  Every robotic network cloud system can be seen as a graph with nodes as
hardware with independent computational processing powers and edges as data
transmissions between nodes. When assigning a task to a node we may change
several values corresponding to the node such as distance to other nodes, the
time to complete all of its tasks, the energy level of the node, energy
consumed while performing all of its tasks, geometrical position, communication
with other nodes, and so on. These values can be seen as fingerprints for the
current state of the node which can be evaluated as a subspace of a hyperspace.
We proposed a theoretical model describing how assigning tasks to a node will
change the subspace of the hyperspace, and from that, we show how to obtain the
optimal task allocation. We described the communication instability between
nodes and the capability of nodes as subspaces of a hyperspace. We translate
task scheduling to nodes as finding the maximum volume of the hyperspace.
