Wireless sensor and actuator networks combine a large number of sensors and a lower number of actuators that are connected with wireless medium, providing distributed sensing and executing appropriate tasks in a special region of interest. To accomplish effective sensing and acting tasks, efficient coordination mechanism among the nodes is required. As an attempt in this direction, this paper develops a collaborative control and estimation mechanism, which addresses the nodes coordination in a distributed manner. First, we propose a regional controllability-based virtual force algorithm as an actuator deployment strategy to enhance area coverage after an initial random placement of actuators. During this process, a dynamic coordination mechanism is adopted to control nodes. This mechanism incorporates two components, namely, proportional-integral-derivative neural network and recursive least squares-based Kalman filter algorithms. Taking advantage of feedback control and online learning technology, the proposed coordination mechanism schedules the corresponding nodes on the basis of the characteristics of current events, utilizes proportional-integral-derivative neural network controller inside each actuator to improve system transient and steady-state responses, and deals with system state/parameter estimation problems according to the recursive least squares-based Kalman filter algorithm, so as to achieve better control accuracy. Simulations demonstrate the effectiveness of our proposed methods.