Comparing the performance of CSA with mean-field annealing, genetic algorithm, and Immune genetic algorithm in solving broadcast scheduling problems runs a series of simulations. The simulation results for the number of nodes | N |, the number of slots | M |, and the network degree are discussed in the following sections. Smart healthcare channel utilization for the entire network is defined as a fitness function factor.
α=\(\frac{1}{M\text{*}N\text{*}F}\left(\sum _{i=0}^{M}\sum _{j=0}^{N}\sum _{k=0}^{F}\left({TSCHM}_{ijk}\right)\right)\) (3)
Average time delay
β=\(\frac{N\text{*}F}{M}\sum _{i=0}^{M}\left[\frac{1}{\sum _{j=0}^{N}\sum _{k=0}^{F}\left({TSCHM}_{ijk}\right)}\right]\) (4)
For optimal smart healthcare network design, the average time delay for every node is calculated by the average availability of the network in minimum turnaround time.
Using python simulation results arrived out for smart healthcare sensor node on various parameters using Table 1. The number of nodes used various between 10 to 100 nodes. In a ten node simulation setup for smart healthcare sensor nodes, every node 5-time slots are utilized.
5.1 Simulation Results For Ga
The motivation behind the first reenactment was to research the execution of hereditary calculation for various organizations appeared in Table 1. The quantity of hubs taken for reenactment goes from five to a hundred. More modest hub organizations performed by more digit of broadcast in a worthy age. Notwithstanding, a 100 nodes organization with 198 edges with the level of nine distinguishes the ideal arrangement TSCH outline after 489 generations. The regular digit of generation for the 100 nodes system is 99.1. This must stay diminished to reduce the performance time.
Table 1
Genetic Algorithm using TSCH simulation results
Number of smart healthcare nodes
|
Number of links
|
Average degree
|
Average
ND
|
Maximum ND
|
Maximum TSCH frame length
|
Avg (α)
|
The average number of generations
|
Computation time
|
10
|
23
|
4.2
|
4
|
5
|
6
|
0.215
|
31.1
|
0.582s
|
25
|
45
|
5.3
|
5
|
7
|
8
|
0.106
|
39.4
|
1.06min
|
30
|
68
|
5.4
|
5.3
|
7
|
8
|
0.117
|
42.5
|
1.82min
|
45
|
99
|
6
|
7
|
9
|
10
|
0.123
|
46.3
|
2.23min
|
60
|
109
|
6.4
|
7.5
|
9
|
10
|
0.112
|
52
|
4.59min
|
80
|
155
|
7.3
|
8.1
|
10
|
11
|
0.102
|
64.3
|
6.25min
|
100
|
198
|
7.8
|
8.2
|
10
|
11
|
0.105
|
99.1
|
15min
|
Table 2
Immune Genetic Algorithm using TSCH simulation results
Number of smart healthcare nodes
|
Number of links
|
Average degree
|
Average
ND
|
Maximum ND
|
Maximum TSCH frame length
|
Avg (α)
|
Average number of generations
|
Computation time
|
10
|
20
|
3.4
|
4
|
5
|
6
|
0.259
|
17.2
|
0.3s
|
15
|
30
|
4
|
4
|
5
|
6
|
0.210
|
18.1
|
0.49s
|
20
|
35
|
4.2
|
6
|
7
|
8
|
0.205
|
18.9
|
0.69s
|
30
|
60
|
4
|
6
|
7
|
8
|
0.193
|
20.9
|
18s
|
45
|
90
|
4.3
|
6
|
8
|
9
|
0.181
|
33.2
|
3.5min
|
60
|
110
|
4
|
7
|
8
|
9
|
0.171
|
59.4
|
10.41min
|
75
|
155
|
4
|
7
|
8
|
9
|
0.165
|
65.4
|
2.19min
|
85
|
170
|
4
|
7
|
8
|
9
|
0.153
|
89.1
|
14.98min
|
100
|
210
|
5
|
8
|
9
|
10
|
0.116
|
97.51
|
25.67min
|
Table 3
Cuckoo Search Algorithm using TSCH simulation results
Number of smart healthcare nodes
|
Number of links
|
Average degree
|
Average
ND
|
Maximum ND
|
Maximum TSCH frame length
|
Avg (α)
|
Average number of generations
|
Computation time
|
60
|
90
|
3.5
|
5
|
6
|
7
|
0.201
|
4.03
|
6s
|
90
|
160
|
3.9
|
7
|
10
|
11
|
0.163
|
6
|
10s
|
110
|
152
|
3
|
6.8
|
10
|
11
|
0.121
|
8.49
|
1.5min
|
120
|
200
|
4.2
|
7.3
|
11
|
12
|
0.130
|
16.48
|
1.9min
|
150
|
250
|
5
|
7
|
11
|
12
|
0.125
|
17.21
|
2.5min
|
200
|
410
|
4
|
7.5
|
8
|
9
|
0.130
|
20.33
|
9.08min
|
250
|
460
|
5
|
7
|
10
|
11
|
0.126
|
28.19
|
11.2min
|
300
|
600
|
4
|
9
|
9
|
10
|
0.139
|
52.14
|
28.9min
|
350
|
650
|
5
|
9
|
9
|
10
|
0.111
|
56.15
|
32.2min
|
400
|
800
|
4
|
8
|
15
|
16
|
0.148
|
58.12
|
55.1min
|
450
|
850
|
5
|
12
|
13
|
14
|
0.121
|
65.21
|
62.2min
|
500
|
1010
|
4
|
9
|
14
|
15
|
0.129
|
85.02
|
65.12min
|
5.2 Simulation Results For Iga:
For varying numbers of nodes and edges, Table 2 significantly outperforms IGA's output.
