A Markov-based channel model algorithm for wireless networks

Konrad, Almudena; Zhao, Ben Y.; Joseph, Anthony D.; Ludwig, R.

doi:10.1145/381591.381602

Cited by 138 publications

(227 citation statements)

References 10 publications

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“…Trace-driven network models are popular in research areas where active measurements are difficult to perform, including wireless networks [16], mobile networks [15], and Internet backbone traffic [6]. Researchers continue to rely on synthetic model-generated traffic traces for experimental research, even as they recognize and continue to reduce the inherent error introduced by these models [15].…”

Section: Related Workmentioning

confidence: 99%

“…wired and wireless networks has shown that the validity of experimental results depends on the accuracy of the data used [12,16]. This is especially true for the growing area of online social networks, where evaluation of social applications can produce very different results depending on the social graphs used [35].…”

mentioning

confidence: 99%

“…Trace-driven modeling is popular in research settings where real measurement data is difficult to gather, such as wireless networks [16,22]. In the social graph context, we can "fit" a graph model to a real social graph, thereby extracting a set of model parameters.…”

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confidence: 99%

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Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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146

127

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Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Due to the high time and resource costs of gathering real graph datasets through direct measurements, researchers are anonymizing and sharing a small number of valuable datasets with the community. However, performing experiments using shared real datasets faces three key disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large datasets, and that a small number of available social graphs limits the statistical confidence in the results.The use of measurement-calibrated graph models is an attractive alternative to sharing datasets. Researchers can "fit" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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146

127

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“…In this protocol the cost of forwarding packets through a link depends on which node a packet arrives from. This protocol uses the Markovian [14] metric for channel resource consumption optimization because of local coding. COAR which was introduced by Yan et al [15] combines NC and opportunistic routing approaches.…”

Section: B Distributed Network Coding-aware Mechanismsmentioning

confidence: 99%

Opportunistic XOR-Coding Content Propagation in VANET

Malakooti¹,

Jalooli²,

Jabbarpour³

et al. 2014

IJCEE

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Abstract-The main difference between typical wireless networks and Vehicular Ad-hoc Networks is mostly about the way traffic is propagated in these types of networks. Unlike conventional wireless networks in VANET connections are established quick and short. Because of these two factors connections in VANET are not as reliable and robust as typical wireless networks. Hence Due to these limitations in VANET and also in order to achieve higher throughput, we are aimed at investigating the adoption of network coding with the existing data propagation and routing methods in Vehicular networks. In order to address the disadvantages, we present an approach called Opportunistic Network Coding-aware Data Dissemination in Wireless Networks (NCODWN). This approach is an enhanced version of COAR. We have managed to improve network throughput and decrease end-to-end delay of the network by implementing a more efficient and reliable mechanism. The simulation results indicate that our approach outperforms COAR in terms of end-to-end delay, percentage of encoded packets, packet delivery and network throughput.Index Terms-Data propagation, network coding, vehicular ad hoc network, wireless. I. INTRODUCTIONThe emergence of wireless networks has made a crucial impact on network communications. Over the last four decades of research were conducted on the different aspects of communication between mobile networks and especially vehicular networks. One of the most important challenges in VANET is considerable rates of packet loss and insufficient throughput. Network coding (NC) [1] was introduced with the main aim of increasing the network throughput. Fig. 1 represents basic concepts of NC. In a wider overview due to nature of wireless networks, they suffer from low throughput and do not scale well in heavy implementations [2] which this problem is inherited by VANET. A. Network Coding and Routing ProtocolsIn order to utilize NC in wireless network in a practical way, one of the most well-known applications is in routing protocols. In other words, NC is applied in routing In order to create a coding-aware routing scheme. Existing NC-aware routing mechanisms in VANET evaluate links only based on possibility and impossibility of coding in nodes.Being aware of Applicable NC scenarios and opportunistic coding among different flows at certain amount of time provides the opportunity to improve the throughput of networks. Thus in order to Improve performance of existing Manuscript received January 9, 2013; revised March 27, 2014 network coding is applied. B. Opportunistic RoutingIn fact opportunistic routing is considerably different from normal routing mechanisms. In conventional routing approaches, the next hop is fixed and is responsible for sending data flows and therefore this kind of routing is not suitable for networks like VANET that their topology is dynamically changing and consequently packet drop probability is high. In contrast in opportunistic routing instead of selection of a fixed next hop, under certain circumstances a se...

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“…The 4-state Markov model and the Markovbased Trace Analysis [16] outperformed the Gilbert model, as the latter was unable to reproduce the variance in burst error lengths. Gamma distributed state durations can be used to replicate the variance in error burst lengths [20], [39].…”

Section: Markov Models Applied In the Literaturementioning

confidence: 99%

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

Hohlfeld

Geib

Hasslinger

2008

2008 16th Interntional Workshop on Quality of Service

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Abstract-Real-time Internet services are gaining in popularity due to rapid provisioning of broadband access technologies. Delivery of high Quality of Experience (QoE) is important for consumer acceptance of multimedia applications.IP packet level errors affect QoE and the resulting quality degradations have to be taken into account in network operation. We derive the second order statistics of the number of packet losses in finite Markov models over several relevant time scales and adapt them to loss processes visible in wired and wireless transmission channels.Higher order Markov chains offer a large set of parameters to be exploited by complex fitting procedures. We experience that the 2-state Gilbert-Elliott model already captures a wide range of observed loss pattern appropriately and discuss how such models can be used to examine the quality degradations caused by packet losses.

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A Markov-based channel model algorithm for wireless networks

Cited by 138 publications

References 10 publications

Measurement-calibrated graph models for social network experiments

Measurement-calibrated graph models for social network experiments

Opportunistic XOR-Coding Content Propagation in VANET

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

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