Debraj De, Wenzhan Song, Mingsen Xu, Cheng-Liang Wang, D. Cook, X. Huo
In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.
本文提出并设计了一种智能环境下的实时用户跟踪系统——Finding Human Motion (Finding Human Motion)。FindingHuMo可以从非侵入性和匿名(非用户特定)二进制运动传感器数据流中对走廊环境中的多个(未知和可变数量)用户执行无设备跟踪。本设计系统的意义在于:(a)从基础设施中静态无线传感器网络的二进制运动数据流中快速跟踪单个目标。这需要解决不可靠的节点序列，系统噪声和路径模糊。(b)多用户跟踪的缩放，其中用户运动轨迹可能以所有可能的方式相互交叉。这需要解决路径歧义以隔离重叠轨迹，FindingHumo在收集的运动数据流上应用以下技术:(i)提出的运动数据驱动的自适应阶隐马尔可夫模型与Viterbi解码(称为adaptive - hmm)，然后(ii)创新的路径消歧算法(称为CPDA)。使用这种方法，系统可以准确地检测和隔离单个用户的运动轨迹。系统性能用智能环境下实时系统部署经验的结果来说明。
{"title":"FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments","authors":"Debraj De, Wenzhan Song, Mingsen Xu, Cheng-Liang Wang, D. Cook, X. Huo","doi":"10.1109/ICDCS.2012.76","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.76","url":null,"abstract":"In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"25 6 1","pages":"163-172"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88194981","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}
Cloud resources are usually priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three pricing schemes -- the subscription option (a.k.a., Reserved Instances), the pay-as-you-go offer (a.k.a., On-Demand Instances), and an auction-like spot market (a.k.a., Spot Instances) -- simultaneously. There arises a new problem of capacity segmentation: how can a provider allocate resources to different categories of pricing schemes, so that the total revenue is maximized? In this paper, we consider an EC2-like pricing scheme with traditional pay-as-you-go pricing augmented by an auction market, where bidders periodically bid for resources and can use the instances for as long as they wish, until the clearing price exceeds their bids. We show that optimal periodic auctions must follow the design of m+1-price auction with seller's reservation price. Theoretical analysis also suggests the connections between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a Markov decision process over some demand prediction window. To mitigate the high computational complexity of the conventional dynamic programming solution, we develop a near-optimal solution that has significantly lower complexity and is shown to asymptotically approach the optimal revenue.
{"title":"Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing","authors":"Wei Wang, Baochun Li, B. Liang","doi":"10.1109/ICDCS.2012.52","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.52","url":null,"abstract":"Cloud resources are usually priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three pricing schemes -- the subscription option (a.k.a., Reserved Instances), the pay-as-you-go offer (a.k.a., On-Demand Instances), and an auction-like spot market (a.k.a., Spot Instances) -- simultaneously. There arises a new problem of capacity segmentation: how can a provider allocate resources to different categories of pricing schemes, so that the total revenue is maximized? In this paper, we consider an EC2-like pricing scheme with traditional pay-as-you-go pricing augmented by an auction market, where bidders periodically bid for resources and can use the instances for as long as they wish, until the clearing price exceeds their bids. We show that optimal periodic auctions must follow the design of m+1-price auction with seller's reservation price. Theoretical analysis also suggests the connections between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a Markov decision process over some demand prediction window. To mitigate the high computational complexity of the conventional dynamic programming solution, we develop a near-optimal solution that has significantly lower complexity and is shown to asymptotically approach the optimal revenue.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"16 1","pages":"425-434"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81536112","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}
Rahul Singh, P. Shenoy, K. Ramakrishnan, R. Kelkar, H. Vin
Modern day enterprises have a large IT infrastructure comprising thousands of applications running on servers housed in tens of data centers geographically spread out. These enterprises periodically perform a transformation of their entire IT infrastructure to simplify, decrease operational costs and enable easier management. However, the large number of different kinds of applications and data centers involved and the variety of constraints make the task of data center transformation challenging. The state-of-the-art technique for performing this transformation is simplistic, often unable to account for all but the simplest of constraints. We present eTransform, a system for generating a transformation and consolidation plan for the IT infrastructure of large scale enterprises. We devise a linear programming based approach that simultaneously optimizes all the costs involved in enterprise data centers taking into account the constraints of applications groups. Our algorithm handles the various idiosyncrasies of enterprise data centers like volume discounts in pricing, wide-area network costs, traffic matrices, latency constraints, distribution of users accessing the data etc. We include a disaster recovery (DR) plan, so that eTransform, thus provides an integrated disaster recovery and consolidation plan to transform the enterprise IT infrastructure. We use eTransform to perform case studies based on real data from three different large scale enterprises. In our experiments, eTransform is able to suggest a plan to reduce the operational costs by more than 50% from the "as-is" state of these enterprise to the consolidated enterprise IT environment. Even including the DR capability, eTransform is still able to reduce the operational costs by more than 25% from the simple "as-is" state. In our experiments, eTransform is able to simultaneously optimize multiple parameters and constraints and discover solutions that are 7x cheaper than other solutions.
