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Clustering and Compressive Data Gathering in Wireless Sensor Network

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Abstract

In wireless sensor network (WSN) redundant data gathering and transmission occurs due to dense deployment. Recently compressive sensing (CS) has attracted considerable attention for efficient data gathering in WSN. CS can reduce data transmission but the total number of transmissions for data collection is high. To alleviate this, hybrid of CS and raw data collection is proposed and integrated with clustering. Clusters used in this integration reduce the number of CS transmissions, but do not reduce the number of transmissions. In a cluster amount of transmission depends on the number of transmitting nodes and their location in data gathering, hence the way in which nodes are clustered together can significantly effect on the number of transmissions in cluster and overall transmissions in network. When density of sensor nodes in a network is high, we can take advantage of their inherent spatial correlation to reduce the number of transmissions. Motivated by this, we propose a novel base station (BS) assisted cluster, spatially correlated, to reduce the number of transmission in a CS-based clustered WSN. Different from other spatially correlated clusters, in this cluster only CH senses, gathers data in the correlated region, and then transmits compressively sensed measurements to BS without incurring any intra-cluster communication cost. In addition, the clusters so formed, convert a randomly deployed network into a structured one i.e. when several clusters are grouped together they form a hexagonal topology (proved to have a high success rate in cellular network). The proposed system makes WSN transmission efficient by reducing number of transmissions in the network and number of data transmission at the CH using clustering and CS. Also energy consumption is reduced and network lifetime is prolonged.

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Authors and Affiliations

  1. Department of Electronics Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, India

    Utkarsha S. Pacharaney

  2. Department of Electronics and Telecommunication Engineering, Terna Engineering College, Nerul, India

    Rajiv Kumar Gupta

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  1. Utkarsha S. Pacharaney
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  2. Rajiv Kumar Gupta
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Correspondence to Utkarsha S. Pacharaney.

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Appendix

Appendix

Consider triangle ABC with inscribed circle of radius OM = \(r_{s}\) (Fig. 13).

Fig. 13
figure 13

Triangle ABC

Full size image
$$AM = \, 2\left( {OM} \right)$$

In the right angle triangle ANM,

$$Therefore\;\theta = \sin^{ - 1} \frac{1}{2} = 30^\circ$$

Considering, \(\Delta AOB\), BC is tangent to the circle at O. Therefore \(\Delta AOB\) is a right angled triangle at O and \(\angle BAO\) = \(30^\circ\)

Since ∠A + ∠B + ∠O = \(180^\circ\)

Therefore ∠B = \(60^\circ\)

Similarly ∠C = \(60^\circ\) and ∠A = \(60^\circ\)

Since ∠A = ∠B = ∠C = \(60^\circ\)

Thus, the triangle is an equilateral triangle.

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Pacharaney, U.S., Gupta, R.K. Clustering and Compressive Data Gathering in Wireless Sensor Network. Wireless Pers Commun 109, 1311–1331 (2019). https://doi.org/10.1007/s11277-019-06614-5

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