In this paper we consider the problem of distributedconvergence to a Nash equilibrium based on minimalinformation about the underlying noncooperative game. We assume that the players/agents generate their actions based only on measurements of local cost functions, which are corrupted with additive noise. Structural parameters of theirown and other players’ costs, as well as the actions of the other players are unknown. Furthermore, we assume that theagents may have dynamics: their actions can not be changedinstantaneously. We propose a method based on a stochasticextremum seeking algorithm with sinusoidal perturbations and we prove its convergence, with probability one, to a Nashequilibrium. We discuss how the proposed algorithm can be adopted for solving coordination problems in mobile sensornetworks, taking into account specific motion dynamics of the sensors. The local cost functions can be designed such that some specific overall goal is achieved. We give an example in which each agent/sensor needs to fulfill a locally defined goal, while maintaining connectivity with neighboring agents. The proposed algorithms are illustrated through simulations.