Data aggregator

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

Data Gathering in Wireless Sensor Networks

Abstract: Data gathering is one of the primary operations carried out in Wireless Sensor Networks (WSNs). It involves data collection with aggregation and data collection without aggregation, referred to as data aggregation and data collection respectively. In the last decade, many techniques for these two applications are proposed, with different focuses, such as accuracy, reliability, time complexity, and so on. This chapter reviews the state of the art of data aggregation and data collection techniques in order to present a comprehensive guidance on how to choose a more appropriate approach for different applications. The definitions of data aggregation and data collection are firstly introduced. Subsequently, the challenges of designing effective data aggregation and data collection methods are discussed. Then some typical data aggregation techniques and their classifications are presented. Particularly, a latest distributed data aggregation algorithm (DAS) is illustrated in details. For data collection, we begin with some new advances and then introduce several new tree-based and cell-based data collection algorithms. Finally, this chapter is ended by pointing out some possible future research directions.

Clustering algorithms for wireless sensor networks using spatial data correlation

Abstract: In densely deployed wireless sensor networks, spatial data correlations are introduced by the observations of multiple spatially proximal sensor nodes on a same phenomenon or event. These correlations bring significant potential advantages for the development of efficient strategies for reducing energy consumption. In this paper, spatial data correlations are exploited to group sensor nodes into clusters of high data aggregation efficiency. We define the problem of selecting the set of cluster heads as the weighted connected dominating set problem. Then we develop a set of centralized and distributed algorithms to select the cluster heads. Simulation results demonstrate the effectiveness and efficiency of the designed algorithms.

Energy efficient differential data aggregation in a dynamic cluster based WSN

Abstract: A key aspect of collaboration systems, and therefore collaboration technologies is the ability to access concerned information in timely manner. The Internet of things (IoT) is being envisioned as the architecture of the future to assist in this regard. It will be created by combining sensing and communicating devices, which will provide data that can be analyzed and used to initiate automated actions. This structure of IoT has now given a new recognition to Wireless Sensor etworks (WS ) such that WS can be identified as the datacollecting component of the IoT. Efficient data collection from a large scale WS , with limited power supply, bandwidth and packet sizes, is a critical issue. One of the methods of data collection in a WS is through forming multiple clusters of the sensor nodes with one cluster head (CH) in each cluster. The objective of this work is to develop an energy efficient data collection environment for a large scale, randomly deployed cluster based wireless sensor networks by using a virtual grid based mechanism to localize the clusters and stabilize the cluster sizes in the network. This has been done as a precondition to implement the proposed differential data aggregation scheme for the spatially correlated data in a cluster.

Sensor data aggregation in a multi-layer Big Data framework

Abstract: Sensors play a vital role in the growth of big data as they are being used in numerous data-intensive applications. This paper introduces a multilayer big data aggregation framework and a priority-based, dynamic data aggregation (PDDA) scheme. The proposed PDDA approach works at the bottom layer at sensors (i.e., data collecting node) as opposed to most existing approaches which only consider aggregating data at the upper layer at the central server side. We evaluate the performance of the proposed PDDA approach and compare it against existing traditional tree and cluster-based schemes in terms of network lifetime and data latency. Simulation results demonstrate that the proposed PDDA approach outperforms the existing approaches.

Identifying individual vulnerability based on public data

Abstract: Companies and government agencies frequently own data sets containing personal information about clients, survey responders, or users of a product. Sometimes these organizations are required or wish to release anonymized versions of this information to the public. Prior to releasing these data, they use established privacy preservation methods such as binning, data perturbation, and data suppression to maintain the anonymity of clients, customers, or survey participants. However, existing work has shown that common privacy preserving measures fail when anonymized data are combined with data from online social networks, social media sites, and data aggregation sites. This paper introduces a methodology for determining the vulnerability of individuals in a pre-released data set to reidentification using public data. As part of this methodology, we propose novel metrics to quantify the amount of information that can be gained from combining pre-released data with publicly available online data. We then investigate how to utilize our metrics to identify individuals in the data set who may be particularly vulnerable to this form of data combination. We demonstrate the effectiveness of our methodology on a real world data set using public data from both social networking and data aggregation sites.