Pub Date : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218553
Bo-Xian Wu, K. Lin, Kai-Cheng Hsu, Hung-Yu Wei
Multi-user MIMO (MU-MIMO) has recently been specified in wireless standards, e.g., LTE-Advance and 802.11ac, to allow an access point (AP) to transmit multiple unicast streams simultaneously to different clients. These protocols however have no specific mechanism for multicasting. Existing systems hence simply allow a single multicast transmission, as a result underutilizing the AP's multiple antennas. Even worse, in most of systems, multicast is by default sent at the base rate, wasting a considerable link margin available for delivering extra information. To address this inefficiency, we present the design and implementation of HybridCast, a MU-MIMO system that enables joint unicast and multicast. HybridCast efficiently leverages the unused MIMO capability and link margin to send unicast streams concurrently with a multicast session, while ensuring not to harm the achievable rate of multicasting. We evaluate the performance of HybridCast via both testbed experiments and simulations. The results show that HybridCast always outperforms single multicast transmission. The average throughput gain for 4-antenna AP scenarios is 6.22× and 1.54× when multicast is sent at the base rate and the best rate of the bottleneck receiver, respectively.
{"title":"HybridCast: Joint multicast-unicast design for multiuser MIMO networks","authors":"Bo-Xian Wu, K. Lin, Kai-Cheng Hsu, Hung-Yu Wei","doi":"10.1109/INFOCOM.2015.7218553","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218553","url":null,"abstract":"Multi-user MIMO (MU-MIMO) has recently been specified in wireless standards, e.g., LTE-Advance and 802.11ac, to allow an access point (AP) to transmit multiple unicast streams simultaneously to different clients. These protocols however have no specific mechanism for multicasting. Existing systems hence simply allow a single multicast transmission, as a result underutilizing the AP's multiple antennas. Even worse, in most of systems, multicast is by default sent at the base rate, wasting a considerable link margin available for delivering extra information. To address this inefficiency, we present the design and implementation of HybridCast, a MU-MIMO system that enables joint unicast and multicast. HybridCast efficiently leverages the unused MIMO capability and link margin to send unicast streams concurrently with a multicast session, while ensuring not to harm the achievable rate of multicasting. We evaluate the performance of HybridCast via both testbed experiments and simulations. The results show that HybridCast always outperforms single multicast transmission. The average throughput gain for 4-antenna AP scenarios is 6.22× and 1.54× when multicast is sent at the base rate and the best rate of the bottleneck receiver, respectively.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131851862","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218547
Tianci Liu, Lei Yang, Xiangyang Li, Huaiyi Huang, Yunhao Liu
To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of `deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.
{"title":"TagBooth: Deep shopping data acquisition powered by RFID tags","authors":"Tianci Liu, Lei Yang, Xiangyang Li, Huaiyi Huang, Yunhao Liu","doi":"10.1109/INFOCOM.2015.7218547","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218547","url":null,"abstract":"To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of `deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131903557","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218605
T. Jurdzinski, D. Kowalski, M. Różański, Grzegorz Stachowiak
This paper studies the task of setting up ad hoc wireless networks. In such networks, it is often the case that nodes become active at different times, without coordination or knowledge about network topology. We consider the following tasks: wake-up, clock synchronization, leader election, and multimessage broadcast. We show how to achieve these goals in scalable O(D polylog(n)) time. As a tool we define and give a solution to a quasi-backbone problem, which aims to set up transmission probabilities at nodes in a way that they can be efficiently used to solve other tasks. Our results are obtained by minimalistic algorithms, which do not require power control or carrier sensing capabilities, use very small energy, local computation and memory. Moreover, unlike many previous work, they remain scalable even if the network is not highly connected.
