Being a finite volume method, the numerical scheme is fully conservative, and the ability to locally refine the mesh can produce solutions with more accuracy for the same number of nodes compared to a uniform mesh, as we demonstrate numerically.
Indeed, numerical results illustrate that by using MIMODog, the network can have a constant improvement M on an asymptotic number of monitor nodes compared to SISO 802.11 DCF MAC.
Experiments are then conducted to show the performance of the proposed CMFFP-tree algorithm in terms of execution time and the number of tree nodes, compared to those of the MFFP-tree and CFFP-tree algorithms.
On a large scale cluster (100 nodes), compared to two production class distributed storage systems (Ceph and GlusterFS), WOSS achieves up to 6× better performance for the synthetic benchmarks and 20 40% better application-level performance gain for real applications.
We also find the conclusion: "the sparse networks need more control nodes than the dense, and the homogeneous networks need fewer control nodes compared to the heterogeneous" (Liu et al., 2011 [18]), is also applicable to network complete controllability.
The higher slot numbers have higher probability to get selected by nodes compared to lower slot numbers.
Our protocol exhausts fewer nodes compared to ZD-AOMDV and AOMDV protocols, which increases the lifetime of the network.
In the sensitivity plot, DIAMOND detects a significantly larger fraction of attacked nodes compared to the parallel algorithm.
Figure 2 shows the probability distribution P. The higher slot numbers have higher probability to get selected by nodes compared to lower slot numbers.
SEP [6, 13] works well for heterogeneous networks, as it considers the initial energy of nodes compared to other nodes in the network for choosing the CH.
However, by reducing the number of nodes (compared to input) in the hidden layer, interesting structural features can be learned (Bengio et al. 2007).
