A method for dynamically adapting the sending period, T_diff, for periodic messages transmitted by a node of a wireless communication network of ad-hoc nodes to nodes of the network, said method being characterised in that the node comprises an evaluation module adapted to determine the next period to be applied and comprising a neural network derived from a prior learning phase and such that the neural network computes, as a function of a vector of parameter(s) that it is provided with as input and qualifying the current stability of the neighbouring links of the node, a score (Q_value_1,...., Q_value_n) for each one of a plurality of distinct sending period candidate values (T1,..., Tn) of said messages from the node, said score evaluating the relevance of implementing said sending period value as a function of said vector of parameter(s); the method further comprising the following steps implemented by said node every T_calc seconds, with T_calc > T_diff: - providing the evaluation module with an updated value of the vector of parameter(s) qualifying the current stability of the neighbouring links of the node; - computing, by means of the neural network, scores (Q_value_1,..., Q_value_n) each associated with a distinct sending period value (T 1,..., T n) as a function of said updated value of the vector of parameter(s); - selecting, by means of the evaluation module, one of said distinct sending period candidate values (T 1,..., T n) as a function of the computed scores; - assigning the selected value to T_diff for the duration T_calc. said method being characterised in that the node comprises an evaluation module adapted to determine the next period to be applied and comprising a neural network derived from a prior learning phase and such that the neural network computes, as a function of a vector of parameter(s) that it is provided with as input and qualifying the current stability of the neighbouring links of the node, a score (Q_value_1,...., Q_value_n) for each one of a plurality of distinct sending period candidate values (T1,..., Tn) of said messages from the node, said score evaluating the relevance of implementing said sending period value as a function of said vector of parameter(s); the method further comprising the following steps implemented by said node every T_calc seconds, with T_calc > T_diff: - providing the evaluation module with an updated value of the vector of parameter(s) qualifying the current stability of the neighbouring links of the node; - computing, by means of the neural network, scores (Q_value_1,..., Q_value_n) each associated with a distinct sending period value (T 1,..., T n) as a function of said updated value of the vector of parameter(s); - selecting, by means of the evaluation module, one of said distinct sending period candidate values (T 1,..., T n) as a function of the computed scores; - assigning the selected value to T_diff for the duration T_calc. - providing the evaluation module with an updated value of the vector of parameter(s) qualifying the current stability of the neighbouring links of the node; - computing, by means of the neural network, scores (Q_value_1,..., Q_value_n) each associated with a distinct sending period value (T 1,..., T n) as a function of said updated value of the vector of parameter(s); - selecting, by means of the evaluation module, one of said distinct sending period candidate values (T 1,..., T n) as a function of the computed scores; - assigning the selected value to T_diff for the duration T_calc.