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Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks

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Abstract

Wireless sensor networks produce immense sensor readings within a report interval to the sink. So transfer of information in a resource constrained wireless environment is difficult. Compressive sensing overcomes the resource constrains in wireless environment by exploiting sparsity in transfer with fewer measurement and recovery of original signal. In this research Intelligent Neighbor Aided Compressive Sensing (INACS) scheme is proposed for efficient data assembly in spatial and temporal correlated WSNs. Sparse Matrix has been formed with spatial and temporal coordinates for data transfer. In every sensing period, the sensor node just sends the readings within the sensing period to uniquely selected neighbour based on a correlation. The transmission period provides significant improvement with compressed data using INACS with the measurement matrix. Thus INACS provides reduction in number of transmission and higher reconstruction accuracy. INACS has been compared with Compressive wireless sensing for reduction in number of transmissions achieved. The time series analysis with INACS has been done to validate the simultaneous association between number of transmissions and time period.

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Acknowledgement

This work was supported by the Faculty Research Grant, from University of Malaya [GPF007A-2018].

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mukil Alagirisamy, Chee-Onn Chow & Kamarul Ariffin Bin Noordin

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  1. Mukil Alagirisamy
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  2. Chee-Onn Chow
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  3. Kamarul Ariffin Bin Noordin
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Correspondence to Mukil Alagirisamy.

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Alagirisamy, M., Chow, CO. & Noordin, K.A.B. Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks. Wireless Pers Commun 112, 91–103 (2020). https://doi.org/10.1007/s11277-019-07017-2

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