Tom Schoellhammer

A system for simulation, emulation, and deployment of heterogeneous sensor networks

Recently deployed Wireless Sensor Network systems (WSNs) are increasingly following <i>heterogeneous</i> designs, incorporating a mixture of elements with widely varying capabilities. The development and deployment of WSNs rides heavily on the availability of simulation, emulation, visualization and analysis support. In this work, we develop tools specifically to support <i>heterogeneous</i> systems, as well as to support the measurement and visualization of <i>operational</i> systems that is critical to addressing the inevitable problems that crop up in deployment. Our system differs from related systems in three key ways: in its ability to simulate and emulate <i>heterogeneous</i> systems in their entirety, in its extensive support for integration and interoperability between motes and microservers, and in its unified set of tools that capture, view, and analyze real time debugging information from simulations, emulations, and deployments.

Lightweight temporal compression of microclimate datasets [wireless sensor networks]

Since the inception of sensor networks, in-network processing has been touted as the enabling technology for long-lived deployments. Radio communication is the overriding consumer of energy in such networks. Therefore, data reduction before transmission, either by compression or feature extraction, will directly and significantly increase network lifetime. This paper evaluates a simple temporal compression scheme designed specifically to be used by mica motes for the compaction of microclimate data. The algorithm makes use of the observation that over a small enough window of time, samples of microclimate data are linear. It finds such windows and generates a series of line segments that accurately represent the data. It compresses data up to 20-to-1 while introducing errors in the order of the sensor hardwares specified margin of error. Furthermore, it is simple, consumes little CPU and requires very little storage when compared to other compression techniques. This paper describes the technique and results using a dataset from a one-year microclimate deployment.