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The message delay in mobile ad hoc networks

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TLDR
A stochastic model is introduced that accurately models the message delay in mobile ad hoc networks where nodes relay messages and the networks are sparsely populated and accurately predicts the messagedelay for both relay strategies for a number of mobility models.
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This article is published in Performance Evaluation.The article was published on 2005-10-01 and is currently open access. It has received 615 citations till now. The article focuses on the topics: Mobility model & Optimized Link State Routing Protocol.

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Citations
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Journal ArticleDOI

Performance modeling of epidemic routing

TL;DR: A rigorous, unified framework based on ordinary differential equations (ODEs) to study epidemic routing and its variations is developed, investigating how resources such as buffer space and the number of copies made for a packet can be traded for faster delivery.
Proceedings ArticleDOI

Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing

TL;DR: The study of traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses, finds that the all-bus-pairs aggregated inter-contact times show no discernible pattern, and suggests the importance in choosing the right level of model granularity when modeling mobility-related measures such as inter- contact times in DTNs.
Proceedings ArticleDOI

Modeling Time-Variant User Mobility in Wireless Mobile Networks

TL;DR: A novel time-variant community mobility model is proposed that defines communities that are visited often by the nodes to capture skewed location visiting preferences, and use time periods with different mobility parameters to create periodical re-appearance of nodes at the same location.
Journal ArticleDOI

Data Offloading Techniques in Cellular Networks: A Survey

TL;DR: This paper presents a comprehensive survey of data offloading techniques in cellular networks and extracts the main requirements needed to integrate data offload capabilities into today's mobile networks.
Book ChapterDOI

Performance modeling of epidemic routing

TL;DR: This paper develops a rigorous, unified framework based on Ordinary Differential Equations (ODEs) to study epidemic routing and its variations, and investigates how resources such as buffer space and power can be traded for faster delivery.
References
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Book ChapterDOI

Dynamic Source Routing in Ad Hoc Wireless Networks

TL;DR: This paper presents a protocol for routing in ad hoc networks that uses dynamic source routing that adapts quickly to routing changes when host movement is frequent, yet requires little or no overhead during periods in which hosts move less frequently.
Proceedings ArticleDOI

A performance comparison of multi-hop wireless ad hoc network routing protocols

TL;DR: The results of a derailed packet-levelsimulationcomparing fourmulti-hopwirelessad hoc networkroutingprotocols, which cover a range of designchoices: DSDV,TORA, DSR and AODV are presented.
Journal ArticleDOI

Mobility increases the capacity of ad hoc wireless networks

TL;DR: The per-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery, can be increased dramatically under this assumption, and a form of multiuser diversity via packet relaying is exploited.
Proceedings ArticleDOI

Random waypoint considered harmful

TL;DR: This study examines the random waypoint model widely used in the simulation studies of mobile ad hoc networks and shows that this model fails to provide a steady state in that the average nodal speed consistently decreases over time, and should not be directly used for simulation.
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Frequently Asked Questions (12)
Q1. What are the contributions mentioned in the paper "The message delay in mobile ad hoc networks" ?

In this paper, a stochastic model is introduced to accurately model the message delay in mobile ad hoc networks where nodes relay messages and the networks are sparsely populated. 

Future research will focus on the message delivery within a certain timeframe, the inclusion of queueing delays, non-homogeneous scenarios ( nodes have a different or a changing transmission range ), the inclusion of interference and transmission times, and the study of other mobility models ( two-dimensional, three-dimensional, or on a sphere ). 

(12)Under the unrestricted multicopy protocol, the expected message delay isE[TU] = 1 λN N∑ i=1 1 i = 1 λN ( log(N) + γ +O ( 1 N )) , (13)where γ ≈ 0.57721 is Euler’s constant. 

For both the two-hop and the unrestricted multicopy protocols the proof is based on modeling the number of copies in the network as an absorbing finite-state Markov chain. 

Since λ = O(r(N)) for the random waypoint model, and with the scaling r = O(1/√N), the authors find that the expected message delay under the two-hop relay protocol is O( √ N), just as was found in [14] but for nodes moving on a sphere. 

In [14] it is shown that, under the two-hop relay protocol, the expected message delay is of the order nTp(n) for the random waypoint mobility model on a sphere (where n is the number of nodes per unit area and Tp(n) is the transmission time of a message). 

Because of the quick emergence of the exponential tail for the random walker mobility, the authors have included it in their analysis to see how robust their model is. 

The expected number of copies under the two-hop multicopy protocol is given by (cf. (2) and (12))E[N2] = 1 N N∑ i=1 i2 Ni N! (N − i)! = √ πN 2 +O(1). (14)Hence E[N2] = λNE[T2]. 

The parameter λ for the random direction (RD) and the random waypoint (RW) mobility models is given byλRD ≈ 2rE[V ∗]L2 , and λRW ≈ 2ωrE[V ∗] L2 , (15)respectively, for values of r L. 

This result shows that for each protocol the expected message delay is a linear function of the expected inter-meeting time 1/λ, and changing the value λ does not have any impact except for a time scaling. 

6 and 7 display the expected message delays obtained both through simulations and by the analytical model as a function of the number of nodes. 

The performance of mobile ad hoc networks is in general studied via lengthy and complex simulations, for a limited number of mobility models, including the random waypoint mobility model [3] or the random direction mobility model [1,8].