A markov routing algorithm for mobile DTNs based on spatio-temporal modeling of human movement data

The commercial success of smartphones increases the feasibility of mobile ad hoc networking in daily life; we define such networks as spontaneous smartphone networks (SSNs). Efficient data delivery in SSNs is challenging because of the low node density, ambiguous contact opportunities, and short message lifetime. The existing schemes attempt to select optimal relays via various cumulative metrics (e.g., encounter history, social centrality, or contact distribution), whose effectiveness is ambiguous and suboptimal. In this paper, we introduce a Markov predictor-based transient delivery scheme that quantifies the regularity of small time scale movement for forwarding decisions. Unlike previous works, we utilized fine-grained mobility data to reduce errors of estimating contact opportunities and contact duration. On the basis of this forwarding strategy, we developed a multi-copy routing scheme. The evaluation using real traces indicates that the proposed approach outperforms compared alternatives in terms of delivery rate and cost.

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“…Moghadam et al . leverages a temporal–spatial model to forward messages to the destination areas. Nevertheless, Gao et al .…”

Section: Related Workmentioning

confidence: 99%

Transient data delivery using fine‐grained mobility data in spontaneous smartphone networks

Yin

Chon

Talipov

et al. 2013

Wireless Communications

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Similarity Search on Uncertain Spatio-temporal Data

Niedermayer

Züfle

Emrich

et al. 2013

Similarity Search and Applications

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In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain motion in space and time. To model similarity -which is an important building block for many applications such as identifying frequent motion patterns or trajectory clustering -we employ the well-known Longest Common Subsequence (LCSS) measure, which becomes a distribution on uncertain spatio-temporal data (ULCSS). We show how the aligned version (without time shifting) of the ULCSS can be exactly computed in PTIME, which is also verified by extensive experiments.

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V2X Routing in a VANET Based on the Hidden Markov Model

Yao

Wang

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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A markov routing algorithm for mobile DTNs based on spatio-temporal modeling of human movement data

Cited by 4 publications

References 21 publications

Transient data delivery using fine‐grained mobility data in spontaneous smartphone networks

Transient data delivery using fine‐grained mobility data in spontaneous smartphone networks

Similarity Search on Uncertain Spatio-temporal Data

V2X Routing in a VANET Based on the Hidden Markov Model

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