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    An Analysis of Loss-Free Data Aggregation for High Data Reliability in Wireless Sensor Networks


    Brown, Stephen (2017) An Analysis of Loss-Free Data Aggregation for High Data Reliability in Wireless Sensor Networks. In: 2017 28th Irish Signals and Systems Conference (ISSC). Institute of Electrical and Electronic Engineers, Red Hook, NY, USA. ISBN 978-1-5386-1046-6

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    Abstract

    Data aggregation is am important feature in Wire-less Sensor Networks, used primarily to reduce energy use.This paper extends our previous results, which showed that data aggregation can improve the reliability of data delivery rather than degrading it as previously assumed. These previous results were based on the use of scaleable aggregation functions,such as SUM, COUNT, MIN, MAX which work independently of the network size. In this paper we extend these results to consider the reliability and energy efficiency of lossless data delivery with the semi-scaleable aggregation function APPEND,and determine the boundary conditions under which the data reliability can be maintained without an increase in the energy cost. These new results show that lossless aggregation using the APPEND aggregation function can provide improved reliability with reduced energy usage in certain conditions.

    Item Type: Book Section
    Additional Information: This paper was presented at 2017 28th Irish Signals and Systems Conference (ISSC), 20-21 June 2017, Killarney, Ireland.
    Keywords: Wireless Sensor Networks; Data Aggregation; Aggregation Functions; Reliability;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 11803
    Identification Number: https://doi.org/10.1109/ISSC.2017.7983622
    Depositing User: Stephen Brown
    Date Deposited: 21 Nov 2019 16:58
    Publisher: Institute of Electrical and Electronic Engineers
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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