def update_energy_levels(self, parameter):
"""
Update the battery & virtual energy level according to the involution expression \eqref{10}.
:param parameter: the instance of class Parameter
:return: no return
"""
for i in range(self.__user_num):
self.__battery_energy_levels[i] = self.__battery_energy_levels[i] - ToolFunction.obtain_transmit_energys(
self.edge_selections[i], parameter, self.__connectable_distances[i]) - \
parameter.get_local_exe_energy() + self.obtain_harvested_energy(parameter)
self.__virtual_energy_levels[i] = self.__battery_energy_levels[
i] - parameter.get_perturbation_para()
def sub_problem_es(self, parameter):
"""
Calculate the optimization goal of sub-problem $\mathcal{P}_2^{es}$.
:param parameter: the instance of class Parameter
:return: the optimization goal of $\mathcal{P}_2^{es}$
"""
distances = self.get_connectable_distances()
optimization_goals = 0
for i in range(len(self.edge_selections)):
edge_selection = self.edge_selections[i]
distance = distances[i]
optimization_goal = parameter.get_v() * self.obtain_overall_costs(parameter) - \
parameter.get_local_exe_energy() - ToolFunction.obtain_transmit_energys(
edge_selection, parameter, distance)
optimization_goals += optimization_goal
return optimization_goals
def obtain_overall_costs(self, parameter):
"""
Calculate 'V * overall_costs + Lyapunov_drift', which is the sum of it of each mobile device.
:param parameter: the instance of class Parameter
:return: V * overall costs + \Delta(\Theta)
"""
overall_costs = 0
for i in range(len(self.edge_selections)):
transmit_times = ToolFunction.obtain_transmit_times(
self.edge_selections[i], parameter,
self.__connectable_distances[i])
edge_exe_times = ToolFunction.obtain_edge_exe_times(
self.edge_selections[i], parameter)
edge_times = transmit_times + edge_exe_times
division = sum(self.edge_selections[i])
is_dropped = False if division else True
overall_cost = max(edge_times) + parameter.get_local_exe_time() + parameter.get_coordinate_cost() * \
sum(self.edge_selections[i]) + is_dropped * parameter.get_drop_penalty()
neg_lyapunov_drift = (parameter.get_local_exe_energy() + ToolFunction.obtain_transmit_energys(
self.edge_selections[i], parameter, self.get_connectable_distances()[i])) * \
self.get_virtual_energy_levels()[i]
overall_costs += overall_cost - neg_lyapunov_drift
return overall_costs