Multilevel Predictions for the Aggregation of Data in Global Sensor Networks

Abstract

Real-time diagnostic simulations are one challenging application domain that is expected to introduce high requirements to global sensor applications. Besides having hard constraints on latency bounds at which data needs to be processed, such simulation applications will impose high requirements with respect to available bandwidth. Predictors, originally introduced in the domain of wireless sensor networks for energy saving, are one appealing solution to provide real-time estimates and at the same time significantly reduce the data rates. While in the setting of wireless sensor networks many prediction models have been analyzed, their behavior and use is unclear when applied to distributed data streams where aggregation results are typically processed over multilevel hierarchies.In the context of weather simulations, we propose a distributed R-Tree-based aggregation algorithm that allows for efficient reuse of aggregate queries. In the setting of real temperature readings taken from weather stations during one month, we study the trade-off between updates of the prediction model and the precision of the predicted values. Our evaluations indicate that even in situations where complex prediction models are expected to perform best, simple prediction models give higher benefits with respect to saving bandwidth while providing similar data accuracy.

Exact convex formulations of network-oriented optimal operator placement

“…Thus, corresponding to the underlay (3,5) to the underlay graph is highlighted ( ). The cost induced by this edge would be given by…”

“…In fact, the solution of (3) contains explicitly the optimal path vectors in the underlay network, corresponding to the routing path used for the data streaming. This implies that an implementation of the optimal operator placement, according to (3), additionally provides the routing mechanism of the underlying physical network. However, the requirement for explicit knowledge of the underlay network topology might be undesirable for some applications.…”

“…Many data-processing tasks like context reasoning [1], estimation using Kalman filters in wireless sensor networks [2] or data aggregation in global sensor networks [3], require the processing of data received at spatially distributed sources for the use at specific locations. Rather than gathering all data at a central location, it is favourable to process the data already in the underlying communication network.…”

Energy Efficient Query Processing for WSN based on Data Caching and Query Containment

“…The Query model in [5] allows queries to specify geographical area rather than explicitly stating single data sources. Each query region is specified as a bounding rectangle using co-ordinates.…”

“…But by using domain knowledge, BS can answer only certain queries without disseminating the query into the network. The approach used in [5] exploits spatial containment among queries. According to this, queries request data from a particular geographical region specified as bounding rectangle co-ordinates.…”