{"title":"On setting-up asynchronous ad hoc wireless networks","authors":"T. Jurdzinski, D. Kowalski, M. Różański, Grzegorz Stachowiak","doi":"10.1109/INFOCOM.2015.7218605","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218605","url":null,"abstract":"This paper studies the task of setting up ad hoc wireless networks. In such networks, it is often the case that nodes become active at different times, without coordination or knowledge about network topology. We consider the following tasks: wake-up, clock synchronization, leader election, and multimessage broadcast. We show how to achieve these goals in scalable O(D polylog(n)) time. As a tool we define and give a solution to a quasi-backbone problem, which aims to set up transmission probabilities at nodes in a way that they can be efficiently used to solve other tasks. Our results are obtained by minimalistic algorithms, which do not require power control or carrier sensing capabilities, use very small energy, local computation and memory. Moreover, unlike many previous work, they remain scalable even if the network is not highly connected.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131917674","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218488
Malhar Mehta, V. Kavitha, N. Hemachandra
When agents compete for common resource and when the utilities derived by them, upon allocation, are independent across the agents and time slots, an opportunistic scheduler is used. The instantaneous utility of one agent can be low, however few among many would have `good' utility with high probability. Opportunistic schedulers utilize these opportunities, allocate resource at any time to a `good' agent. Efficient schedulers maximize the sum of accumulated utilities. Thus, every time `best' agent is allocated. This can result in negligible (unfair) accumulations for some agents, whose instantaneous utilities are `low' with high probability. Fair opportunistic schedulers are thus introduced (e.g., alpha-fair schedulers). We study their price of fairness (PoF). We group the agents into finite classes, each class having identical utilities and QoS requirements. We study the asymptotic PoF as agents increase, while maintaining class-wise proportions constant. Asymptotic PoF is less than one, depends only upon the differences in the largest utilities of individual classes and is less than the maximum such normalized differences. The PoF is zero initially and increases with increase in fairness requirements to an upper bound strictly less than one. We observe that the fair schedulers are essentially priority schedulers, which facilitated easy analysis of PoF.
{"title":"Price of fairness for opportunistic and priority schedulers","authors":"Malhar Mehta, V. Kavitha, N. Hemachandra","doi":"10.1109/INFOCOM.2015.7218488","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218488","url":null,"abstract":"When agents compete for common resource and when the utilities derived by them, upon allocation, are independent across the agents and time slots, an opportunistic scheduler is used. The instantaneous utility of one agent can be low, however few among many would have `good' utility with high probability. Opportunistic schedulers utilize these opportunities, allocate resource at any time to a `good' agent. Efficient schedulers maximize the sum of accumulated utilities. Thus, every time `best' agent is allocated. This can result in negligible (unfair) accumulations for some agents, whose instantaneous utilities are `low' with high probability. Fair opportunistic schedulers are thus introduced (e.g., alpha-fair schedulers). We study their price of fairness (PoF). We group the agents into finite classes, each class having identical utilities and QoS requirements. We study the asymptotic PoF as agents increase, while maintaining class-wise proportions constant. Asymptotic PoF is less than one, depends only upon the differences in the largest utilities of individual classes and is less than the maximum such normalized differences. The PoF is zero initially and increases with increase in fairness requirements to an upper bound strictly less than one. We observe that the fair schedulers are essentially priority schedulers, which facilitated easy analysis of PoF.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464555","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218578
Jia Liu, Bin Xiao, Shigang Chen, F. Zhu, Lijun Chen
In RFID systems, the grouping problem is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on the unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of m tags simultaneously in multicast, which improves the efficiency by m times when comparing with unicast. We study fast grouping protocols in large RFID systems. To the best of our knowledge, it is the first attempt to tackle this practically important yet uninvestigated problem. We start with a straightforward solution called the Enhanced Polling Grouping (EPG) protocol. We then propose a time-efficient FIltering Grouping (FIG) protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping (CCG) protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.
{"title":"Fast RFID grouping protocols","authors":"Jia Liu, Bin Xiao, Shigang Chen, F. Zhu, Lijun Chen","doi":"10.1109/INFOCOM.2015.7218578","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218578","url":null,"abstract":"In RFID systems, the grouping problem is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on the unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of m tags simultaneously in multicast, which improves the efficiency by m times when comparing with unicast. We study fast grouping protocols in large RFID systems. To the best of our knowledge, it is the first attempt to tackle this practically important yet uninvestigated problem. We start with a straightforward solution called the Enhanced Polling Grouping (EPG) protocol. We then propose a time-efficient FIltering Grouping (FIG) protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping (CCG) protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133813112","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218429
Shimin Gong, Lingjie Duan, Ping Wang
We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.
