Page 129 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 3 – Internet of Bio-Nano Things for health applications

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 3

13 7 1
2 15
4 14
5
11
10
9
8 3
350
6 12
0
300
Fig. 7 – Layout for the simulation organism.
Table 2 – Simulation parameters 250
Name Value Unit Total Transmission
Vesicle size 40000 molecules 200
2
Diffusion constant 1 × 10 −12 /
Radius 0.5 150
Offsprings = 2
Exhausted node ratio 0.2 Offsprings = 4
# of Vesicles per node 40 100 Offsprings = 8
Offsprings 2 0 200 400 600 800 1000
Node count 16 Evolution
Dimensions 1 × 2 × 10 × ×
Change amplitude 3 Fig. 8 – Performance of evolution with different child numbers.
4. VESICLE COUNT OPTIMIZATION 450

In this section, we use our approach to optimize a ran‑ 400
domly generated organism. The only design constraint
we have is introducing a separation between the sink and 350
the sensors, i.e., the probability of any sensor successfully
sending information to the sink without the relay nodes Total Transmission 300
is small enough to be ignored. The simulation parameters
and the simulation organism are given in Table 2 and 250
Amplitude = 1
Fig. 7 respectively. The selected simulation parameters Amplitude = 2
are all arbitrary; however, one can alter the time scale, 200 Amplitude = 3
Amplitude = 4
diffusion constant, radii, and the organism size easily to
Amplitude = 5
it them to an actual organism. 150
0 200 400 600 800 1000
Firstly, we investigate the effect of the number of offspring Evolution
per organism. Since the population is kept constant, the
Fig. 9 – Performance of evolution with different amplitude values.
selection rate is proportional to the inverse of the off‑
spring per organism.
changes in the resource distribution set the organism
Child count change back. Fig. 9 displays the performance of evolution with
different change amplitudes.
Changing the child count for each stage has dramatic ef‑
Inspecting Fig. 9, we also realise that the performance of
fects on the performance of the organism. These effects
= 1 line is inferior for the irst hundred it‑
are visible in Fig. 8. A smaller number of offspring imply
erations of evolution. This is obviously due to the small
that more parents join in the creation of the next genera‑
increases due to the limited inter‑generational changes.
tion. As a result, there is less uncertainty in the next gen‑
However, the same factor boosts the inal performance of
eration. More offspring increase the uncertainty. If one
the organism, which veri ies the biological evolution, i.e.,
of the parents reached their performance mostly due to
small changes advance the organisms while huge
luck, most of the offspring in the next generation becomes
changes are not sustainable.
inferior. This situation leads to huge discrepancies in per‑
formance between generations.
5. CONCLUSION
Amplitude change
In this paper, we simulate the resource allocation in an or‑
Increasing the amplitude of change inter‑generations has ganism, having nodes communicating via MC, using evo‑
a dramatic impact on the system performance. For large lutionary game theory. We propose a two‑staged evo‑
amplitudes, the evolution fails to reach its potential. Once lution process realized by selecting the organisms with
the organism reaches a certain performance, the huge

© International Telecommunication Union, 2021 117