Vmstar

Sensor networks are being deployed at massive scales, containing a range of platforms. Programming paradigms for sensor networks should meet the attendant challenges of scale and heterogeneity. Researchers have considered virtual machines as a means to address these challenges. However, in order to satisfy the resource limitations of sensor nodes, they export only a minimal set of services to the application programmer. This makes applications of even moderate complexity difficult to implement. We present VMSTAR --- a framework for building resource-efficient virtual machines that scale and export comprehensive service suites on a per-application basis. We advocate the use of fine-grained software synthesis to build resource-efficient system software, and facilitate both application changes and system software upgrades at runtime through an efficient incremental update scheme. We have used our framework to build virtual machines on the Mica platform and describe how virtual machines are effective in meeting the difficult demands of heterogeneity and reprogrammability.