{"title":"eTransform: Transforming Enterprise Data Centers by Automated Consolidation","authors":"Rahul Singh, P. Shenoy, K. Ramakrishnan, R. Kelkar, H. Vin","doi":"10.1109/ICDCS.2012.54","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.54","url":null,"abstract":"Modern day enterprises have a large IT infrastructure comprising thousands of applications running on servers housed in tens of data centers geographically spread out. These enterprises periodically perform a transformation of their entire IT infrastructure to simplify, decrease operational costs and enable easier management. However, the large number of different kinds of applications and data centers involved and the variety of constraints make the task of data center transformation challenging. The state-of-the-art technique for performing this transformation is simplistic, often unable to account for all but the simplest of constraints. We present eTransform, a system for generating a transformation and consolidation plan for the IT infrastructure of large scale enterprises. We devise a linear programming based approach that simultaneously optimizes all the costs involved in enterprise data centers taking into account the constraints of applications groups. Our algorithm handles the various idiosyncrasies of enterprise data centers like volume discounts in pricing, wide-area network costs, traffic matrices, latency constraints, distribution of users accessing the data etc. We include a disaster recovery (DR) plan, so that eTransform, thus provides an integrated disaster recovery and consolidation plan to transform the enterprise IT infrastructure. We use eTransform to perform case studies based on real data from three different large scale enterprises. In our experiments, eTransform is able to suggest a plan to reduce the operational costs by more than 50% from the \"as-is\" state of these enterprise to the consolidated enterprise IT environment. Even including the DR capability, eTransform is still able to reduce the operational costs by more than 25% from the simple \"as-is\" state. In our experiments, eTransform is able to simultaneously optimize multiple parameters and constraints and discover solutions that are 7x cheaper than other solutions.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"4 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77958429","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}
Although battery capacities keep increasing, the execution demands of modern mobile devices continue to outstrip their battery lives. As a result, battery life is bound to remain a key constraining factor in the design of mobile applications. To save battery power, mobile applications are often partitioned to offload parts of their execution to a remote server. However, partitioning an application renders it unusable in the face of network outages. In this paper, we present a novel approach that reduces the energy consumption of mobile applications through server offloading without partitioning. The functionality that consumes energy heavily is executed in the cloud, with the program's state check pointed and transferred across the mobile device and the cloud. Our approach is portable, as it introduces the offloading functionality through byte code enhancement, without any changes to the runtime system. The check pointed state's size is minimized through program analysis. In the case of a network outage, the offloading interrupts and the application reverts to executing locally from the latest checkpoint. Our case studies demonstrate how our approach can reduce energy consumption for third-party Android applications. Transformed through our approach, the applications consume between 30% and 60% fewer Joules than their original versions. Our results indicate that portable offloading can improve the battery life of modern mobile applications while maintaining their resilience to network outages.
