Data aggregator

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

Energy-Efficient Data Aggregation Hierarchy for Wireless Sensor Networks

Abstract: A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, we first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS). Then, we generalize it to an aggregation hierarchy and extend EPAS to a Hierarchical Energy-Efficient Protocol for Aggregator Selection (hEPAS). We derive the optimal number of aggregators with generalized compression and power-consumption models, and present fully distributed algorithms for aggregator deployment. Simulation results show that our algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols, and are thus applicable to a broad spectrum of application environments.

GP^2S: Generic Privacy-Preservation Solutions for Approximate Aggregation of Sensor Data (concise contribution)

Abstract: Protecting privacy in sensor networks poses new challenges because of the potential incompatibilities between new privacy-preserving mechanisms and mechanisms already implemented in sensor networks (such as in-network data aggregation). To address this problem, we propose in this paper a set of new privacy-preservation data aggregation schemes. Different from past research, our solutions have the following features: supporting data aggregation for a variety of queries; providing privacy protection for both individual data and aggregate data; being resilient to any number of node collusion; being highly efficient.

PDA: Privacy-Preserving Data Aggregation for Information Collection

Abstract: Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme---Cluster-based Private Data Aggregation (CPDA)---leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme---Slice-Mix-AggRegaTe (SMART)---builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

An adaptive modular approach to the mining of sensor network data

Abstract: This paper proposes a two-layer modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task.

A Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Aggregation in Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage rather than injecting snap-shot queries into the network that contain data-reducing operators (e.g. MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this paper we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We present theoretical performance bounds of our algorithm and include simulation results which indicate energy savings of up to 80\% when compared to collecting raw data.