Data aggregator

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

Designing an efficient PCA based data model for wireless sensor networks

Abstract: Recent technology in wireless communication has enabled the development of low-cost sensor networks. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. Wireless sensor networks (WSNs) usually have limited energy and transmission capacity, which cannot match the transmission of a large number of data collected by sensor nodes. So, it is necessary to perform in-network data aggregation in the WSN which is performed by aggregator node. Since, the nodes in WSN are vulnerable to malicious attackers and physical impairment; the data collected in WSNs may be unreliable. So, in this paper, we propose an efficient model based technique to detect the unreliable data. Data model is designed using the sound statistical multivariate technique called Principal Component Analysis (PCA). But as a drawback, it is not robust to outliers. Hence, if the input data is corrupted, an arbitrarily wrong representation is obtained. To overcome this problem, we propose two approaches namely Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) to design robust PCA which aids in design of a noise-free data model. The performance of proposed approach is evaluated and compared with previous approaches and found that our approach is effective and efficient.

On Real-Time Distributed Geographical Database Systems

Abstract: Advanced Traveler Information Systems (ATIS) under the intelligent Vehicle Highway Systems (IVHS) context require efficient information retrieval and updating in a dynamic environment and at different geographical scales. Some problems in ATIS can be solved based on the functionalities provided by GIS systems. However, extra requirements such as real-time response are not readily met in existing GIS systems. We investigate the use of GIS-based systems for applications in ATIS and we propose a new system architecture based on existing GIS technology and distributed computing technology. Issues on data modeling, data representation, storage and retrieval, data aggregation, and parallel processing of on-line queries in the proposed GIS-based systems are discussed.

Data aggregation processes: a survey, a taxonomy, and design guidelines

Abstract: Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things systems, many with timing constraints. Understanding the common and variable features of data aggregation processes, especially their implications to the time-related properties, is key to improving the quality of the designed system and reduce design effort. In this paper, we present a survey of data aggregation processes in a variety of application domains from literature. We investigate their common and variable features, which serves as the basis of our previously proposed taxonomy called DAGGTAX. By studying the implications of the DAGGTAX features, we formulate a set of constraints to be satisfied during design, which helps to check the correctness of the specifications and reduce the design space. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also serves as the foundation of a design tool which facilitates the model-driven design of data aggregation processes.

Modeling and designing efficient data aggregation in wireless sensor networks under entropy and energy bounds

Abstract: Sensor networks are characterized by limited energy, processing power, and bandwidth capabilities. These limitations become particularly critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is considered to be an effective technique to reduce energy consumption and prevent congestion in wireless sensor networks. In this paper, we derive a number of important insights concerning the data aggregation process, which have not been discussed in the literature so far. We first estimate the conditions under which aggregation is a costly process in comparison to a non aggregation approach, by considering a realistic scenario where the processing costs related to aggregation of data are not neglected. We also consider that aggregation should preserve the integrity of data, and therefore, the entropy of the correlated data sent by sources can be considered in order to both decrease the amount of redundant data forwarded to the sink and perform an overall lossless process. We also derive the cumulative and the probability distribution functions of the delay in an aggregator node queue, which can be used to relate the delay to the amount of aggregation being considered. The framework we present in this paper serves to investigate the tradeoff between the increase in data aggregation required to reduce energy consumption, and the need to maximize information integrity, while also understanding how aggregation impacts the network propagation delay of a data packet.

Symmetric-Key Based Homomorphic Primitives for End-to-End Secure Data Aggregation in Wireless Sensor Networks

Abstract: In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.