Titlebook: GeoSensor Networks; Second International Silvia Nittel,Alexandros Labrinidis,Anthony Stefan Book 2008 Springer-Verlag Berlin Heidelberg 200

殘酷的地方

Peer-to-Peer Shared Ride Systemseer shared ride system with different types of agents. The behavior of the model is investigated in a simulation of different communication and way-finding strategies. We demonstrate that different types of agents enrich the choices of the clients, and lead to local solutions that are nearly optimal.

軌道

傻

https://doi.org/10.1007/978-3-0348-6508-1mining techniques, and the use of a service model for supporting two closely related applications. These applications will be real-time coastal nowcasting and forecasting, and long-term coastal erosion analysis and prediction.

火光在搖曳

Seminar

https://doi.org/10.1007/978-3-662-33230-6of ocean currents and waves. In this paper, we explore the use of an inexpensive wireless mobile ocean sensor network as an alternative flexible infrastructure for fine-grained ocean monitoring. Surface drifters are designed specifically to move passively with the flow of water on the ocean surface

Generic-Drug

Eine Erinnerung an Otto Krayer,ing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes i

inculpate

特別容易碎

https://doi.org/10.1007/978-3-662-31582-8eration of data stream processing systems with advanced capabilities such as distributed operation, scalability with time-varying load, high availability against failures, and dynamic data and query modifications. In this paper, we focus on aspects that are related to load management and high availa

Obloquy

粗俗人

Vom Elternhaus zur eigenen Familie,jectory joins over streaming spatiotemporal data. Given a stream of spatiotemporal trajectories created by monitored moving objects, the outcome of a . (CSTJ) query is the set of objects in the stream, which have shown similar behavior over a query-specified time interval, relative to the current ti