Energy measures for sensor networks

The problem of full data recovery from a sensor network of thousands of randomly-placed nodes is addressed as it relates to the complexity of the network data. In the applications of interest, the user requires a representation of the full two-dimensional data generated by a field underlying the area of the network. The usefulness of data recovery algorithms is judged by the units of energy consumption to recover sufficient data to reconstruct the field to a specified error. Energy consumption is measured primarily in units of data transmission and reception as these constitute the main limiter of node performance in current and envisioned networks.; A set of novel algorithms broken up into two major classes is simulated on several continuous test fields spanning a broad range of complexity. The first class consists of algorithms that transmit raw data directly to the user with no intra-network processing and differ in the mechanism for which data is selected. The second class consists of algorithms that rely on local data collection and compression prior to sending a full representation of the local field to the user. As the complexity changes, different algorithms become the best performers in terms of the proposed energy measures.; In addition to the continuous field tests, the algorithms are applied to discontinuous fields and images. A lower-bounding algorithm relying on regularly-placed nodes and hierarchical compression is analyzed. The best performing algorithms are simulated in a full system setting using a single energy metric, namely the overall energy consumption in the network in Joules. Depending on the complexity of the data, different strategies have the lowest overall energy cost.