SIAM Journal on Computing
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
An adaptive real-time Web search engine
Proceedings of the 2nd international workshop on Web information and data management
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
SenseWeb: An Infrastructure for Shared Sensing
IEEE MultiMedia
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Cellular Census: Explorations in Urban Data Collection
IEEE Pervasive Computing
Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Middleware for smart gateways connecting sensornets to the internet
Proceedings of the 5th International Workshop on Middleware Tools, Services and Run-Time Support for Sensor Networks
Demo abstract: mediascope: selective on-demand media retrieval from mobile devices
Proceedings of the 12th international conference on Information processing in sensor networks
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The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where high-level state information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects). A key service for the WoT is searching for entities which exhibit a certain dynamic state at the time of the query, which is a challenging problem due to the dynamic nature of the sought state information and due to the potentially huge scale of the WoT. In this paper we introduce a primitive called sensor ranking to enable efficient search for sensors that have a certain output state at the time of the query. The key idea is to efficiently compute for each sensor an estimate of the probability that it matches the query and process sensors in the order of decreasing probability, such that effort is first spent on sensors that are very likely to actually match the query. Using real data sets, we show that sensor ranking can significantly improve the efficiency of content-based sensor search.