{"title":"Energy-Efficient and Fault-Tolerant Distributed Mobile Execution","authors":"Young-Woo Kwon, E. Tilevich","doi":"10.1109/ICDCS.2012.75","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.75","url":null,"abstract":"Although battery capacities keep increasing, the execution demands of modern mobile devices continue to outstrip their battery lives. As a result, battery life is bound to remain a key constraining factor in the design of mobile applications. To save battery power, mobile applications are often partitioned to offload parts of their execution to a remote server. However, partitioning an application renders it unusable in the face of network outages. In this paper, we present a novel approach that reduces the energy consumption of mobile applications through server offloading without partitioning. The functionality that consumes energy heavily is executed in the cloud, with the program's state check pointed and transferred across the mobile device and the cloud. Our approach is portable, as it introduces the offloading functionality through byte code enhancement, without any changes to the runtime system. The check pointed state's size is minimized through program analysis. In the case of a network outage, the offloading interrupts and the application reverts to executing locally from the latest checkpoint. Our case studies demonstrate how our approach can reduce energy consumption for third-party Android applications. Transformed through our approach, the applications consume between 30% and 60% fewer Joules than their original versions. Our results indicate that portable offloading can improve the battery life of modern mobile applications while maintaining their resilience to network outages.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"206 1","pages":"586-595"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83190418","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}
Jeff Seibert, Sheila Becker, C. Nita-Rotaru, R. State
Virtual coordinate systems (VCS) provide accurate estimations of latency between arbitrary hosts on a network, while conducting a small amount of actual measurements and relying on node cooperation. While these systems have good accuracy under benign settings, they suffer a severe decrease of their effectiveness when under attack by compromised nodes acting as insider attackers. Previous defenses mitigate such attacks by using machine learning techniques to differentiate good behavior (learned over time) from bad behavior. However, these defense schemes have been shown to be vulnerable to advanced attacks that make the schemes learn malicious behavior as good behavior. We present Newton, a decentralized VCS that is robust to a wide class of insider attacks. Newton uses an abstraction of a real-life physical system, similar to that of Vivaldi, but in addition uses safety invariants derived from Newton's laws of motion. As a result, Newton does not need to learn good behavior and can tolerate a significantly higher percentage of malicious nodes. We show through simulations and real-world experiments on the Planet Lab test bed that Newton is able to mitigate all known attacks against VCS while providing better accuracy than Vivaldi, even in benign settings.
{"title":"Securing Virtual Coordinates by Enforcing Physical Laws","authors":"Jeff Seibert, Sheila Becker, C. Nita-Rotaru, R. State","doi":"10.1109/ICDCS.2012.22","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.22","url":null,"abstract":"Virtual coordinate systems (VCS) provide accurate estimations of latency between arbitrary hosts on a network, while conducting a small amount of actual measurements and relying on node cooperation. While these systems have good accuracy under benign settings, they suffer a severe decrease of their effectiveness when under attack by compromised nodes acting as insider attackers. Previous defenses mitigate such attacks by using machine learning techniques to differentiate good behavior (learned over time) from bad behavior. However, these defense schemes have been shown to be vulnerable to advanced attacks that make the schemes learn malicious behavior as good behavior. We present Newton, a decentralized VCS that is robust to a wide class of insider attacks. Newton uses an abstraction of a real-life physical system, similar to that of Vivaldi, but in addition uses safety invariants derived from Newton's laws of motion. As a result, Newton does not need to learn good behavior and can tolerate a significantly higher percentage of malicious nodes. We show through simulations and real-world experiments on the Planet Lab test bed that Newton is able to mitigate all known attacks against VCS while providing better accuracy than Vivaldi, even in benign settings.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"77 1","pages":"315-324"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88178969","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}
Xingyu Ma, Chengchen Hu, Kai Chen, Che Zhang, Hongtao Zhang, K. Zheng, Yan Chen, Xianda Sun
Address auto-configuration is a key problem in data center networks, where servers and switches encode topology information into their addresses for routing. A recent work DAC [2] has been introduced to address this problem. Without malfunctions, DAC can auto-configure all the devices quickly. But in case of malfunctions, DAC requires significant human efforts to correct malfunctions and it can cause substantial operation delay of the whole data center. In this paper, we further optimize address auto-configuration process even in the presence of malfunctions. Instead of waiting for all the malfunctions to be corrected, we could first configure the devices that are not involved in malfunctions and let them work first. This idea can be translated to considerable practical benefits because in most cases malfunctions in data centers only account for a very small portion. To realize the idea, we conceptually remove the malfunctions from the physical data center topology graph and mathematically convert the address configuration problem into induced sub graph isomorphism problem, which is NP-complete. We then introduce an algorithm that can solve the induced sub graph isomorphism quickly by taking advantage of data center topology characteristics and induced sub graph properties. We extensively evaluate our design on representative data center structures with various malfunction scenarios. The evaluation results demonstrate that the proposed framework and algorithm are efficient and labor-free to deal with the mapping task in the presence of error devices.
