A dynamic heuristic for WSNs routing

Figures & data

Figure 1. Weighted graph and its minimum spanning tree

Figure 2. PRIM’s algorithm

Figure 3. Nodes coverage in WSN

Figure 4. Tabu Node Selection Algorithm (TNS)

Figure 5. Nodes positions with 100 × 100 grid

Table 1. WNS benchmarks

#	N	Area	Nd
I1	100	100x100	0.01
I2	100	200x200	0.0025
I3	100	300x300	0.0011
I4	100	400x400	0.00625
I5	200	100x100	0.02
I6	200	200x200	0.005
I7	200	300x300	0.0024
I8	200	400x400	0.00125
I9	300	100x100	0.03
I10	300	200x200	0.075
I11	300	300x300	0.0033
I12	300	400x400	0.0018
I13	400	100x100	0.02
I14	400	200x200	0.01
I15	400	300x300	0.004
I16	400	400x400	0.025
I17	100	1000x1000	0.0001

Table 2. Simulation parameters

Parameters	Value
Initial energy E0	200 J
Transmitting and receiving energy Eelec	5 nJ/bit
Amplification energy for short distance Ef	10 Pj/bit/m^2
Amplification energy for long distance Eamp	0.013 pJ/bit/m^4
Packet size	200 bits
Operating frequency	2.4 GHz
Transmit power	32 mW
Reference distance	3 m
Path loss exponent	4
SNR	2 dB

Figure 6. Residual energy for energy level 10%

Figure 7. Throughput for energy level 10%

Figure 8. Network lifetime for energy level 10%

Figure 9. Throughput for energy level 30%

Figure 10. Network lifetime for energy level 30%

Figure 11. Residual energy for energy level 30%

Figure 12. Residual for energy level 40%

Figure 13. Throughput for energy level 40%

Figure 14. Network lifetime for energy level 40%

Figure 15. Residual energy for energy level 50%

Figure 16. Throughput for energy level 50%

Figure 17. Network lifetime for energy level 50%

Table 3. Comparison with E2S-DL (Sinde et al., 2020)

Energy Level	parameter	Tabu-MST Algorithm	E2S-DRL
50%	Througput	88%	92%
	Residual Energy	37%	30%
	Network lifetime	6345 rounds	6450 rounds

Sinde, R., Begum, F., Njau, K., & Kaijage, S. (2020). Refining network lifetime of wireless sensor network using energy-efficient clustering and DRL-based sleep scheduling. Sensors, 20 (5), 1540. Basel, Switzerland. https://doi.org/10.3390/s20051540

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