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This feature is especially useful in heterogeneous CRNs where the data traffic is represented by background services or some interactive applications with relaxed delay constraints.
In addition, the uncongested traffic is represented in the upper part of the graph and the lower part is the congested traffic condition.
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The time slots allocated for Q-demand and A-demand traffic are represented by (gamma _{n_{k}}^{Q}) and (gamma _{n_{k}}^{A}), respectively.
Figure 3 shows the traffic model of the L-MAC; where, a parent (P) has multiple child nodes (C), and C neighbour nodes 1 and 2. The network follows the shortes route; where, incoming traffic, outgoing traffic, and interfering traffic are represented by F in, F out, and F inter, respectively.
Specifically, while the city and streets are rendered using 3D graphics, the taxi, passengers, and other traffic are represented by sprites in order to work on the limited GBA hardware.
In addition, genes associated with extracellular matrix constitution and intracellular protein traffic were represented much more abundantly in the dataset.
In addition, a model of bone homing and cellular trafficking is represented by bone metastasis in human cancer.
Figure 7 Throughput versus Load: High Priority traffic in AWPP-POAP-EDCA-HCCA. Figure 8 Delay versus Load: High Priority traffic in AWPP-POAP-EDCA-HCCA. Figure 9 shows the MP traffic throughput as a function of the MP traffic load, while the MP traffic average delay versus the MP traffic load is represented in Figure 10.
Three users, each using a different traffic type (red circles depict VoIP users, green squares video streaming users and yellow diamonds depict BE users), are located on each ring (Figure 5); hence, n = K/3 and each traffic type is represented with K/3 users.
The traffic loading condition is represented by the traffic index, TI, which is related to the design 80 kN ESAL applications as provided in Eq. 4.
This model shows a form of hierarchical multimodeling where the decision logic of a simple traffic light system is represented heterogeneously using both finite state machines (FSMs) and event relationship graphs (ERGs).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com