Data aggregator

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

A Tree Based Data Collection Scheme for Wireless Sensor Network 1

Abstract: Combining the delivery of query messages with the selection of data aggregation points, a novel distributed data collection scheme for wireless sensor network - TBDCS (A Tree Based Data Collection Scheme) is proposed in this paper. Using a flooding avoidance method, it sets up a tree with minimum intermediate nodes, which are also data aggregators when sensor nodes send data back. Moreover, each intermediate node knows all its next-hop children in the tree, so it can choose a proper time locally to aggregate the received data. Theoretical analysis proves that TBDCS changes neither the network connectivity nor the shortest path’s length between the sink and sensor nodes. Simulations show TBDCS significantly reduces the traffic and achieves longer system lifetime.

Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia

Abstract: This paper introduces Dengue Active Surveillance System (DASS) framework for an early warning system of the outbreak. Dengue and dengue hemorrhagic fever are emerging as major public health problems in most Asian countries such as Malaysia. Effective prevention and control programs will depend on improved surveillance. A new approach to active surveillance outlined with emphasis on the inter-epidemic period. The objective is to develop an early warning surveillance system (framework) that can predict epidemic dengue to improve current passive surveillance system available in Malaysia. Basically, the framework introduced data harvesting process from multiple sources as input, data pre-processing using data aggregator and filtering engine, storing large data in repository, analytic engine for analysis and processing the large data, and presentation of the information to the users. The data harvested from two major sources such as weather or flood information, and social media such as build development and dengue symptom using system API, SOAP and others. The data aggregator will aggregate the data from three different types of data such as structured, semi-structured and unstructured data to be stored into the semi-structured database such as MongoDB and NoSQL. The data parse to the filtering engine for filtering and cleaning the data sources using suitable keywords prior to store it in the large data repository. After that, the large data will be processed and analyzed using algorithm or mathematical calculation to determine the expected dengue cases. Then, the processed information will be presented to the users in a form of web or mobile application and other method, for example, short message service (SMS). Finally, the system accuracy will be evaluated based on the comparison study with the traditional passive system.

Double-sided determination of aggregation level for intelligent transportation system data

Abstract: Intelligent transportation system (ITS) data, which are normally collected at a 15- to 30-s interval, are a rich and valuable resource for a variety of applications, including transportation planning. However, raw ITS data exhibit a wide range of fluctuations, which may not be directly useful for planning purposes, for which aggregations taken over longer time intervals are commonly needed. Proper determination of aggregation level of ITS data will ensure the retention of necessary information and the elimination of as much unnecessary information as possible. The traditional approaches to determining aggregation level are intuitive and easy to implement, yet they may be unable to determine whether particular information is kept or lost. The newly developed wavelet-based approach, although good for decomposing original data sets, needs to be further improved. A double-sided technique was developed that could refine the selection of aggregation levels based on two self-defined indices: the dissimilarity index and the information loss index. A computer program coded in MATLAB with the use of the Wavelet Toolbox was developed to implement the proposed technique. A case study illustrating the process was conducted by using real-time ITS data from traffic management centers in the San Antonio TransGuide. It is expected that the proposed technique will maximize the use of real-time ITS data and improve data needs in the urban transportation planning process.

Learning-Based Sparse Data Reconstruction for Compressed Data Aggregation in IoT Networks

Abstract: Due to the booming of various devices in Internet-of-Things (IoT) networks, more data should be transmitted over the networks, which will thereby consume more transmission bandwidth and more transmit power. Compressed data aggregation (CDA) has been proposed as an effective way to reduce the amount of the collected data in IoT networks. Although adopting compressed sensing (CS), CDA can sample the source data efficiently, and sparse data reconstruction is still a big challenge. In this work, inspired by this, we propose a learning-based sparse data reconstruction scheme by jointly utilizing CS and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep CS network is designed by adopting an end-to-end learning method to build a measurement matrix and an efficient and high-accuracy reconstruction network. To show the performance of the proposed scheme, six data sets with different structured sparse models and a real sensor data set are utilized in doing experiments. The performance of the proposed scheme in terms of mean-squared error, peak-signal-noise-ratio, and structural similarity is investigated. The results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio. The results also show that the proposed scheme is suitable for the process of CDA, thus can effectively reduce the amount of data to be transmitted in IoT networks.

Research and Improvement of Wireless Sensor Network Secure Data Aggregation Protocol Based on SMART

Abstract: The privacy-preserving of information is one of the most important problems to be solved in wireless sensor network (WSN). Privacy-preserving data aggregation is an effective way to protect security of data in WSNs. In order to deal with the problem of energy consumption of the SMART algorithm, we present a new dynamic slicing D-SMART algorithm which based on the importance degree of data. The proposed algorithm can decrease the communication overhead and energy consumption effectively while provide good performance in preserving privacy by the reasonable slicing based on the importance degree of the collected raw data. Simulation results show that the proposed D-SMART algorithm improve the aggregation accuracy, enhance the privacy-preserving, reduce the communication overhead to some extent, decrease the energy consumption of sensor node and prolong the network lifetime indirectly.