Challenges for Data Mining in Distributed Sensor Networks

The way of collecting sensor data faces a revolution when the newly developing technology of distributed sensor networks becomes fully functional and widely available. Smart sensors acquire full interconnection capabilities with similar devices, so that run-time data aggregation, parallel computing, and distributed hypothesis formation become reality with off-the-shelf components and sensor boards. This revolution started around in 1996, and now hardware and network are converging on the first convincing solutions. Exploring and exploiting this paradigm are a renovated challenge for the pattern recognition and data mining community. This paper attempts a survey on state-of-the-art of wireless sensor technology, with an eye on data-related problems and technological limits. Although the possibilities seem promising, the limited computational resources of individual nodes hamper the elaboration of data with computationally-intensive algorithms. New software paradigms must be developed, both creating new techniques or adapting, for network computing old algorithms of earlier ages of computing