Data aggregator

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

Efficient aggregation technique for data privacy in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), the existing cluster-based private data aggregation techniques are energy-intensive due to high message transmission complexity. Reliable data transmissions are also vital for resource constraint WSNs. To address these issues, the authors propose a reliability enabled private data aggregation technique that has message transmission complexity of O(N). Every node in the cluster cleaves its data into n integrants using simple modular arithmetic with suitable prime moduli and transmits to the cluster heads (CHs) for intermediate aggregation. The CHs, in turn, forward the partial aggregate data to the base station where the final aggregate is recovered using an elegant Chinese remainder theorem. The authors use data privacy, communication overhead, and reliability metrics to gauge the performance of the proposed work. Numerical and simulation results demonstrate that the proposed solution outperforms the existing schemes having O(N 2 ) communication complexity.

Enhancing data delivery with density controlled clustering in wireless sensor networks

Abstract: Wireless sensor networks (WSN) are primarily used for sensing and collecting the information from environment. This information is sent to base station (BS), where, it is processed and analyzed by the underlying application. Functioning of WSN highly depend on the deployment strategy and the coordination among the sensor nodes. Preserving the energy is an important goal that must be considered when developing a routing protocol for WSNs. Radio communication in sensor nodes is highly expensive operation in terms of energy usage. Energy can be conserved in an efficient way with specifically customized routing techniques. Data aggregation methods when applied to sensor nodes, clusters are created where data generated from cluster members is aggregated. This kind of data collection strategy results into energy efficient communication. Integration of data aggregation and clustering approach is enabled through customized hierarchical routing strategies. Clustering technique is key to apply and exploit, the advantages in-network data processing offers. This paper compares various clustering protocols like LEACHC, K-means and its variants. The protocols are compared with respect to network lifetime, data delivery and energy consumption. These protocols only consider intra cluster distance of members while doing clustering, due to this non-uniform clusters are created. These kind of clusters do not deliver data periodically and uniformly from every corner of the field. A density control based approach is proposed which does balancing of cluster members assignment in a loose way. A loose density control (LDC) based approach is integrated with K-means clustering method. LDC based approach exhibits improved average data delivery per node, ensuring regular data availability from every corner of the deployment area as long as the nodes are alive. Not only it improves the amount of data delivered, it also improves the network lifetime considerably. This performance improvement is achieved with almost similar energy expenditure compared to other protocols.

Intelligent data aggregation method and intelligent data aggregation system based on relational online analytical processing

Abstract: The invention discloses an intelligent data aggregation method based on relational online analytical processing, which includes the steps: (1) establishing an aggregation table and establishing the relation between the aggregation table and a dimension table and the relation between the aggregation table and a fact table; (2) analyzing the relation between the aggregation table and the dimension table and the relation between the aggregation table and the fact table to generate a data processing module for extracting, transforming and loading data; (3) starting the data processing module in a database at regular time to add data in the fact table into the aggregation table at regular time; and (4) judging whether the aggregation table or the fact table is queried according to information matching in a matching aggregation knowledge base when data are queried in a system. The method is high in query efficiency, small in occupied space and less in labor cost.

Effect of Source Selection, Deployment Pattern, and Data Forwarding Technique on the Lifetime of Data Aggregating Multi-sink Wireless Sensor Network

Abstract: Data aggregation has been used as a prominent technique for lifetime enhancement of wireless sensor networks (WSN) for quite some time. Data aggregation reduces total number of transmissions in a WSN. Since transmitting energy is the most prominent component of energy consumption in a WSN, data aggregation reduces energy expenditure of the network and thereby enhances network lifetime. The nature of aggregation, however, may vary from one application to another. Along with this, the way source nodes are selected for transmission has an effect on the energy depletion and lifetime of the nodes. In this paper, we have studied the effect of certain non-electrical factors such as source selection, deployment pattern, packet size, and data forwarding technique on the performance of aggregation of a multi-sink WSN with varying degrees of aggregation.

A New Data Aggregation Scheme to Support Energy Efficiency and Privacy Preservation for Wireless Sensor Networks

Abstract: Because a sensor node has limited resources, such as battery capacity, data aggregation techniques have been proposed for wireless sensor networks (WSNs). On the other hand, the provision of efficient data aggregation for preserving data privacy is challenging issue in WSNs. Existing data aggregation methods for preserving data privacy are CPDA, SMART, Twin-Key based method, and GP2S. However, they have a main limitation that communication cost for network construction is considerably high. To resolve the problem, we propose a privacy preserving data aggregation scheme based on Hilbert curve for WSNs. For data aggregation, we utilize a tree-based network structure which minimizes communication among sibling sensor nodes for network construction. Moreover, we adapt a Hilbert curve technique to preserve data privacy. Because the sending data is encrypted by using a unique Hilbert value, it is very difficult to trace a real value even though attackers overhear the sending data. Through our performance analysis, we show that our data aggregation scheme outperforms the existing methods in terms of energy efficiency and privacy preservation