IEEE Consumer Electronics Magazine - September 2018 - 38
1) Initialize EDSLT, MSLT, MRIDT, CSIDT, and CAL to zero
and WL with an ascending order of camera IDs.
2) Deploy the scalars S i (S 1, S 2, S 3, f, S n) and cameras
C j (C 1, C 2, C 3, f, C m) randomly.
3) Estimate the Euclidian distance Dist (S i, C j) between
the pairs of scalars and cameras. While estimating the
Euclidian distance Dist (S i, C j) for any S i and C j, WL is
updated as follows:
If Dist (S i, C j) < DOF or Dist (S i, C j) # 2 # Sr
Then Set MSSTi ! MSSTi U " i , and maintain C j in WL,
Else
Drop C j from WL.
4) Update WL.
5) Segregate the monitored region using (1) and update
MRIDT and CSIDT accordingly.
6) Estimate D Sv, l using (3) for each of the scalars in
the virtual compartment. In each of the virtual
compartments, the scalar having the minimal D Sv, l is
selected as the SL of concern.
7) While receiving MEDM from SL i do:
If MEDM received from SL i and i e MSSTi,
then
Set MSLTi ! MSLTU " i ,
End While
8) While WL ! 0
If camera C j is present first in WL, then
Actuate (C j);
Broadcast (MUSLM, C j);
Set CAL j ! CAL j U { j };
Set WL j ! WL j - {j};
9) for j = 1 to CC W L do
If MUSLM received from Cj Then
If (SL id eMSLTCj) = (SL id eMSLTCk)
Then
Actuate (C k);
End for
End While
10) Update CAL, which retains the IDs of the finally actuated
camera sensors.
Table 2. The simulation parameters.
Parameters
Values
DOF
25 m
FOV
360°
Monitored region size
500 × 500 m2
Sensing range of scalar
20 m
Event point
(250,270)
Event radius
50 m
Event area
7,855 m2
38 IEEE Consumer Electronics Magazine
^
september 2018
with the IDs of the already activated camera sensors. This process is continued until WL becomes empty and CAL is finally
updated. The complete pseudocode for the event detection and
camera actuation procedure is depicted in Algorithm 1.
PERFORMANCE EVALUATION
We assessed the effectiveness of the proposed CMT-MDSD
approach for a geographic area of 500 × 500 m2.
SIMULATION SETUP
The sensors were considered to be randomly deployed. We
carried out the experiment by varying the number of cameras
and observing the impact on the DCA-SC [4], DCCA-SM
[5], and CSC-SI [6] approaches based on the following three
performance metrics:
▼ the number of cameras activated
▼ the coverage ratio, which represents the portion of the area
of an event that is covered by all of the actuated cameras
with respect to the total area of the prevailing events [4]
▼ the redundancy ratio, which is defined as the ratio of the
total portion of the superimposed region of the FOVs of
the actuated cameras pertaining to the prevailing event
zone to the total unique portion of the event region covered
by the actuated cameras.
The default parameters maintained during the simulation
as the camera numbers were changed are listed in Table 2. As
the number of cameras was altered, the number of scalars
deployed was assumed to be 320.
RESULTS AND DISCUSSION
We varied the number of camera sensors deployed and
marked the effect on the number of cameras activated, as portrayed in Figure 5. It was observed that the number of cameras activated rose gradually for all of the approaches since a
greater number of cameras came under the purview of the
event region. Moreover, it was found that the number of cameras activated in the proposed CMT-MDSD approach was
smaller than in the others [4]-[6].
Similarly, it is obvious from Figure 6(a) that, with an
increase in the number of cameras deployed, the coverage
Number of Cameras Activated
Algorithm 1
140
DCA-SC
DCCA-SM
120
100
CSC-SI
CMT-MDSD
80
60
40
20
0
0
180 220 260 300 340 380
Number of Cameras Deployed
420
FIGURE 5. The effect of varying numbers of cameras deployed on
the cameras activated.
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