This paper presents a sensor scheduling algorithm in a classical range-only target tracking application. Since only a small portion of deployed sensors can provide useful information about the position of target at a specific time instant, clustering method is utilized and Dynamic Cluster Scheduling (DCS) is applied instead of scheduling the individual sensor nodes. Particle filtering algorithm is employed for the tracking task, by processing data provided by the sensor nodes in the active cluster Active cluster is updated at each time step by comparing the position of master node in every cluster and the estimated target position. Cramer-Rao Bound is used as a comparison criteria for the proposed scheduling method. Simulation results show that activated sensor nodes during the overall tracking task in the region of interest are quite close to the scheduling results obtained with the Posterior Cramer-Rao Bound which sets a theoretical lower limit on the estimator performance.