{"title":"Robust optimization of cognitive radio networks powered by energy harvesting","authors":"Shimin Gong, Lingjie Duan, Ping Wang","doi":"10.1109/INFOCOM.2015.7218429","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218429","url":null,"abstract":"We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114546622","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218566
Chul-Ho Lee, Do Young Eun
The Metropolis-Hastings (MH) algorithm, in addition to its application for Markov Chain Monte Carlo sampling or simulation, has been popularly used for constructing a random walk that achieves a given, desired stationary distribution over a graph. Applications include crawling-based sampling of large graphs or online social networks, statistical estimation or inference from massive scale of networked data, efficient searching algorithms in unstructured peer-to-peer networks, randomized routing and movement strategies in wireless sensor networks, to list a few. Despite its versatility, the MH algorithm often causes self-transitions of its resulting random walk at some nodes, which is not efficient in the sense of the Peskun ordering - a partial order between off-diagonal elements of transition matrices of two different Markov chains, and in turn results in deficient performance in terms of asymptotic variance of time averages and expected hitting times with slower speed of convergence. To alleviate this problem, we present simple yet effective distributed algorithms that are guaranteed to improve the MH algorithm over time when running on a graph, and eventually reach `efficiency-optimality', while ensuring the same desired stationary distribution throughout.
{"title":"On the efficiency-optimal Markov chains for distributed networking applications","authors":"Chul-Ho Lee, Do Young Eun","doi":"10.1109/INFOCOM.2015.7218566","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218566","url":null,"abstract":"The Metropolis-Hastings (MH) algorithm, in addition to its application for Markov Chain Monte Carlo sampling or simulation, has been popularly used for constructing a random walk that achieves a given, desired stationary distribution over a graph. Applications include crawling-based sampling of large graphs or online social networks, statistical estimation or inference from massive scale of networked data, efficient searching algorithms in unstructured peer-to-peer networks, randomized routing and movement strategies in wireless sensor networks, to list a few. Despite its versatility, the MH algorithm often causes self-transitions of its resulting random walk at some nodes, which is not efficient in the sense of the Peskun ordering - a partial order between off-diagonal elements of transition matrices of two different Markov chains, and in turn results in deficient performance in terms of asymptotic variance of time averages and expected hitting times with slower speed of convergence. To alleviate this problem, we present simple yet effective distributed algorithms that are guaranteed to improve the MH algorithm over time when running on a graph, and eventually reach `efficiency-optimality', while ensuring the same desired stationary distribution throughout.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123017907","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218458
Mohammad Y. Hajjat, Ruiqi Liu, Yiyang Chang, T. Ng, Sanjay G. Rao
Provider policy (e.g., bandwidth rate limits, virtualization, CPU scheduling) can significantly impact application performance in cloud environments. This paper takes a first step towards understanding the impact of provider policy and tackling the complexity of selecting configurations that can best meet the cost and performance requirements of applications. We make three contributions. First, we conduct a measurement study spanning a 19 months period of a wide variety of applications on Amazon EC2 to understand issues involved in configuration selection. Our results show that provider policy can impact communication and computation performance in unpredictable ways. Moreover, seemingly sensible rules of thumb are inappropriate - e.g., VMs with latest hardware or larger VM sizes do not always provide the best performance. Second, we systematically characterize the overheads and resulting benefits of a range of testing strategies for configuration selection. A key focus of our characterization is understanding the overheads of a testing approach in the face of variability in performance across deployments and measurements. Finally, we present configuration pruning and short-listing techniques for minimizing testing overheads. Evaluations on a variety of compute, bandwidth and data intensive applications validate the effectiveness of these techniques in selecting good configurations with low overheads.
