Abstract: Mobile delay-tolerant sensor networks are becoming increasingly important because of their ability to deliver long periods of fine-grained sensing over a wide area with a small number of nodes. A key challenge in these systems, however, is that nodes are extremely energy constrained since they must be small, lightweight, and function autonomously for months at a time. This problem is compounded by the fact that mobile nodes demand radios with relatively long ranges to maximize the effectiveness of short, infrequent communication periods. This dissertation explores energy conservation techniques for these networks from a variety of system viewpoints. From the application's viewpoint, it introduces a family of lossless compression algorithms tailored to sensor networks. These algorithms include a novel LZW variant that exploits characteristic patterns of sensor data to reduce energy consumption by more than 40% as well as further data transforms that can take advantage of the structure of the data to decrease energy consumption by nearly a factor of three. Then, this dissertation explores a data abstraction layer for mobile sensor networks that lies between the file system and the application to reorganize data from the network's viewpoint. This organization facilitates the development of services for data identification, search, and reduction, which combine to save energy by making communications more efficient. Finally, it looks at system-specific energy trade-offs throughout the system, from the hardware-software interface to the application. For reference, this dissertation explores two detailed case studies from the ZebraNet project, in which we designed, developed, and deployed an energy-aware mobile sensor network that employed peer-to-peer protocols and mobile ad-hoc networking technologies to help track zebra migrations in ways not possible with current animal tracking technology. As energy is the fundamental constraint in mobile delay-tolerant sensor networks, interest in novel energy conservation techniques for these systems continues to grow. This dissertation explores the energy issues inherent to mobile sensor nodes and proposes numerous energy-saving methods that have the potential to greatly increase system lifetime and usability.