  The timing-based localization, which utilize the triangulation principle with
the different arrival time of gamma-ray photons, with a fleet of Cubesats is a
unique and powerful solution for the future all-sky gamma-ray observation,
which is a key for identification of the electromagnetic counterpart of the
gravitational wave sources. The Cubesats Applied for MEasuring and Localising
Transients (CAMELOT) mission is now being promoted by the Hungarian and
Japanese collaboration with a basic concept of the nine Cubesats constellations
in low earth orbit. The simulation framework for estimation of the localization
capability has been developed including orbital parameters, an algorithm to
estimate the expected observed profile of gamma-ray photons, finding the peak
of the cross-correlation function, and a statistical method to find a best-fit
position and its uncertainty. It is revealed that a degree-scale localization
uncertainty can be achieved by the CAMELOT mission concept for bright short
gamma-ray bursts, which could be covered by future large field of view
ground-based telescopes. The new approach utilizing machine-learning approach
is also investigated to make the procedure automated for the future large scale
constellations. The trained neural network with 10$^6$ simulated light curves
generated by the artificial short burst templates successfully predicts the
time-delay of the real light curve and achieves a comparable performance to the
cross-correlation algorithm with full automated procedures.
