This thesis develops new techniques for the analysis and design of energy-efficient, delay-tolerant wireless sensor networks. We describe how to efficiently manage the energy resource for two types of delay-tolerant networks: underwater ecological sensing networks, and terrestrial, energy-limited, mobile sensor networks. For underwater networks, we develop a distributed, scalable, energy-efficient medium access control (MAC) protocol that works despite long, unknown propagation delays of the underwater acoustic medium. In our protocol, the use of relative time stamps and the sleep mode of sensor units allow the nodes to operate in a synchronized environment. Further, we develop novel methods that are robust in that they adapt to the changes of a network such as channel variations, new node deployment, loss of synchronization, and node failures.

For delay-tolerant, energy-limited terrestrial networks, we develop methodologies for handling high mobility in order to manage the limited node energy supplies. We propose a novel framework to share, retain and refine end-to-end energy metrics in the joint memory of the nodes, over time scales over which this information can be spread to the network and utilized for energy planning decisions. We construct "energy maps," which are maps of the end-to-end energy metrics in space, in order to enable energy optimization in high-mobility networks. We show how to (1) compute the spatial derivatives of energy potentials in high-mobility networks, (2) construct energy maps on-demand via path integration methods, and (3) distribute, share, fuse, and refine energy maps over time by information exchange during encounters.

We develop an algorithm for energy optimization, based on the energy maps, that finds the optimal bit allocation strategy to minimize the energy consumption, subject to a delay constraint. We show that significant energy savings are obtained by leveraging network mobility and the energy maps, when compared with a competing algorithm that allocates the traffic at a constant rate without utilizing the energy map. These techniques enable energy optimization and planning for mobile energy-limited networks.