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Wireless sensor networks (WSNs) are composed of a large number of inexpensive power-constrained wireless sensor nodes, which detect and monitor physical parameters around them through self-organization. Utilizing clustering algorithms to form a hierarchical network topology is a common method of implementing network management and data aggregation in WSNs. Assuming that the residual energy of nodes follows the random distribution, we propose a load-balanced clustering algorithm for WSNs on the basis of their distance and density distribution, making it essentially different from the previous clustering algorithms. Simulated tests indicate that the new algorithm can build more balanceable clustering structure and enhance the network life cycle.