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Data aggregator
About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.
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10 citations
TL;DR: The Adaptive Aggregation Tree (AAT) is proposed to dynamically transform the structure of the routing tree to improve the efficiency of data aggregation and approaches the cost savings of MST without explicit maintenance of an infrastructure.
Abstract: Data aggregation reduces energy consumption by reducing the number of message transmissions in sensor networks. Effective aggregation requires that event messages be routed along common paths. While existing routing protocols provide many ways to construct the aggregation tree, this opportunistic style of aggregation is usually not optimal. The Minimal Steiner Tree (MST) maximises the possible degree of aggregation, but finding such a tree requires global knowledge of the network, which is not practical in sensor networks. In this paper, we propose the Adaptive Aggregation Tree (AAT) to dynamically transform the structure of the routing tree to improve the efficiency of data aggregation. It adapts to changes in the set of source nodes automatically, and approaches the cost savings of MST without explicit maintenance of an infrastructure. The evaluation results show that AAT reduces the communication energy consumption by 23%, compared to shortest-path tree, and by 31%, compared to GPSR.
10 citations
26 May 2015
TL;DR: This paper proposes a novel data aggregation mechanism for WSNs, called Adaptive-PCA, which consists of two schemes which are 1) event checker and 2) PCA data accuracy checker, and shows that the total battery energy consumption is less than that of a traditional WSN.
Abstract: Dimensionality reduction techniques are convenient for data aggregation to reduce battery energy consumption in sensor nodes. Normally, principal component analysis (PCA), a dimensionality reduction technique, has been used for data aggregation in WSNs. However, PCA yields to data errors when the sensing data are not related. The PCA processing time is also an issue in an urgent situation that the sensing data are required to be transmitted to the base station instantly. This paper proposes a novel data aggregation mechanism for WSNs, called Adaptive-PCA. In Adaptive-PCA, PCA is performed dynamically based on the sensing data. In a normal situation, PCA is performed for data aggregation to reduce the number of transmitted packets. On the other hand, in an urgent situation, sensing data change dramatically, PCA is not performed; the sensing data are transmitted to the base station instantly. Adaptive-PCA consists of two schemes which are 1) event checker and 2) PCA data accuracy checker. These two schemes drive each sensor node whether perform PCA or instantly transmit the sensing data. The simulation results show that Adaptive-PCA adjusts the number of transmitted packets to the environmental changes. Using Adaptive-PCA, the total battery energy consumption is less than that of a traditional WSN. Also, the data accuracy of Adaptive-PCA is higher than that of Non-adaptive-PCA.
10 citations
TL;DR: From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account, and is based on Lagrangean relaxation in conjunction with a number of optimization-basedHeuristics.
Abstract: By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account.
10 citations
TL;DR: Performance analysis and simulation results prove that the proposed improved algorithm for WSNs based on data aggregation tree can reduce the energy cost of nodes and shorten the time of synchronization effectively.
10 citations