{"title":"Error Tolerant Address Configuration for Data Center Networks with Malfunctioning Devices","authors":"Xingyu Ma, Chengchen Hu, Kai Chen, Che Zhang, Hongtao Zhang, K. Zheng, Yan Chen, Xianda Sun","doi":"10.1109/ICDCS.2012.27","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.27","url":null,"abstract":"Address auto-configuration is a key problem in data center networks, where servers and switches encode topology information into their addresses for routing. A recent work DAC [2] has been introduced to address this problem. Without malfunctions, DAC can auto-configure all the devices quickly. But in case of malfunctions, DAC requires significant human efforts to correct malfunctions and it can cause substantial operation delay of the whole data center. In this paper, we further optimize address auto-configuration process even in the presence of malfunctions. Instead of waiting for all the malfunctions to be corrected, we could first configure the devices that are not involved in malfunctions and let them work first. This idea can be translated to considerable practical benefits because in most cases malfunctions in data centers only account for a very small portion. To realize the idea, we conceptually remove the malfunctions from the physical data center topology graph and mathematically convert the address configuration problem into induced sub graph isomorphism problem, which is NP-complete. We then introduce an algorithm that can solve the induced sub graph isomorphism quickly by taking advantage of data center topology characteristics and induced sub graph properties. We extensively evaluate our design on representative data center structures with various malfunction scenarios. The evaluation results demonstrate that the proposed framework and algorithm are efficient and labor-free to deal with the mapping task in the presence of error devices.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"1 1","pages":"708-717"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87262977","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}
A. Arefin, Zixia Huang, K. Nahrstedt, Pooja Agarwal
3D Tele-immersive systems create real-time multi-stream and multi-view 3D collaborative contents from multiple sites to allow interactive shared activities in virtual environments. Applications of 3DTI include online sports, tele-health, remote learning and collaborative arts. In addition to interactive participants in 3DTI environments, we envision a large number of passive non-interactive viewers that (a) watch the interactive activities in 3DTI shared environments, and (b) select views of the activities at run time. To achieve this vision, we present 4D Tele Cast, a novel multi-stream 3D content distribution framework for non-interactive viewers providing the functionality of multi-view selection. It addresses the following challenges: (1) supporting a large number of concurrent multi-stream viewers as well as multi-views, (2) preserving the unique nature of 3DTI multi-stream and multi-view dependencies at the viewers, and (3) allowing dynamic viewer behavior such as view changes and large-scale simultaneous viewer arrivals or departures. We divide the problem space into two: (1) multi-stream overlay construction problem that aims to minimize the cost of distribution of multi-stream contents, and maximize the number of concurrent viewers with sufficient viewer dynamism in terms of their resources and availabilities, and (2) effective resource utilization problem that aims to preserve the multi-stream dependencies in a view considering the heterogeneous resource constraints at the viewers. We evaluate 4D Tele Cast using extensive simulations with 3DTI activity data and Planet Lab traces.
{"title":"4D TeleCast: Towards Large Scale Multi-site and Multi-view Dissemination of 3DTI Contents","authors":"A. Arefin, Zixia Huang, K. Nahrstedt, Pooja Agarwal","doi":"10.1109/ICDCS.2012.58","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.58","url":null,"abstract":"3D Tele-immersive systems create real-time multi-stream and multi-view 3D collaborative contents from multiple sites to allow interactive shared activities in virtual environments. Applications of 3DTI include online sports, tele-health, remote learning and collaborative arts. In addition to interactive participants in 3DTI environments, we envision a large number of passive non-interactive viewers that (a) watch the interactive activities in 3DTI shared environments, and (b) select views of the activities at run time. To achieve this vision, we present 4D Tele Cast, a novel multi-stream 3D content distribution framework for non-interactive viewers providing the functionality of multi-view selection. It addresses the following challenges: (1) supporting a large number of concurrent multi-stream viewers as well as multi-views, (2) preserving the unique nature of 3DTI multi-stream and multi-view dependencies at the viewers, and (3) allowing dynamic viewer behavior such as view changes and large-scale simultaneous viewer arrivals or departures. We divide the problem space into two: (1) multi-stream overlay construction problem that aims to minimize the cost of distribution of multi-stream contents, and maximize the number of concurrent viewers with sufficient viewer dynamism in terms of their resources and availabilities, and (2) effective resource utilization problem that aims to preserve the multi-stream dependencies in a view considering the heterogeneous resource constraints at the viewers. We evaluate 4D Tele Cast using extensive simulations with 3DTI activity data and Planet Lab traces.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"22 1","pages":"82-91"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83237916","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}
A key challenge for wireless networks in which nodes can move is for each node to keep track of its dynamically changing set of nearby nodes (neighbors). We present a solution for nodes to maintain neighbor knowledge where nodes communicate via wireless broadcast and are restricted to move on a two-dimensional road network. A road network is a collection of one-dimensional lines that may intersect each other. For nodes to exchange neighbor information, we construct a deterministic collision-free broadcast schedule which utilizes time division multiplexing and geographical segmentation. Under a certain node density requirement and assuming initial neighbor knowledge, our broadcast schedule tolerates node movement on the road network while providing deterministic guarantees in maintaining neighbor knowledge. We also provide a lower bound on the speed of a message propagation given our broadcast schedule. In addition, we consider grouping nodes into clusters and show that, under certain conditions, neighbor knowledge is maintained when two different clusters move close to each other. Finally, we address the issue of obtaining initial neighbor knowledge.
