A DICLA firstly is a CA in which a learning automaton is assigned to every cell.
The current value of n for a particular node determined by n = f(n) according to its current energy level and its neighbors using the principle of DICLA. Maximum and minimum thresholds of parameter n are also used to avoid the incomprehensible value.
* The dynamic mechanism is imported from LA to adjust the parameter n, and DICLA is used to characterize the mobility of nodes in the opportunistic networks.
However, diversification of energy consumption of nodes and their current neighbors are considered using the DICLA  method.
We use the DICLA model to illustrate dynamic topology of opportunistic networks.