Data aggregator

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

Data aggregation in sensor networks: No more a slave to routing

Abstract: Application-specific data aggregation can play a significant role in energy-efficient operation of wireless sensor networks. Existing aggregation techniques rely heavily on the routing protocol to build shortest paths to route node measurements to the base station and are limited in the types of supported queries. We propose an aggregation scheme that utilizes the inherent information gradients present in the network. The query is directed to the source of information, resulting in better load sharing in the network. We support a variety of queries ranging from simple maximum, minimum or average of the readings of sensor nodes to more complex quantile queries such as k highest values or kth highest value through a generic query algorithm. The query algorithm shifts the computation to the querying agent, thus eliminating any in-network aggregation.

Data aggregation technologies of wireless multimedia sensor networks: A survey

Abstract: The advance in CMOS camera and wireless sensor network (WSN) has promoted the development of wireless multimedia sensor network (WMSN), which can collect more abundant video data and image data. One important enabling technology for WMSN is data aggregation, which is essential for WMSN to be reusable and cost-efficient. The data aggregation technologies can be divided into three parts, namely, data acquisition, data transmission and data processing. This paper presents the state of the art in data aggregation of WMSN according to this category.

OSCAR: a framework to integrate spatial computing ability and data aggregation for emergency management of public health

Abstract: Spatial computing has emerged a critical issue in emergency management of public health. Due to the complexity of spatial data structure and disperse character of spatio-temporal data, when emergency event of public health occurs, it is difficult to get the needed data and analysis it then make quick decision in a short time. In this paper, OSCAR: an Open Spatial Computing and data Resource platform were introduced including its components, framework, elements and two implementations. OSCAR provides a data resource aggregation platform to retrieve data from official statistic agencies through data service and database, scrawl related data from BBS and social media and mirror the environment data from earth observation data sites. All the dataset are arranged in data cubes according to their spatial and temporal dimensions. This mechanism ensures the feasibility and timeliness of time-sequence analysis of specific regions. The algorithms of spatial computing of public health are usually complicated and depend on particular computing environment, which is usually not default configuration of computer of nowadays. OSCAR deploys a series of computation images in a cloud-computing environment. The computation ability can be extended on-demand and thus the time of the computation can be shortened and limited in several minutes when it is needed. The two implementation of human rabies of China and H7N9 in China show the convenience of our platform.

A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks

Abstract: Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions.

The Hierarchial Data Aggregation Method in Backbone Traffic Streaming Analyzing to Ensure Digital Systems Information Security

Abstract: Modern large digital systems security poses the urgent task of detecting network attacks on the backbone highspeed traffic flow. To solve this problem, one need to preprocess, prepare and aggregate data from network packets. The network traffic analysis module aggregates big data from the traffic flow in time series for mathematical analysis. The authors propose a new hierarchical method of data aggregation, which allows to more effectively reducing the data size and speed up the processing of each separate new fragment. The method consists in introducing Parent-Child-Relation links between the analyzed parameters time series and the use of data accumulation and shift based on these relationships. Paper include estimating of proposed method effectiveness.