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It has been discussed in the literature that the mobility of a mobile sensor network (MSN) can be used to improve its sensing coverage. How the mobility can efficiently be managed toward a better coverage, however, remains unanswered. In this paper, motivated by classical dynamics that study the movement of objects, we propose the concept of network dynamics and define the associated potential functions that capture the operational goals, as well as the environment of an MSN. We find that in managing the mobility of an MSN, Newton's laws of motion in classical dynamics are insufficient, for they introduce oscillations into the movement of sensor nodes. Instead, in network dynamics, the laws of motion are formulated using the steepest descent method in optimization. Based on the network dynamics model, we first devise a parallel and distributed algorithm (parallel and distributed network dynamics (PDND)) that runs on each sensor node to guide its movement. PDND then turns sensor nodes into autonomous entities that are capable of adjusting their locations according to the operational goals and environmental changes. After that, we formally prove the convergence of PDND. Finally, we apply PDND in three applications to demonstrate its effectiveness.