  The Internet of Bio-Nano-Things is a new paradigm that can bring novel
remotely controlled actuation and sensing techniques inside the human body.
Towards precise bionano sensing techniques in the brain, we investigate the
challenges of modelling spatial distribution of astrocyte networks in
developing a mathematical framework that lay the groundwork for future
early-detection techniques of neurodegenerative disease. In this paper, we
investigate the effect of the $\beta$-amyloid plaques in astrocytes with the
Alzheimer's disease. We developed a computation model of healthy and
Alzheimer's diseases astrocytes networks from the state of the art models and
results that account for the intracellular pathways, IP$_3$ dynamics, gap
junctions, voltage-gated calcium channels and astrocytes volumes. We also
implemented different types of astrocytes network topologies including shortcut
networks, regular degree networks, Erd\"os R\'enyi networks and link radius
networks. A proposed multi-scale stochastic computational model captures the
relationship between the intracellular and intercellular scales. Lastly, we
designed and evaluated a single-hop communication system with frequency
modulation using metrics such as propagation extend, molecular delay and
channel gain. The results show that the more unstable but at the same time
lower level oscillations of Alzheimer's astrocyte networks can create a
multi-scale effect on communication between astrocytes with increased molecular
delay and lower channel gain compared to healthy astrocytes, with an elevated
impact on Erd\"os R\'enyi networks and link radius networks topologies.
