Sensor networks are fundamentally constrained by the difficulty and energy expense of delivering information from sensors to sink. Our work has focused on garnering additional significant energy improvements by devising computationally-efficient lossless compression algorithms on the source node. These reduce the amount of data that must be passed through the network and to the sink, and thus have energy benefits that are multiplicative with the number of hops the data travels through the network.Currently, if sensor system designers want to compress acquired data, they must either develop application-specific compression algorithms or use off-the-shelf algorithms not designed for resource-constrained sensor nodes. This paper discusses the design issues involved with implementing, adapting, and customizing compression algorithms specifically geared for sensor nodes. While developing Sensor LZW (S-LZW) and some simple, but effective, variations to this algorithm, we show how different amounts of compression can lead to energy savings on both the compressing node and throughout the network and that the savings depends heavily on the radio hardware. To validate and evaluate our work, we apply it to datasets from several different real-world deployments and show that our approaches can reduce energy consumption by up to a factor of 4.5X across the network.