{"title":"Neighbor Knowledge of Mobile Nodes in a Road Network","authors":"Hyun-Chul Chung, Saira Viqar, J. Welch","doi":"10.1109/ICDCS.2012.16","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.16","url":null,"abstract":"A key challenge for wireless networks in which nodes can move is for each node to keep track of its dynamically changing set of nearby nodes (neighbors). We present a solution for nodes to maintain neighbor knowledge where nodes communicate via wireless broadcast and are restricted to move on a two-dimensional road network. A road network is a collection of one-dimensional lines that may intersect each other. For nodes to exchange neighbor information, we construct a deterministic collision-free broadcast schedule which utilizes time division multiplexing and geographical segmentation. Under a certain node density requirement and assuming initial neighbor knowledge, our broadcast schedule tolerates node movement on the road network while providing deterministic guarantees in maintaining neighbor knowledge. We also provide a lower bound on the speed of a message propagation given our broadcast schedule. In addition, we consider grouping nodes into clusters and show that, under certain conditions, neighbor knowledge is maintained when two different clusters move close to each other. Finally, we address the issue of obtaining initial neighbor knowledge.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"23 6 1","pages":"486-495"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83538560","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}
J. Carretero, Florin Isaila, Anne-Marie Kermarrec, François Taïani, Juan M. Tirado
Geolocated social networks, combining traditional social networking features with geolocation information, have grown tremendously over the last few years. Yet, very few works have looked at implementing geolocated social networks in a fully distributed manner, a promising avenue to handle the growing scalability challenges of these systems. In this paper, we focus on georecommendation, and show that existing decentralized recommendation mechanisms perform in fact poorly on geodata. We propose a set of novel gossip-based mechanisms to address this problem, in a modular similarity framework called GEOLOGY. The resulting platform is lightweight, efficient, and scalable, and we demonstrate its advantages in terms of recommendation quality and communication overhead on a real dataset of 15,694 users from Foursquare, a leading geolocated social network.
{"title":"Geology: Modular Georecommendation in Gossip-Based Social Networks","authors":"J. Carretero, Florin Isaila, Anne-Marie Kermarrec, François Taïani, Juan M. Tirado","doi":"10.1109/ICDCS.2012.36","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.36","url":null,"abstract":"Geolocated social networks, combining traditional social networking features with geolocation information, have grown tremendously over the last few years. Yet, very few works have looked at implementing geolocated social networks in a fully distributed manner, a promising avenue to handle the growing scalability challenges of these systems. In this paper, we focus on georecommendation, and show that existing decentralized recommendation mechanisms perform in fact poorly on geodata. We propose a set of novel gossip-based mechanisms to address this problem, in a modular similarity framework called GEOLOGY. The resulting platform is lightweight, efficient, and scalable, and we demonstrate its advantages in terms of recommendation quality and communication overhead on a real dataset of 15,694 users from Foursquare, a leading geolocated social network.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"115 1","pages":"637-646"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74828822","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}
In many mobile sensing applications devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to cycle their radios to conserve energy, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pair wise, direct one-hop communication. We argue that such pair wise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept of extended quorum system, which leverages indirect discovery to further conserve energy. Specifically, we use quorum graph to capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum based discovery and rendezvous solutions. The simulation and test bed experimental results show that our solution can reduce as much as 55% energy consumption with a maximal 5% increase in latency for existing solutions.
{"title":"EQS: Neighbor Discovery and Rendezvous Maintenance with Extended Quorum System for Mobile Sensing Applications","authors":"Desheng Zhang, Tian He, Fan Ye, R. Ganti, H. Lei","doi":"10.1109/ICDCS.2012.73","DOIUrl":"https://doi.org/10.1109/ICDCS.2012.73","url":null,"abstract":"In many mobile sensing applications devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to cycle their radios to conserve energy, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pair wise, direct one-hop communication. We argue that such pair wise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept of extended quorum system, which leverages indirect discovery to further conserve energy. Specifically, we use quorum graph to capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum based discovery and rendezvous solutions. The simulation and test bed experimental results show that our solution can reduce as much as 55% energy consumption with a maximal 5% increase in latency for existing solutions.","PeriodicalId":6300,"journal":{"name":"2012 IEEE 32nd International Conference on Distributed Computing Systems","volume":"56 1","pages":"72-81"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79679584","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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