Data aggregator

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

Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks

Abstract: Wireless sensor networks (WSNs) are almost everywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes WSNs one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head to eliminate redundant data sets generated by neighbouring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions, respectively. Based on real sensor data, we have analyed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensor networks.

Concealed Data Aggregation in Heterogeneous Sensor Networks using Privacy Homomorphism

Abstract: Data aggregation is implemented in wireless sensor networks to reduce data redundancy and to summarize relevant and necessary information without requiring all pieces of the data. The benefit of data aggregation can be maximized by implementing it at every data aggregator on the path to the base station. However, data confidentiality requires sensor nodes to encrypt their data prior to transmission. Moreover, once data is encrypted by a sensor node, it should be decrypted at the base station to maintain end-to-end security. This makes the implementation of data aggregation very difficult because data aggregation algorithms require encrypted data to be decrypted. Consequently, data aggregation and secure communication have conflicts in their implementation. To achieve data aggregation and secure communication together, this paper employs privacy homomorphism which offers end-to-end concealment of data and ability to operate on ciphertexts. In the proposed protocol, the computational overhead imposed by the privacy homomorphic encryption functions is tolerated by employing a set of powerful nodes, called AGGNODEs.

On data aggregation quality and energy efficiency of wireless sensor network protocols - extended summary

Abstract: In-network data gathering and data fusion are essential for the efficient operation of wireless sensor networks. While most existing data gathering routing protocols addressed the issue of energy efficiency, few of them, however, have considered the quality of the implied data aggregation process. In this work, an information model for sensed data is first formulated. A new metric for evaluating data aggregation process, data aggregation quality (DAQ), is formally derived. DAQ does not assume any prior knowledge on values or on statistical distributions of sensing data, and may be applied to most data gathering protocols. Next, two new protocols are proposed: the enhanced LEACH and the clustered PEGASIS, enhanced from two major existing protocols: the cluster-based LEACH and the chain-based PEGASIS. By carefully accounting for listening energy, energy efficiency of all four protocols is evaluated. In addition, DAQ is applied to evaluate their data aggregation process. It is found that, while chain-based protocols are more energy efficient than cluster-based protocols, they however suffer from poor data aggregation quality. DAQ may be readily applied to most of continuous data gathering protocols; it is therefore significant to future development of sensor network protocols.

Communication-Efficient Data Aggregation Tree Construction for Complex Queries in IoT Applications

Abstract: Data aggregation is a fundamental operation in Internet of Things (IoT) applications, e.g., distributed Internet-based industrial control and computing systems. As IoT devices are increasingly connected to the system via resource-constrained wireless communication links, it is critical to perform communication-efficient data aggregation to answer complex queries (e.g., skyline queries and equality joins) from IoT applications. In this paper, we investigate the problem of constructing an aggregation tree (AT) for complex queries with the minimum communication cost. As complex queries have a dynamic size of intermediate results, existing Steiner tree-based approaches for traditional query operators, e.g., MIN and top- ${k}$ , cannot be directly applied. We first formalize the aggregation gain by jointly considering the data pruning power (the size of data points that can be pruned during the aggregation for complex queries) and aggregation cost (the size of data points transmitted for the aggregation). By maximizing the aggregation gain, the data set that has a higher pruning power and a smaller size is selected and transferred for data aggregation at succeeding nodes. We then propose to construct the AT by connecting a set of aggregation operations with maximum aggregation gain. Extensive evaluation shows that our proposed framework achieves the promising results.

MDPA: multidimensional privacy-preserving aggregation scheme for wireless sensor networks

Abstract: In this paper, we propose a novel multidimensional privacy-preserving data aggregation scheme for improving security and saving energy consumption in wireless sensor networks (WSNs). The proposed scheme integrates the super-increasing sequence and perturbation techniques into compressed data aggregation, and has the ability to combine more than one aggregated data into one. Compared with the traditional data aggregation schemes, the proposed scheme not only enhances the privacy preservation in data aggregation, but also is more efficient in terms of energy costs due to its unique multidimensional aggregation. Extensive analyses and experiments are given to demonstrate its energy efficiency and practicability. Copyright © 2009 John Wiley & Sons, Ltd. In this paper, we propose a novel multidimensional privacy-preserving data aggregation scheme for improving security and saving energy consumption in wireless sensor networks (WSNs). The proposed scheme integrates the super-increasing sequence and perturbation techniques into compressed data aggregation, and has the ability to combine more than one aggregated data into one. Compared with the traditional data aggregation schemes, the proposed scheme not only enhances the privacy preservation in data aggregation, but also is more efficient in terms of energy costs due to its unique multidimensional aggregation. Extensive analyses and experiments are given to demonstrate its energy efficiency and practicability.