Data aggregator

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

On effective data aggregation techniques in Host-based Intrusion Detection in MANET

Abstract: Mobile Ad Hoc Networks (MANETs) have been widely used in commercial and tactical domains. MANETs commonly demand a robust, diverse, energy-efficient, and resilient communication and computing infrastructure, enabling network-centric operation with minimal downtime. MANETs face security risks and energy consumption. However, conducting cyber attack monitoring and detection in a MANET becomes a challenging issue because of limited resources and its infrastructureless network environment. To address this issue, we develop both lossless and lossy aggregation techniques to reduce the energy cost in information transition and bandwidth consumption while preserving the desired detection accuracy. In particular, we develop two lossless aggregation techniques: compression-based and event-based aggregation and develop a lossy aggregation technique: feature-based aggregation. We conduct real-world experiments and simulation study to evaluate the effectiveness of our proposed data aggregation techniques in terms of the energy consumption and detection accuracy.

A spatial correlation aware scheme for efficient data aggregation in Wireless Sensor Networks

Abstract: One of the most popular and efficient methods for conserving energy in Wireless Sensor Networks (WSNs) is data aggregation. This technique usually introduces an additional delay in the transmission of data packets. The inherent trade-off between energy consumption and end-to-end delay imposes an important decision to be made by the nodes, mainly to determine the most appropriate time for aggregating local/transiting packets and forwarding the resulting packet(s) to the next hop towards the sink. Most of the solutions proposed so far are either unable to significantly reduce the overhead caused by the redundant transmissions, or require a long waiting time before aggregating the received packets. To overcome the above limitations, we propose a novel scheme that ensures efficient and fast packets aggregation in WSNs. This scheme defines optimal decision making policies at the cluster head level (i.e. in a cluster based topology) to determine the appropriate waiting time before aggregating the local data sampling, as well as data sampling received from other neighbor cluster heads. The obtained evaluation results confirm the efficiency of our scheme in terms of the achieved end-to-end transmission delay for periodic packets and the reduced overhead. These results reveal also that our scheme outperforms other three literature schemes (no aggregation, randomized waiting and full aggregation) as it ensures the best compromise for aggregation saving and delay reduction.

Towards Energy-Efficient Algorithm-Based Estimation in Wireless Sensor Networks

Abstract: A primary purpose of sensing in a sensor network is to collect and aggregate information about a phenomenon of interest. The batteries on today’s wireless sensor barely last a few days, and nodes typically expend a lot of energy in computation and wireless communication. Hence, the energy efficiency of the system is a major issue. Different representative mechanisms has been proposed to achieve a long lived sensors such as “clustering mechanisms” as well as Aggregation techniques to reduce the amount of data communication generated by sensors. Depending on the data type, ARMA series and forecasting are possible ways to reduce data transmission. In this work, we adopt single-hop clustering mechanism where all sensor nodes in a cluster communicate with their Cluster-Head (or sink) via single hop (such as In/On body sensors for personal health monitoring,..). We propose different data aggregation algorithms based on the AutoRegressive model, to predict local readings and reduce the communication traffic. We evaluate the performance of our work in terms of communication cost and energy consumption. We also extend our work to enhance the prediction accuracy by estimating dynamic prediction threshold. Our simulation shows that depending on data type, communication overhead and rate can be reduced and a considerable accuracy prediction can be obtained.

Data aggregation and dissemination in vehicular ad-hoc networks

Abstract: Vehicular Ad-Hoc Networks (VANETs) are a fast growing technology that many governments and automobile manufacturers are investing in to provide not only safer and more secure roads, but also informational and entertainment-based applications for drivers. The applications developed for VANETs can be classified into multiple categories (safety, informational, entertainment). Most VANET applications, regardless of their category, depend on having certain vehicular data (vehicular speed, X position and Y position) available. Although these applications appear to use the same vehicular data, the characteristics of this data (i.e., amount, accuracy, and update rate) will vary based on the application category. For example, safety applications need an accurate version of the vehicular data with high frequency, but over short distances. Informational applications relax the data frequency constraint as they need the vehicular data to be reasonably accurate with less frequency, but over longer distances. If each of these applications shares the vehicular data with only its peers using its own mechanism, this behavior will not only introduce redundant functionalities (sending, receiving, processing, etc.) for handling the same data, but also wastefully consume the bandwidth by broadcasting the same data multiple times. Despite the differences in the data characteristics needed by each application, this data can be still shared. Vehicular networks introduce the potential for many co-existing applications. If we do not address the problem of data redundancy early, it may hinder the deployment and usefulness of many of these applications. Therefore, we developed a framework, cluster-based accurate syntactic compression of aggregated data in VANETs (CASCADE), for efficiently aggregating and disseminating commonly-used vehicular data. CASCADE is architccted as a layer that provides applications with a customized version of the vehicular data, based on parameters that each application registers with CASCADE. Additionally, the framework performs the common data handling functionalities (sending, receiving, aggregating, etc.) needed by the applications. This dissertation makes the following contributions: (1) a lossless data compression technique based on differential coding that is tailored for the characteristics of vehicular data; (2) a syntactic data aggregation mechanism that can represent the vehicular data in a 1.5 km area in one IEEE 802.11 frame; (3) a light-weight position verification technique that quickly detects false data with very low false positives; (4) a probabilistic data dissemination technique that alleviates the spatial broadcast storm problem and effectively uses the bandwidth to disseminate data to distant areas in a short amount of time in addition to having less redundancy and more coverage than other techniques. (5) a mechanism for recovering from the communication discontinuity problem in short time based on the traffic density in the opposite direction; (6) an investigation of the possible data structures for representing the vehicular data in a searchable format; (7) a parametric mechanism for matching the vehicular data and providing a customized version of the data that satisfies certain characteristics based on the parameter value. CASCADE through its four major components, local view, extended view, data security and data dissemination, provides an efficient solution for the problem of scalability for VANET applications.

Attribute Based Spanning Tree Construction for Data Aggregation in Heterogeneous Wireless Sensor Networks

Abstract: Wireless sensor network consists of densely deployed sensor nodes which have limited resources like energy, node lifetime. Data aggregation is an effective scheme to reduce redundancy of sampled data generated by sensor nodes. Homogeneous sensor networks easily adapt the data aggregation scheme because of easy synchronization of data samples; but heterogeneous sensor network have difficulty to handle data aggregation due to synchronization of different data packets produced by different sensor nodes. In order to perform efficient data aggregation in heterogeneous sensor networks our proposed method Attribute based Spanning Tree (AST) introduced the method of attribute based spanning tree construction over heterogeneous networks. Based on the characteristics of sensor nodes, logical separation of nodes formed then each group constructs Minimum Spanning Tree (MST), aggregation follows this MST to reach sink node. By adapting Kruskal's algorithm into our proposed method MST is constructed in sensor nodes. Our simulation results shows that AST is more spatially convergent and it uses shortest path cost for aggregation leads to increase node lifetime and saves energy.