Sensor networking has emerged as a promising tool for monitoring and actuating the devices of the physical world, employing self-organizing networks of battery-powered wireless sensors that can sense, process, and communicate. Such networks can be rapidly deployed at low cost, enabling large-scale, on-demand monitoring and tracking over a wide area. Energy is the most crucial and scarce resource for such networks. However, since sensor network applications generally exhibit specific limited behaviors, there is both a need and an opportunity for adapting the network architecture to match the application in order to optimize resource utilization. Many applications-such as large-scale collaborative sensing, distributed signal processing, and distributed data assimilation-require sensor data to be available at multiple resolutions, or allow fidelity to be traded-off for energy efficiency. In this thesis, I develop an adaptive cross-layered sensor network architecture that enables multi-scale collaboration and communication. Analyzing the unique characteristics of sensor networks, I identify cross-layering and adaptability to applications as the primary design principles needed to build three closely coupled-protocols: (1) a self-organizing adaptive hierarchical data service for multi-scale communication, together with communication primitives to simplify application design; (2) a medium scheduling protocol tailored for this hierarchical data service, to take advantage of the communication and routing characteristics to achieve close to optimal latency and energy usage; and (3) an adaptive clock synchronization service, which provides an analytical framework for mapping clock synchronization requirements to actual protocol parameters, to provide required synchronization. I have analyzed as well as simulated the performance of these protocols to show optimized energy utilization.