cost","authors":"S. Jafarizadeh","doi":"10.1109/LCN48667.2020.9314818","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314818","url":null,"abstract":"Complex networks of coupled dynamical systems, are effective models of numerous distributed systems. The desire to control such systems has led to the pinning control techniques where a subset of nodes in the network is controlled. A problem of interest is to optimize the pinning node selection and control gain design, while minimizing the associated total control cost. Here this optimal control optimization problem has been reformulated as a standard semidefinite programming problem. Solving the resultant problem, the analytical solution for the optimal feedback gain and pinning nodes are derived. An algorithm for determining the optimal feedback gain for a set of pinned nodes is developed. For a number of topologies, closed-forms of the optimal results are provided. Interestingly, increasing the number of pinned nodes in the network reduces the total pinning cost, i.e., the performance index. For a network of Lorenz systems, optimal results for all possible pinning node selections are provided.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126157679","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 : 2020-11-16DOI: 10.1109/lcn48667.2020.9314792
{"title":"Technical Program of LCN 2020","authors":"","doi":"10.1109/lcn48667.2020.9314792","DOIUrl":"https://doi.org/10.1109/lcn48667.2020.9314792","url":null,"abstract":"","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715156","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314857
Salma Matoussi, Ilhem Fajjari, N. Aitsaadi, R. Langar
Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.
{"title":"Deep Learning based User Slice Allocation in 5G Radio Access Networks","authors":"Salma Matoussi, Ilhem Fajjari, N. Aitsaadi, R. Langar","doi":"10.1109/LCN48667.2020.9314857","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314857","url":null,"abstract":"Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122751339","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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