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Wireless sensor (and actuator) networks have critical applications in industrial, scientific, medical, and military domains. Envisioned applications require large networks consisting of hundreds, thousands, or even millions of inexpensive wireless sensor nodes with bandwidth and energy constraints. Distributed self-organization, which involves network decomposition into connected clusters, is a fundamental problem in this environment. In this dissertation, we address three challenges in the self-organization of wireless sensor networks: (i) message efficiency, which is critical in the bandwidth and energy constrained sensor network environment, (ii) bounding cluster size, which has a significant impact on routing efficiency and overall network performance, and (iii) design of timers for clustering, in order to control the size and number of clusters produced. We first present a novel approach for message-efficient clustering, in which nodes allocate local growth budgets to neighbors. We introduce two algorithms that make use of this approach. Unlike the earlier and commonly used expanding ring approach, our algorithms do not involve the initiator in each round, and do not violate the specified maximum cluster size at any time. We derive analytical performance bounds of our algorithms and also provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the expanding ring approach. Next, we present a new methodology for designing initiator timers. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition using this methodology. This bound is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators, significantly reduces the network decomposition time, and most importantly does not require knowledge of the network topology. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks. This work can be applied to achieve rapid and efficient self-organization of large wireless sensor networks and can also be used in other applications that require distributed algorithms for bounded size clustering.