Stochastic Properties of Mobility Models in Mobile Ad Hoc Networks

Bandyopadhyay, Subhajyoti; Coyle, Eugene; Falck, Thomas

doi:10.1109/tmc.2007.1014

Cited by 59 publications

(29 citation statements)

References 40 publications

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“…In [32], different mobility models and their effects to ad hoc networks are compared. Stochastic properties of random walk and random waypoint mobility models are analyzed in [33] and [34]- [37], respectively. Another way of combining micro-and macroscopic path loss uncertainty has been explored in [38], where small-scale fading is interpreted as a distortion of the point process in modeling the node locations.…”

Section: Related Workmentioning

confidence: 99%

Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation

Gong

Haenggi

2014

IEEE Trans. on Mobile Comput.

154

162

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In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multi-path effects. In this paper, the interference statistics in mobile random networks are characterized by mapping the distance variations of mobile nodes to the channel gain fluctuations. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference distribution in a single snapshot does not change under uniform mobility models, but random waypoint mobility increases the interference. Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes be smart (i.e,. correlationaware) to avoid bursts of transmission failures.

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Section: Related Workmentioning

confidence: 99%

Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation

Gong

Haenggi

2014

IEEE Trans. on Mobile Comput.

154

162

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“…The non-uniform distribution of the AOM has also been reported from the real-world tracing in [ 17]. Nonetheless, under special conditions, the first-order density p p α v (α v ; l) in (14) can be simplified to a constant value, indicating that the AOM p α v (l) is uniformly distributed. Notice that the uniform distribution of the AOM is one of the widely made assumptions in the synthetic mobility models proposed in the literature, which is, however, not necessarily true.…”

Section: B Aom Processmentioning

confidence: 64%

“…Starting from the phase distribution of a complex Gaussian distribution with uncorrelated quadratures in [35, Eq. (2)], setting ρ = 0, and performing some straightforward mathematical manipulations results in (14).…”

Section: B Aom Processmentioning

confidence: 99%

A Highly Flexible Trajectory Model Based on the Primitives of Brownian Fields—Part II: Analysis of the Statistical Properties

Borhani

Pätzold

2016

IEEE Trans. Wireless Commun.

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Abstract-In the first part of our paper, we have proposed a highly flexible trajectory model based on the primitives of Brownian fields (BFs). In this second part, we study the statistical properties of that trajectory model in depth. These properties include the autocorrelation function (ACF), mean, and the variance of the path along each axis. We also derive the distribution of the angle-of-motion (AOM) process, the incremental travelling length process, and the overall travelling length. It is shown that the path process is in general non-stationary. We show that the AOM and the incremental travelling length processes can be modelled by the phase and the envelope of a complex Gaussian process with nonidentical means and variances of the quadrature components. In accordance with empirical studies, we proof that the AOM process does not follow the uniform distribution. As special cases, we show that the incremental travelling length process follows the Rice and Nakagami-q distributions, whereas the overall travelling path can be modelled by a random variable following either the Gaussian or the lognormal distribution. The flexibility of the results is demonstrated and discussed extensively. It is shown that the results are in line with those of real-world user tracings. The results can be used in many areas of wireless communications.

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“…are not interested in investigating the stochastic features of the random waypoint mobility model while a comprehensive analysis can be found in [33,34]. The probability of leaving the POS ( ) will be dependent on the transmission range (R) of the sensor nodes and the total number of movements inside a given POS (assuming a straight line trajectory in a POS), expressed in (29).…”

Section: = × ×mentioning

confidence: 99%

Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

2016

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Stochastic Properties of Mobility Models in Mobile Ad Hoc Networks

Cited by 59 publications

References 40 publications

Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation

Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation

A Highly Flexible Trajectory Model Based on the Primitives of Brownian Fields—Part II: Analysis of the Statistical Properties

Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

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