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We propose the adaptive control and reconfiguration schemes for mobile wireless sensor networks (MWSN) to achieve timely and accurate mobile multi-target tracking (MMTT) with cost-effective energy consumption. In particular, our proposed schemes can detect the mobile multi-targets' random appearance and disappearance in the clutter environments with high accuracy and low energy cost. We develop the optimal mutual-information based techniques to adaptively control the reconfiguration of the proposed MWSN by designing the Distributed/Decentralized Probability Hypothesis Density (DPHD) filtering algorithms. By dynamically adjusting the sensors' states, including their positions and activations, our schemes can efficiently improve the observabilities of the tracked multi-targets. We further analyze the asymptotic performance of our proposed schemes by deriving the upper-bounds of the detection-error probabilities. Also presented are the performance analyses which validate and evaluate our proposed adaptive control and reconfiguration schemes for MWSN in terms of the multi-target states estimation accuracy, the energy-consumption efficiency, and the robustness to the interference/noise.