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Rezensionsexemplar
August 2014
Andreea Hossmann-Picu
Delay Tolerant Networks with Heterogeneous Mobility: Models and Implications for Algorithms
Opportunistic or Delay Tolerant Networks (DTNs) may be used to enable communication in case of failure or lack of infrastructure (disaster, censorship, remote areas) and to complement existing wireless technologies (cellular, WiFi). Wireless peers communicate when in contact, forming an impromptu network, whose connectivity graph is highly dynamic and only partly connected. In this harsh environment, communication algorithms are mostly local search heuristics, choosing a solution among the locally available ones. Furthermore, they are routinely evaluated through simulations only, as they are hard to model analytically. Even when more insight is sought from models, these usually assume homogeneous node meeting rates, thereby ignoring the attested heterogeneity and non-trivial structure of human mobility.
We propose a new unified analytical model, that maps an important class of DTN optimization problems over heterogeneous mobility (contact) models, into a Markov chain traversal over the relevant solution space. (Heterogeneous) meeting probabilities between different pairs of nodes dictate the chain's transition probabilities and determine neighboring solutions. Local optimization algorithms can accept/reject candidate transitions (deterministically or randomly), thus "modulating" the above transition probabilities. We apply our model to two example problems: routing and content placement. We predict the performance of state-of-the-art algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that surprising precision can be achieved against simulations, despite the complexity of the problems and diversity of settings.
We propose a new unified analytical model, that maps an important class of DTN optimization problems over heterogeneous mobility (contact) models, into a Markov chain traversal over the relevant solution space. (Heterogeneous) meeting probabilities between different pairs of nodes dictate the chain's transition probabilities and determine neighboring solutions. Local optimization algorithms can accept/reject candidate transitions (deterministically or randomly), thus "modulating" the above transition probabilities. We apply our model to two example problems: routing and content placement. We predict the performance of state-of-the-art algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that surprising precision can be achieved against simulations, despite the complexity of the problems and diversity of settings.
Schlagwörter: Delay Tolerant Network; Opportunistic Network; heterogeneous mobility; performance analysis; Mobile Ad Hoc Network; Markov chain; epidemic routing; content placement; Markov chain Monte Carlo
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