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 and transfer in periodic sensor networks

Abstract: Limited battery power and high transmission energy consumption in wireless sensor networks make in-network aggregation and prediction a challenging area for researchers. The most energy consumable operation is transmitting data by a sensor node, comparing it with the energy consumption of in-network computation which is negligible. The energy trade-off between communication and computation provides applications benefit when processing the data at the network side rather than simply transmitting sensor data. In this study, the authors consider a cluster-based technique with which data is sent periodically from sensor nodes to their appropriate cluster-heads (CH). The proposed technique manages energy efficiency in periodic sensor network and it consists of two phases: ‘aggregation phase and adaptation phase’. The aggregation phase is used to find similarities between data (measurements captured during a period p) in order to eliminate redundancy from raw data, thus reducing the amount of data-sets sent to the CH. The adaptation phase provides sensors the ability to identify duplicate data-sets captured among successive periods, using the sets-similarity joins functions. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. Results show that the proposed technique is effective in term of energy consumption and quality of data.

A novel data aggregation scheme based on self-organized map for WSN

Abstract: Wireless sensor network allows efficient data collection and transmission in IoT environment. Since it usually consists of a large number of sensor nodes, a significant amount of redundant data and outliers are generated which deteriorate the network performance. In this paper, a novel data aggregation scheme is proposed which is based on self-organized map neural network to reduce redundant data and eliminate outliers. In addition, cosine similarity is used to improve the clustering process of sensor nodes based on the density and similarity of the data, and interquartile analysis is adopted to remove outliers. It allows to significantly reduce the energy consumption and enhance the network performance. Extensive simulation with real dataset shows that the proposed scheme consistently outperforms the existing representative data aggregation schemes in term of data reduction rate, network lifetime, and energy efficiency.

Privacy-Preserving Multidimensional Data Aggregation Scheme Without Trusted Authority in Smart Grid

Abstract: Privacy-preserving multidimensional data aggregation is a significant basic building block for protecting the users’ privacy in smart grid, and it can not only expand the applications of data aggregation but also fulfill the demands of the fine-grained analysis of multidimensional data. However, traditional multidimensional data aggregation schemes depend on the trusted authority and cannot resist the coalition attack from the gateway (GW) and the control center (CC), which may cause the users’ fears about privacy violations. Therefore, this article proposes a privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid based on the ElGamal homomorphic cryptosystem with distributed decryption, which can resist the coalition attack from the GW and the CC. What is more, the proposed scheme does not depend on the trusted authority which is not fully trusted in the real world. The detailed security analysis indicates that our scheme can satisfy the security requirement of smart grid. The performance analysis shows that the proposed scheme achieves the lowest computation and communication costs in data encryption phase and data aggregation phase, thus it is appropriate for many practical applications.

WDARS: A Weighted Data Aggregation Routing Strategy with Minimum Link Cost in Event-Driven WSNs

Abstract: Realizing the full potential of wireless sensor networks (WSNs) highlights many design issues, particularly the trade-offs concerning multiple conflicting improvements such as maximizing the route overlapping for efficient data aggregation and minimizing the total link cost. While the issues of data aggregation routing protocols and link cost function in a WSNs have been comprehensively considered in the literature, a trade-off improvement between these two has not yet been addressed. In this paper, a comprehensive weight for trade-off between different objectives has been employed, the so-called weighted data aggregation routing strategy (WDARS) which aims to maximize the overlap routes for efficient data aggregation and link cost issues in cluster-based WSNs simultaneously. The proposed methodology is evaluated for energy consumption, network lifetime, throughput, and packet delivery ratio and compared with the InFRA and DRINA. These protocols are cluster-based routing protocols which only aim to maximize the overlap routes for efficient data aggregation. Analysis and simulation results revealed that the WDARS delivered a longer network lifetime with more proficient and reliable performance over other methods.

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

Abstract: In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.