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In a heterogeneous wireless sensor networks, there are usually a number of inexpensive energy constraint sensor nodes that collect sensing data from the sensing environment, and then these nodes will cooperatively transmit the sensing data to the improved cluster head. The joint design of balanced energy consumption among member nodes and network lifetimes prolonging can be achieved by employing clustering techniques in such networks. In classical clustering techniques, clustering and in-cluster data rate allocation are usually established as independent items. Although separate considerations of these two items can simplify the networking design, it is often the non-optimal performances for wireless sensor networks. This paper proposes an integral framework, in which these two correlated items are integrated as an interactive entirety. For that, we develop the clustering problems using mixed integer nonlinear programming. The evolutionary process of clustering is provided in simulations. Results validate that our joint-design proposal reaches a near optimal match between member nodes and cluster heads.