24,834 research outputs found
Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
In this paper, we study the problem of processing continuous range queries in
a hierarchical wireless sensor network. Contrasted with the traditional
approach of building networks in a "flat" structure using sensor devices of the
same capability, the hierarchical approach deploys devices of higher capability
in a higher tier, i.e., a tier closer to the server. While query processing in
flat sensor networks has been widely studied, the study on query processing in
hierarchical sensor networks has been inadequate. In wireless sensor networks,
the main costs that should be considered are the energy for sending data and
the storage for storing queries. There is a trade-off between these two costs.
Based on this, we first propose a progressive processing method that
effectively processes a large number of continuous range queries in
hierarchical sensor networks. The proposed method uses the query merging
technique proposed by Xiang et al. as the basis and additionally considers the
trade-off between the two costs. More specifically, it works toward reducing
the storage cost at lower-tier nodes by merging more queries, and toward
reducing the energy cost at higher-tier nodes by merging fewer queries (thereby
reducing "false alarms"). We then present how to build a hierarchical sensor
network that is optimal with respect to the weighted sum of the two costs. It
allows for a cost-based systematic control of the trade-off based on the
relative importance between the storage and energy in a given network
environment and application. Experimental results show that the proposed method
achieves a near-optimal control between the storage and energy and reduces the
cost by 0.989~84.995 times compared with the cost achieved using the flat
(i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure
CERN Storage Systems for Large-Scale Wireless
The project aims at evaluating the use of CERN computing infrastructure for next generation sensor networks data analysis. The proposed system allows the simulation of a large-scale sensor array for traffic analysis, streaming data to CERN storage systems in an efficient way. The data are made available for offline and quasi-online analysis, enabling both long term planning and fast reaction on the environment
A Review of the Enviro-Net Project
Ecosystems monitoring is essential to properly understand their development
and the effects of events, both climatological and anthropological in nature.
The amount of data used in these assessments is increasing at very high rates.
This is due to increasing availability of sensing systems and the development
of new techniques to analyze sensor data. The Enviro-Net Project encompasses
several of such sensor system deployments across five countries in the
Americas. These deployments use a few different ground-based sensor systems,
installed at different heights monitoring the conditions in tropical dry
forests over long periods of time. This paper presents our experience in
deploying and maintaining these systems, retrieving and pre-processing the
data, and describes the Web portal developed to help with data management,
visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers'
comments v1: 38 pages, 8 figure
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