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Data aggregator

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


Papers
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Journal ArticleDOI
TL;DR: In this paper , an intermediate device is used to combine all the individual sensor data and deliver it to the sink in a single packet, which helps to extend the life of a node while also reducing network transmission.
Abstract: Because privacy concerns in IoT devices are the most sensitive of all the difficulties, such an extreme growth in IoT usage has an impact on the privacy and life spans of IoT devices, because until now, all devices communicated one to one, resulting in high traffic that may shorten the life of unit nodes. In addition, delivering data repeatedly increases the likelihood of an attacker attacking the system. Such traffic may exacerbate security concerns. The employment of an aggregator in the system as an intermediary between end nodes and the sink may overcome these problems. In any system with numerous sensors or nodes and a common controller or sink, we can use an intermediate device to combine all of the individual sensor data and deliver it to the sink in a single packet. Aggregator is the name given to such a device or component. Data aggregation is carried out to decrease traffic or communication overhead. In general, this strategy helps to extend the life of a node while also reducing network transmission.

18 citations

Journal ArticleDOI
01 Apr 2012
TL;DR: It is believed that the proposed protocol for distributed data aggregation paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.
Abstract: This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.

18 citations

Journal ArticleDOI
TL;DR: The method can greatly decrease the data redundancy of WSN and save large amount of storage resources and network bandwidth consumption, and the scheme has high efficiency and good scalability.
Abstract: Since the measuring accuracy and environment of each sensor are different, there must exist difference in the correctness of measurement If the testing data is not processed and utilized with distinction, it will cause impreciseness to the testing results and lead to errors of the system It is necessary to selectively distinguish the importance among the sensors, contraposing the situation of each sensor in the testing system and the accuracy of tests So the related concepts of data aggregation technology in wireless sensor networks and the aggregation algorithm performance evaluation criteria are introduced The core problem in WSN, aggregation operation for sensing data, is studied deeply The problems in node data group when the distributed clustering technology is implemented to WSN are also analyzed Then a distributed K-mean clustering algorithm based on WSN is proposed On the basis of this improved algorithm, we realize a network data aggregation processing mechanism based on adaptive weighted allocation of WSN DKC algorithm is mainly used to process the testing data of bottom nodes When reducing the data redundancy it can provide more accurate field testing information and system status information It can make rapid packet for the network nodes The packed data will be used to provide correct judgement, according to the size of its corresponding weight, to acquire more reasonable results The experiments have demonstrated that our method can greatly decrease the data redundancy of WSN and save large amount of storage resources The network bandwidth consumption is also reduced So this scheme has high efficiency and good scalability

18 citations

Journal ArticleDOI
TL;DR: A novel algorithmic approach for energy-efficient itinerary planning of MAs engaged in data aggregation tasks that adopts an iterated local search approach in deriving the hop sequence of multiple travelling MAs over the deployed source nodes.
Abstract: Data aggregation is recognized as a key method for reducing the amount of network traffic and the energy consumption on wireless sensor network nodes. Mobile agent (MA) technology represents a distributed computing paradigm which has been proposed as a means for increasing the energy efficiency of data aggregation tasks and addressing the scalability problems of centralized methods. Nevertheless, the itineraries followed by travelling MAs largely determine the overall performance of the data aggregation applications. Along this line, this article introduces a novel algorithmic approach for energy-efficient itinerary planning of MAs engaged in data aggregation tasks. Our algorithm adopts an iterated local search approach in deriving the hop sequence of multiple travelling MAs over the deployed source nodes. Simulation results demonstrate the performance gain of our method against existing multiple MA itinerary planning methods.

18 citations

Dissertation
01 Jan 2015
TL;DR: An intelligent location-aware data aggregation mechanism for real-time observation, estimation, and efficient dissemination of any kind of traffic information in vehicular ad hoc networks (VANETs) is presented in this paper.
Abstract: The main contribution of this thesis is the LA mechanism - an intelligent, locationaware data aggregation mechanism for real-time observation, estimation and efficient dissemination of messages in VANETs. The proposed mechanism is based on a generic modelling approach which makes it applicable to any type of VANET applications. The data aggregation mechanism proposed in this thesis introduces location awareness technique which provides dynamic segmentation of the roads enabling efficient spatiotemporal database indexing. It further provides the location context to the messages without the use of advanced positioning systems like satellite navigation and digital maps. The mechanism ensures that the network load is significantly reduced by using the passive clustering and adaptive broadcasting to minimise the number of exchanged messages. The incoming messages are fused by Kalman filter providing the optimal estimation particularly useful in urban environment where incoming measurements are very frequent and can cause the vehicle to interpret them as noisy measurements. The scheme allows the comparison of aggregates and single observations which enables their merging and better overall accuracy. Old information in aggregates is removed by realtime database refreshing leaving only newer relevant information for a driver to make real-time decisions in traffic. The LA mechanism is evaluated by extensive simulations to show efficiency and accuracy.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023104
2022277
2021189
2020207
2019179
2018188