{"title":"Application-specific configuration selection in the cloud: Impact of provider policy and potential of systematic testing","authors":"Mohammad Y. Hajjat, Ruiqi Liu, Yiyang Chang, T. Ng, Sanjay G. Rao","doi":"10.1109/INFOCOM.2015.7218458","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218458","url":null,"abstract":"Provider policy (e.g., bandwidth rate limits, virtualization, CPU scheduling) can significantly impact application performance in cloud environments. This paper takes a first step towards understanding the impact of provider policy and tackling the complexity of selecting configurations that can best meet the cost and performance requirements of applications. We make three contributions. First, we conduct a measurement study spanning a 19 months period of a wide variety of applications on Amazon EC2 to understand issues involved in configuration selection. Our results show that provider policy can impact communication and computation performance in unpredictable ways. Moreover, seemingly sensible rules of thumb are inappropriate - e.g., VMs with latest hardware or larger VM sizes do not always provide the best performance. Second, we systematically characterize the overheads and resulting benefits of a range of testing strategies for configuration selection. A key focus of our characterization is understanding the overheads of a testing approach in the face of variability in performance across deployments and measurements. Finally, we present configuration pruning and short-listing techniques for minimizing testing overheads. Evaluations on a variety of compute, bandwidth and data intensive applications validate the effectiveness of these techniques in selecting good configurations with low overheads.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122724906","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}
With increasing popularity of location-based services (LBSs), there have been growing concerns for location privacy. To protect location privacy in a LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this study, we leverage the social tie structure among mobile users to motivate them to participate in pseudonym change. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to change pseudonyms as a socially-aware pseudonym change game (PCG). The PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SGUM-based PCG, we show that there exists a socially-aware Nash equilibrium (SNE), and quantify the system efficiency of the SNE with respect to the optimal social welfare. Then we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. It turns out that this algorithm can efficiently find a Pareto-optimal SNE with social welfare higher than that for the socially-oblivious PCG, pointing out the impact of exploiting social tie structure. We further show that the Pareto-optimal SNE can be achieved in a distributed manner.
随着基于位置的服务(lbs)的日益普及，人们越来越关注位置隐私。为了保护LBS中的位置隐私，物理上接近的移动用户可以协同工作，集体更改他们的假名，以隐藏他们位置痕迹中的时空相关性。在本研究中，我们利用移动用户之间的社会关系结构来激励他们参与假名更改。利用社会群体效用最大化(social group utility maximization, SGUM)框架，我们将用户是否更改假名的决策视为社会意识假名更改游戏(social -aware pseudonym change game, PCG)。PCG进一步假设了一个通用匿名模型，该模型允许用户为个性化位置隐私设置特定的匿名集。对于基于sgum的PCG，我们证明存在社会意识纳什均衡(SNE)，并量化了SNE相对于最优社会福利的系统效率。然后，我们开发了一种贪婪算法，该算法基于仅从策略已确定的用户中获得的社会群体效用来近视地确定用户的策略。结果表明，该算法可以有效地找到社会福利高于社会无关PCG的帕累托最优SNE，指出了利用社会关系结构的影响。我们进一步证明了pareto最优SNE可以在分布式方式下实现。
{"title":"Personalized location privacy in mobile networks: A social group utility approach","authors":"Xiaowen Gong, Xu Chen, Kai Xing, Dong-Hoon Shin, Mengyuan Zhang, Junshan Zhang","doi":"10.1109/INFOCOM.2015.7218473","DOIUrl":"https://doi.org/10.1109/INFOCOM.2015.7218473","url":null,"abstract":"With increasing popularity of location-based services (LBSs), there have been growing concerns for location privacy. To protect location privacy in a LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this study, we leverage the social tie structure among mobile users to motivate them to participate in pseudonym change. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to change pseudonyms as a socially-aware pseudonym change game (PCG). The PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SGUM-based PCG, we show that there exists a socially-aware Nash equilibrium (SNE), and quantify the system efficiency of the SNE with respect to the optimal social welfare. Then we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. It turns out that this algorithm can efficiently find a Pareto-optimal SNE with social welfare higher than that for the socially-oblivious PCG, pointing out the impact of exploiting social tie structure. We further show that the Pareto-optimal SNE can be achieved in a distributed manner.","PeriodicalId":342583,"journal":{"name":"2015 IEEE Conference on Computer Communications (INFOCOM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122851120","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 : 2015-08-24DOI: 10.1109/INFOCOM.2015.7218658
Zijiang Hao, Yutao Tang, Yifan Zhang, Ed Novak, Nancy Carter, Qun A. Li
Mobile devices are now ubiquitous in the modern world. In this paper, we propose a novel and practical mobile-cloud platform for smart mobile devices. Our platform allows users to run the entire mobile device operating system and arbitrary applications on a cloud-based virtual machine. It has two design fundamentals. First, applications can freely migrate between the user's mobile device and a backend cloud server. We design a file system extension to enable this feature, so users can freely choose to run their applications either in the cloud (for high security guarantees), or on their local mobile device (for better user experience). Second, in order to protect user data on the smart mobile device, we leverage hardware virtualization technology, which isolates the data from the local mobile device operating system. We have implemented a prototype of our platform using off-the-shelf hardware, and performed an extensive evaluation of it. We show that our platform is efficient, practical, and secure.
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