Presentation on theme: "1 Searching Internet of Sensors Junghoo (John) Cho (UCLA CS) Mark Hansen (UCLA Stat) John Heidemann (USC/ISI)"— Presentation transcript:
1 Searching Internet of Sensors Junghoo (John) Cho (UCLA CS) Mark Hansen (UCLA Stat) John Heidemann (USC/ISI)
2 Todays Sensornet Islands of private sensor network sensornet
3 Internet of Sensors Public network of multiple sensor networks the Internet sensornet
4 Benefits of Internet of Sensors Easy sharing and easy access Leverage what is available and contribute what is missing Freely combine data from multiple sources Enable unpredicted use of sensor data the Internet the sensornet (aggregate)
5 Challenges Scale –Potentially millions of sensors No central control –New sensor may pop up anywhere, anytime –No central registry Variation in data quality –Structured, well-managed sensornet –Citizen-initiated sensornet How to discover? What to trust? Which one to use?
6 Sensor Search Engine Data broker between sensornet publisher and data consumer Crawl and index sensor data –Starting from known sensors and by following republication chain Ranking and clustering sensors –Based on their quality, data type, geo-location, trustworthiness, etc. How does it discover sensornets? Where does it get the meta data?
7 Linking and Tagging Sensor Data Re-publication and tagging as the first-class citizen –Allows users easily share aggregated and/or tagged sensor data –Tremendous efforts already put into by data consumer Ranking and clustering sensors –Republication as vote of confidence –Aggregation as similarity grouping –Accuracy estimation based on past data
8 Unique Properties of Sensor Data? (Mostly) time series of numeric values –Temporal query is often important Aggregation possible –Min, max, average, sum, … Multiple data presentation possibilities –Visualization, multi-resolution, aggregation
9 Research Issues How to express temporal queries? –Popular time-series queries for sensor data? How to support temporal queries efficiently? –Signature computation? Indexing? How to present data? –Visualization? Multi-resolution? Trustworthiness? How does multiple aggregations interact? –Max over Min? Lineage tracing? –How does aggregation affect data quality?