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Data aggregation in wireless sensor networks: Compressing or Forecasting?

Jin Cui 1, 2 Fabrice Valois 1, 2 
2 URBANET - Réseaux capillaires urbains
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Wireless sensor networks suffer from constrains in terms of energy, memory and computing capability. In recent years, the main challenge was to develop energy efficient solutions mainly at the MAC and network layers to increase the lifetime of the network, which spawned the development of the data aggregation. Data aggregation is the procedure of intelligently gathering information which reduce the amount of data send to the sink, this improve the network capacity. In this report, we show that data aggregation can effectively reduce the energy consuming and improve the network capacity. More, we present the state-of-the-art aggregation functions, including compressing-based and forecasting-based method; compressing aggregation focus on compress the data packets accompanied with transmitting based on spatial correlation; while forecasting aggregation tends to use mathematical model to fit the time series and predict the new value due to highly temporal correlation. We detail these two methods and characterize them respectively. We propose comparison between A-ARMA and Compressing Sensing, which are on behalf of forecasting aggregation and compressing aggregation respectively.
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https://hal.inria.fr/hal-00861598
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Submitted on : Thursday, October 31, 2013 - 5:41:15 PM
Last modification on : Friday, May 13, 2022 - 3:16:02 PM
Long-term archiving on: : Saturday, February 1, 2014 - 2:45:28 AM

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  • HAL Id : hal-00861598, version 1

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Jin Cui, Fabrice Valois. Data aggregation in wireless sensor networks: Compressing or Forecasting?. [Research Report] RR-8362, INRIA. 2013. ⟨hal-00861598⟩

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