Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

The CMS data aggregation system

Abstract: Meta-data plays a significant role in large modern enterprises, research experiments and digital libraries where it comes from many different sources and is distributed in a variety of digital formats. It is organized and managed by constantly evolving software using both relational and non-relational data sources. Even though we can apply an information retrieval approach to non-relational data sources, we can’t do so for relational ones, where information is accessed via a pre-established set of data-services. Here we discuss a new data aggregation system which consumes, indexes and delivers information from different relational and non-relational data sources to answer cross data-service queries and explore meta-data associated with petabytes of experimental data. We combine the simplicity of keyword-based search with the precision of RDMS under the new system. The aggregated information is collected from various sources, allowing end-users to place dynamic queries, get precise answers and trigger information retrieval on demand. Based on the use cases of the CMS experiment, we have performed a set of detailed, large scale tests the results of which we present in this paper.

Data aggregation method in wireless sensor network

Abstract: The invention relates to a data aggregation method in a wireless sensor network, in particular to a tree-based and time-controlled data aggregation (TBTCDA) method in the wireless sensor network. Under the situation that information on a node position is undetermined, the data aggregation is performed to fulfill the aim of reducing network traffic and node energy consumption and balancing a contradiction between data aggregation accuracy and communication time delay. The method specifically comprises the following steps of: firstly, initializing a network; secondly, initiating a notice of establishing an aggregation tree by Sink; and finally, controlling aggregation time, data acquisition and aggregation, wherein the third step comprises the following steps of: 31) evaluating unit time delay Ta when all nodes participate in aggregation; and 32), setting Ta and the aggregation accuracy P according to an application demand, or receiving data acquired by Ci nodes by a cluster and transmitting aggregated data to a father cluster by a cluster head.

Routing-Aware and Malicious Node Detection in a Concealed Data Aggregation for WSNs

Abstract: Data aggregation in Wireless Sensor Networks (WSNs) can effectively reduce communication overheads and reduce the energy consumption of sensor nodes. A WSN needs to be not only energy efficient but also secure. Various attacks may make data aggregation unsecure. We investigate the reliable and secure end-to-end data aggregation problem considering selective forwarding attacks and modification attacks in homogeneous WSNs, and propose two data aggregation approaches. Our approaches, namely Sign-Share and Sham-Share, use secret sharing and signatures to allow aggregators to aggregate the data without understanding the contents of messages and the base station to verify the aggregated data and retrieve the raw data from the aggregated data. To the best of our knowledge, this is the first lightweight en-routing malicious node detection in concealed data aggregation. We have performed an extensive simulation to compare our approaches and the two state-of-the-art approaches PIP and RCDA-HOMO. The simulation results show that both Sign-Share and Sham-Share consume a reasonable amount of time in processing and aggregating the data. The simulation results show that our first approach achieved an average network lifetime of 102.33% over PIP and average aggregation energy consumption of 74.93%. In addition, it achieved an average aggregation processing time and sensor data processing time of 95.4% and 90.34% over PIP and 98.7% and 92.07% over RCDA-HOMO, respectively, and it achieved an average network delay of 71.95% over PIP. Although RCDA-HOMO is completely a different technique, a comparison was performed to measure the computational overhead.