Computational Intelligence - August 2013 - 49

A. Verification on the Ability of Regulation
and Stabilization for the ISOS

Table 2 Parameters of the networks and the environment.

The following simulation and corresponding analysis are carCaTegory
ITem
Value
AreA
Length
400 m
ried out to verify the performance of the ISOS on regulating
configurAtion
Width
400 m
and maintaining the whole network status with the predefined
netWork
PercentAge of Working nodes (p) (0,1)
indices (which refers to the percentage of the nodes that should
configurAtion
QuAntity of node (N)
500
be in waking status), and no RFS is involved. The distribution
communicAtion rAdius
32 m
of the nodes is completely randomized. All the nodes are iniserving rAdius
16 m
tially set to the waking status, and then they are autonomously
-10
hormone
min_h
10
configurAtion
mAx_h
working according to the waking probability P with the pro0
sWitch_h
posed ISOS. Specifically, the value of P changed from 0.1 to
D AH = D HH
1
0.9 with an increment of 0.1 for each time in the simulations.
For each value of P, a group of ten experiments are carried out
3) The variation (error) of the average quantity of the wakand their results are averaged to minimize the side effects
ing nodes away from its expected value is slight. The
brought by the randomness of the network distributions.
maximum value of the variation reached 2.12% for the
The results with different waking probabilities are listed in
case with P = 0.4, which is much smaller than the theoTable 3, and the detailed tuning processes of the waking node
retical error range ! 10%. On most occasions, the error
quantities are illustrated in Fig. 5. Here only the cases with
P = 0.2, 0.5 and 0.8 are selected to draw the graph (one in ten
can be limited to less than 1% (which can be referred to
experiments for each value of P), similar results can still be
the column of number of nodes in the waking status in
acquired by changing the probability to its subsequent values. A
Table III). Although the communication and coordination
conventional distributed algorithm named randomized algoamong nodes may be constrained by the distribution of
rithm (RA) [32] that drives the nodes to work in a random
the network, the ISOS can still preserve the high adjustfrequency is applied for comparison. Note that an identical
ing accuracy and stability of the network.
network distribution should be applied with both the networks
4) The network with the proposed ISOS experiences less
with RA and ISOS to avoid possible inconsistencies on the
fluctuations after the network gets into its stable status. It
network structure that may lead to incomparable results.
can be observed from Table 3 that the fluctuation range
Some principle features that the IoT with the proposed ISOS
of the quantity of the waking nodes is less than 2% in
bears can be observed from Table 3 and Fig. 5 as follows.
most cases after the network reaches a steady state, as can
1) The percentage of the nodes in the waking status can be
be seen in Fig. 5. For P = 0.2/0.5/0.8, the standard deviatuned from the initial status (P = 1) to the expected level,
tions of the quantities of the waking nodes with the prono matter which value is previously assigned to P (from
posed ISOS and RA are 1.0198/9.3390, 1.3684/11.5242,
0.1 to 0.9). After the quickly and effectively regulating
and 0.9788/8.4015, respectively. This indicates that the
process at the beginning, the quantity of the waking nodes
proposed ISOS has a better ability to maintain the stabilican be tuned to the expected value and then the steady
ty of the network and with higher control precision.
state of the network can be maintained continuously until
The coverage ratio changes of the waking nodes with difnew disturbance (e.g. RFS or the change of P) appears.
ferent P during the simulations is shown in Fig. 6. By com2) The time consumed by the network for the tuning process
paring the results in Fig. 6 and Fig. 5, it can be observed that
to the expected value is small. On most occasions, the
the coverage ratios and the quantities of the waking nodes in
quantity of nodes in waking status can reach the
Table 3 Tuning processes and results with different probabilities of working nodes.
expected value in only one
regulating process or even
osCIllaTIon number seTTlIng TIme
QuanTITy of awake nodes
none (refer to the columns
P
aVerage range
aVerage range
aVerage/n range/n
aVerage error
of oscillation number and
0.1
0
[0]
6.1
[6, 7]
52.95
[-0.97%, 1.02%] 0.59%
settling time in Table 3).
0.2
0.8
[0, 1]
21.2
[6, 29]
100.02
[-1.18%, 1.02%] 0.004%
This indicates that the net0.3
0.8
[0, 1]
21.6
[6, 35]
149.92
[-1.74%, 1.23%] -0.016%
work can attain the final
0.4
0.3
[0, 1]
9.3
[3, 25]
205.77
[-2.87%, 2.11%] 1.115%
state quickly and efficiently
0.5
0.3
[0, 1]
2.2
[0, 9]
249.89
[-0.94%, 1.25%] 0.022%
by applying the proposed
ISOS, though the nodes
0.6
0.6
[0, 1]
12.3
[0, 32]
297.77
[-1.20%, 1.56%] -0.446%
are still working in a dis0.7
0.9
[0, 1]
25.4
[4, 36]
349.79
[-2.56%, 1.47%] -0.042%
tributed form and cannot
0.8
1
[1]
23.7
[13, 29]
399.85
[-1.37%, 1.29%] -0.03%
acquire any overall infor0.9
0
[0]
5.8
[5, 6]
450.43
[-1.00%, 1.00%] 0.086%
mation of the network.

August 2013 | IEEE ComputAtIonAl IntEllIgEnCE mAgAzInE

49



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