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Figure 1 shows a model where nodes maintain separate queues for each AC and packets at the head-of-line (HOL) of each queue contend for channel access using AC-specific parameters [4] which are more favorable to HP traffic than the LP traffic.
The impact of mobility is also noticed in the Random Direction model where nodes only change direction when they reach the scenario boundaries, and in the Random Street Map model which, similarly to the Manhattan model, is based on the organization of the streets in a city.
The performance analysis of conventional route establishment schemes in ad hoc networks is based on over-simplified communication link models such as the binary link model where nodes can perfectly communicate within a transmission radius and nothing at all is communicated outside that radius [5].
To reveal hidden direct correlations between modules, i.e correlations masked by the effect of other modules, we adopted partial correlation analysis and constructed a network based on Gaussian graphical model where nodes correspond to modules and edges indicate significant partial correlations between modules.
The graph is then simulated as a physical model, where nodes interact with each other based on their physical attributes while edges constrain their movement.
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Software packages facilitate the interpretation of omics data by using graph models where nodes represent bio-elements and edges are bio-interactions between these elements (Junker et al., 2006).
To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables.
In our proposal, we have designed a model where the nodes' IP addresses configuration has two main phases: first, a local connection address is generated by the node that wants to take part of the network.
Existing routing and broadcasting protocols for ad hoc networks assume an ideal physical layer model, where two nodes communicate if and only if they are at distance at most R, where R is the transmission radius.
It has been shown in [7] that the capacity upper bound in the asynchronous channel model, i.e., the channel model where the nodes do not have complete CSI, can be found by setting the correlation ρ to zero.
By combining the models of attack, intrusion, and rejuvenation, we get an extended model where healthy nodes can be intruded and then be reverted back to a healthy state.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com