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This article summarizes the results of a literature search for adaptive numerical methods of solving partial differential equations; the methods discussed involve the adaptive movement of nodes, so as to obtain a low level of solution truncation error while minimizing the number of nodes used in the calculation.
In comparison to many existing moving or adaptive grid approaches, this has the advantage that the semi-discrete PBE does not need to be solved in conjunction with an additional system of equations governing the movement of nodes in particle property space.
In addition, the movement of nodes may change network topology.
Working Model Day Movement makes the movement of nodes even more realistic.
The movement of nodes is simulated with the RandomWayPoint mobility model.
It is an algorithm based on the assumption of non-random movement of nodes.
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Therefore, movement of node follows a pattern known as social-based movement pattern.
In this work, the movement of node m ∈ M is modeled as a second-order Markov process.
In this case the movement of node #6 also helps the communication to node #5 by creating more routes available and more probability to choose a better route from node #1 to node #5.
Comparing with the values of VST scenario, where delay increased progressively with the increase of hop distance, in this case the movement of node #6 also helps the communication to node #5 by creating more routes available and more probability to choose a better route from node #1 to node #5.
Our work is novel in that we utilize the past node trajectories to predict the future co-movement of nodes for data-centric storage in mobile environments.
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