Statistics yields better data with less battery power
Context: Sensor networks are collections of small devices that measure local conditions like temperature or light intensity. The devices, sometimes called sensor “motes,” transmit data wirelessly to monitoring stations. Sensor networks could let soldiers track enemy tanks remotely, engineers gauge the structural integrity of buildings, or scientists monitor animals in their natural habitats. But one barrier to their widespread use is the difficulty of coaxing reliable information from motes whose batteries are low or whose connectivity is intermittent. Motes tend to be widely scattered and often malfunction; but a sensor network that represents the world inaccurately or incompletely is of limited use. Now researchers at Intel Research Berkeley and their collaborators have shown that new statistical techniques can compensate for some of these flaws.
Methods and Results: A statistical model based on previous data from a sensor network can correct for biases caused by malfunctioning or poorly placed sensors. It can also tailor the network’s performance to specific tasks: someone querying the network can specify the accuracy of the response, and the model will automatically determine which data are needed. Pulling in just the required data means that precious battery power is not wasted on extra measurements and transmissions. Amol Deshpande and his colleagues developed a query system enhanced with statistical techniques and tested it in sensor motes deployed over a redwood tree in Berkeley, CA. The system answered queries – such as what the temperature at a certain spot was – highly accurately (95 percent confidence), often performing only one-fortieth the number of observations previously required.
Why it Matters: Sensor networks promise to transform environmental monitoring, military surveillance, and inventory management. Now, the data that a sensor network gathers can be interpreted and used with more confidence. When high accuracy is necessary, a network using a statistical model like Intel’s will collect more data. When rougher estimates are acceptable, using statistical models reduces the battery power consumed by sensor motes. A longer battery life for sensor motes is a great boon, especially for motes distributed in hostile or inaccessible terrains. Although Intel’s models and methods can still be improved, its technique greatly expands sensor networks’ utility in the real world.
Source: Deshpande, A. et al. 2004. Model-driven data acquisition in sensor networks. Proceedings of the 30th International Conference on Very Large Data Bases, pp. 588-599.