Unlike conventional mobile telephone networks, ad hoc mobile networks do not rely on fixed base-station infrastructure to relay calls from mobile handsets. If they are close enough to each other, handsets communicate directly on a peer-to-peer basis, and if they are not close enough, they must rely on other handsets to act as transit nodes. In this context the authors define their model of a mobile ad hoc network. Moreover, they develop an analysis of the critical recharge rates. In fact they propose a dynamic model that evolves in time. They do this allowing battery energy and credit at each handset in the ad hoc network to be replenished. The result is a network of stochastic fluid models which all increase or decrease according to current network occupancy. The authors show how to calculate the stationary distribution of a stochastic fluid model in which the modulating process is forced to change state when the fluid level drops to zero. These results are employed to calculate the call dropout probabilities of the network via a reduced-load method. Leonardo Pasini

Recensione dell'articolo: (Latouche, G.; Taylor, P. G. - " A stochastic fluid model for an ad hoc mobile network " - Queueing Syst. 63 (2009), no.1-4, 109–129.)

PASINI, Leonardo
2011-01-01

Abstract

Unlike conventional mobile telephone networks, ad hoc mobile networks do not rely on fixed base-station infrastructure to relay calls from mobile handsets. If they are close enough to each other, handsets communicate directly on a peer-to-peer basis, and if they are not close enough, they must rely on other handsets to act as transit nodes. In this context the authors define their model of a mobile ad hoc network. Moreover, they develop an analysis of the critical recharge rates. In fact they propose a dynamic model that evolves in time. They do this allowing battery energy and credit at each handset in the ad hoc network to be replenished. The result is a network of stochastic fluid models which all increase or decrease according to current network occupancy. The authors show how to calculate the stationary distribution of a stochastic fluid model in which the modulating process is forced to change state when the fluid level drops to zero. These results are employed to calculate the call dropout probabilities of the network via a reduced-load method. Leonardo Pasini
2011
262
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11581/332581
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