When compared to GA, information included in IGA might vastly improve the searching capacity and versatility, as well as dramatically speed up the process. They chose an antigen that is improved with a larger number of transmissions at the same time as the vaccination measure, to use the channel to connect with many nodes at the same time without interference. When comparing the reproduction aftereffects of IGA to GA in Table 1, the quantity of generation is reduced, while the normal number of transmissions of each network is improved. The arrangement is connected to satisfactory age for networks with 85 and 100 nodes. The best arrangement is recognized in 14 minutes and 23 minutes for a 100-node network with normal levels of four and five, respectively. In any event, IGA meets the first two criteria; however, the third, namely, the running time of a large organization, is not reduced.
Table 4 compares the total number of transmissions generated by MA for different node networks with different time slots to GA and IGA.
Table 4
Comparison of CSA with GA and IGA in terms of number of transmissions
No. of
nodes
|
Time
slots |M|
|
GA
|
IGA
|
CSA
|
10
|
7
|
15
|
19
|
33
|
25
|
9
|
32
|
35
|
43
|
35
|
12
|
47
|
49
|
58
|
35
|
13
|
53
|
55
|
62
|
40
|
8
|
67
|
69
|
79
|
40
|
9
|
81
|
84
|
91
|
50
|
10
|
88
|
94
|
103
|
5.3 Simulation Results For Csa:
CSA aims to achieve convergence speed by simulating the results of aggregation as the number of transmissions increases, while other methods aim at efficiency. The results of IGA and GA can be seen in Tables 1 and 2, which demonstrate a reduction in generation size and computation time while increasing channel utilization. Table 3 shows the average channel use, average number of generations, and calculation time for various networks that use CSAs at various degrees of networks. CSA takes 1.5 minutes for a 110-node network, which is faster than IGA, which takes 26.61 minutes for the same network. IGA and other recently proposed efficient methods do not have much efficiency for networks with more than 100 nodes compared to CSA. This is the key benefit of CSA.
By comparing CSA and GA, it is faster to compute optimal solutions and generate more generations to identify optimal solutions. In Fig. 6, CSA and modified genetic algorithm are compared by the average time taken. Figure 7 compares the time delay of different calculations with CSA.
CSA Algorithm
Experimental Result:
An image of IoT nodes is shown in Fig. 8. This proposed project was implemented in an IoT environment with 21 nodes. As shown in Fig. 9, the nodes are randomly placed, and their one-hop and two-hop connections are shown. A CM, HM, and SM network structure is calculated based on the network structure. Taking the SM as input, the proposed algorithm in section 5 is employed to increase the number of transmissions. Figure 4 shows optimization results for the implemented real-time scenario in terms of average degree, throughput, and computation time. As can be seen in the table, the 21-node network provides the maximum throughput.
Table 5
No. of nodes
|
Average degree
|
Throughput
|
Computation time
|
21
|
3.2
|
30
|
1.01s
|
21
|
3.3
|
32
|
0.9s
|
21
|
3.3
|
30
|
1.2s
|
21
|
3.2
|
32
|
1.3s
|
21
|
3.9
|
29
|
1.4s
|