Data aggregator

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

Cluster-Based Systematic Data Aggregation Model (CSDAM) for Real-Time Data Processing in Large-Scale WSN

Abstract: In present decade, wireless sensor networks is applied in a variety of applications such as health monitoring, agriculture, traffic management, security domains, pollution management, and so on. Owing to the node density, the same data are collected by multiple sensors that introduce redundancy, which should be avoided by means of proper data aggregation methodology. With that note, this paper presents a cluster-based systematic data aggregation model (CSDAM) for real-time data processing. First, the network is formed into a cluster with active and sleep state nodes and cluster-head (CH) is selected based on ranking given to sensors with two criteria: existing energy level (EEL) and geographic-location (GL) to base station (BS), [i.e., Rank(EEL,GL)]. Here, the CH is the aggregator. Second, Aggregation is carried out in 3 levels where the data processing of level 3 has been reduced by aggregating the data at level 1 and level 2. If the energy of aggregator goes below the threshold, we choose another aggregator. Third, Real time application should be given more precedence than other applications, so additionally an application type field is added to each sensor node from which the priority of data processing is given first to real time applications. The simulation results show that CSDAM minimizes the consumption of energy and transmission delay effectively, thereby increasing the network lifespan.

Linked Open Data Aggregation: Conflict Resolution and Aggregate Quality

Abstract: The paradigm of publishing governmental data is shifting from data trapped in relational databases, scanned images, or PDF files to open data, or even linked open data, bringing the information consumers (citizens, companies) unrestricted access to the data and enabling an agile information aggregation, which has up to now not been possible. Such information aggregation comes with inherent problems, such as provision of poor quality, inaccurate, irrelevant or fraudulent information. As part of the OpenData.cz initiative, we are developing projects which will enable creation, maintenance, and usage of the data infrastructure formed by the Czech governmental linked open data. In particular, the project ODCleanStore will enable data consumers seamless automated data aggregation to simplify the manual aggregation process, which would have to be performed otherwise, and will also provide provenance tracking and justifications why the aggregated data should be trusted by the consumer in the given situation. In this paper, we describe two crucial aspects of the data aggregation process in ODCleanStore - resolution of data conflicts and computation of aggregate quality helping consumers to decide whether the aggregated data are worth using. Since the data aggregation algorithm is executed during query time, we show that the proposed algorithm is fast enough to work in real-world settings.

Redundancy Elimination for Data Aggregation in Wireless Sensor Networks

Abstract: Energy consumption is a critical issue affecting the lifetime of wireless sensor networks (WSNs). Data aggregation approach surfaces as an important method enabling to reduce the energy consumption of sensor nodes and improve the bandwidth utilization. This paper proposes a Redundancy Elimination Data Aggregation algorithm, called REDA, based on pattern generation approach. The proposed pattern is specific to the sensed data and it employs differential data collected from sensor nodes in consecutive iterations. Thus, the transmission of redundant data from sensor nodes, within the same cluster, to the relative cluster head (CH) is avoided during all iterations. Performance evaluation proves that energy consumption using REDA algorithm is saved up to 44 % compared with protocol without data aggregation methods. Moreover, compared with existent data aggregation algorithms specifically, ESPDA and SRDA, the simulation results demonstrates that the proposed technique is efficient in terms of bandwidth occupancy.

W-LEACH Based Dynamic Adaptive Data Aggregation Algorithm for Wireless Sensor Networks:

Abstract: We consider the problem of data aggregation for nonuniform and evolving wireless sensor networks. We introduce a new data aggregation algorithm that is able to run on uniform, non-uniform, and evolving networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. Experimental results on real and synthetic data show that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible, maintaining the accuracy of the sensed data, and being able to detect and handle sudden bursts of data.