A framework for evaluating DTN mobility models

Petz, Agoston; Enderle, Justin Wayne; Julien, Christine

doi:10.4108/icst.simutools2009.5647

Cited by 26 publications

(19 citation statements)

References 27 publications

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“…GeSoMo mobility model provides results that are coherent with a broad range of empirical data which describe real-world human social behaviour and mobility [9].…”

Section: General Social Mobility Model (Gesomo)supporting

confidence: 65%

“…This creates simple, but quite artificial movement which humans hardly can move or select for their destinations in a square area. This model often results in a non-uniform stationary node distribution and hence complicates the analysis [8,9,10,11]. RWP is improved by Smooth Random Mobility Model (SRMM) that adds a temporal dependency where speed is changed incrementally in a smooth fashion [12].…”

Section: Random Walkway Point (Rwp)mentioning

confidence: 99%

See 1 more Smart Citation

Mobility Models for Delay Tolerant Network: A Survey

Shahzamal¹,

Parvez²,

Zaman³

et al. 2014

IJWMN

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“…GeSoMo mobility model provides results that are coherent with a broad range of empirical data which describe real-world human social behaviour and mobility [9].…”

Section: General Social Mobility Model (Gesomo)supporting

confidence: 65%

Section: Random Walkway Point (Rwp)mentioning

confidence: 99%

Mobility Models for Delay Tolerant Network: A Survey

Shahzamal¹,

Parvez²,

Zaman³

et al. 2014

IJWMN

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show abstract

“…After reaching the destination, the node may pause for a random amount of time before the new destination and speed are chosen randomly for the next movement. In LW mobility model [31], [33], [34], on the other hand, each movement length and pause time distributions closely match truncated power-law distributions. Further, angles of movement are pulled from a uniform distribution.…”

Section: Network/communication Model and Technical Background In Contextmentioning

confidence: 91%

An Iterative Algorithm for Trust Management and Adversary Detection for Delay-Tolerant Networks

Ayday

Fekri

2012

IEEE Trans. on Mobile Comput.

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Abstract-Delay/Disruption Tolerant Networks (DTNs) have been identified as one of the key areas in the field of wireless communication, wherein sparseness and delay are particularly high. They are emerging as a promising technology in vehicular, planetary/interplanetary, military/tactical, disaster response, underwater and satellite networks. DTNs are characterized by large endto-end communication latency and the lack of end-to-end path from a source to its destination. These characteristics pose several challenges to the security of DTNs. Especially, Byzantine attacks in which one or more legitimate nodes have been compromised and fully controlled by the adversary can give serious damages to the network in terms of latency and data availability. Using reputationbased trust management systems is shown to be an effective way to handle the adversarial behavior in Mobile Ad hoc Networks (MANETs). However, because of the unique characteristics of DTNs, those traditional techniques do not apply to DTNs. Our main objective in this paper is to develop a robust trust mechanism and an efficient and low cost malicious node detection technique for DTNs. Inspired by our recent results on reputation management for online systems and e-commerce, we develop an iterative malicious node detection mechanism for DTNs referred as ITRM. The proposed scheme is a graph-based iterative algorithm motivated by the prior success of message passing techniques for decoding low-density parity-check codes over bipartite graphs. Applying ITRM to DTNs for various mobility models, we observed that the proposed iterative reputation management scheme is far more effective than well-known reputation management techniques such as the Bayesian framework and EigenTrust. Further, we concluded that the proposed scheme provides high data availability and packet-delivery ratio with low latency in DTNs under various adversary attacks which attempt to both undermine the trust and detection scheme and the packet delivery protocol.

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“…Some DTN examples [21] include: (1) urban settings involving vehicles meeting opportunistically, and performing data transactions that enable connectivity between isolated geographical regions; (2) rural environments conformed by a set of village-like areas which may have internal connectivity but are isolated among themselves and from the rest of the world; (3) networks of sensors which may be static but are used to gather statistics regarding the movement of other entities, such as animals in wildlife settings; and (4) networks of autonomous robots distributed in a given environment assigned with the task of relaying data between otherwise disconnected areas. The semantics of this type of environments can be effectively mapped to UMMF models.…”

Section: Disruption-tolerant Networking (Dtn)mentioning

confidence: 99%

On the Universal Generation of Mobility Models

Medina¹,

Gürsun²,

Basu³

et al. 2010

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Abstract-Mobility models have traditionally been tailored to specific application domains such as human, military, or ad hoc transportation scenarios. This tailored approach often renders a mobility model useless when the application domain changes. Furthermore, the failure to adapt the mobility model to accurately match the new domain naturally leads to wrong conclusions about the performance of protocols and applications running atop. In this paper, we propose a mobility modeling framework based on the observation that the mobility characteristics of most mobility-based applications can be captured in terms of a few fundamental factors: (1) Targets; (2) Obstacles; (3) Dynamic Events; (4) Navigation; (5) Steering behaviors; , and (6) Dynamic Behaviors. We have designed and implemented a Universal Mobility Modeling Framework (UMMF), which enables the instantiation of a mobility model from a wide universe of possibilities defined by the aforementioned factors. We describe the mapping from application-domain-specifics to UMMF elements, demonstrating the power and flexibility of our approach by capturing representative mobility models with good accuracy in terms of a large number of topological metrics. We also describe several specific mobility scenarios and their UMMFbased model representations.

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A framework for evaluating DTN mobility models

Cited by 26 publications

References 27 publications

Mobility Models for Delay Tolerant Network: A Survey

Mobility Models for Delay Tolerant Network: A Survey

An Iterative Algorithm for Trust Management and Adversary Detection for Delay-Tolerant Networks

On the Universal Generation of Mobility Models

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