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An Energy-Aware Multilayer Clustering-Based Butterfly Optimization Routing for Underwater Wireless Sensor Networks

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Abstract

Underwater wireless sensor network transmission leads to higher energy consumption due to propagation distance and high energy loss. Moreover, the acoustic medium of acoustic animals and shadow affects the transmission between the source node and destination node. Some demerits of underwater communication are multipath fading, high path loss, frequent packet drops, limited bandwidth, salinity, pressure, temperature, sink hole and curved paths. These factors affect the network lifetime, packet delivery ratio (PDR), link quality and energy. To overcome these problems, Multilayer Clustering-based Butterfly Optimization Routing (MCBOR) algorithm has been introduced to deliver the data packets to the destination without any loss using the sense of butterflies. The proposed work aims to increase PDR and reduce transmission loss. The performance of the proposed MCBOR is evaluated by comparing its performance with state-of-the-art methods. Based on the evaluation report, the proposed MCBOR gives a packet delivery ratio of 0.98%, end to end delay of 6.3 s, and residual energy of 0.47 J. Thus, MCBOR is proved to be more efficient in PDR and reduces transmission loss.

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Abbreviations

\(E_{t}\) :

Residual Energy

\(T_{E}\) :

Threshold Residual Energy

\(D_{\min }\) :

Sink and the closest node distance

x:

Coefficient of distance associated with \(D_{\min }\) and D

t:

Layer index

\({E}_{0}^{2}\) :

Nodes radius

\({E}_{t}\) :

Energy used for sending data

\({E}_{r}\) :

Energy used for receiving the data

\(R({B}_{i})\) :

Node’s radius in the network

\(l_{r}\) :

Node

\(L_{r}\) :

Neighbor nodes number of \(l_{r}\)

\(\overline{{D(l_{r} }} )\) :

Distance sum between \(l_{r}\) and neighbor nodes

\(d(l_{r} ,S)\) :

Distance from \(l_{r}\) to SN

a(f):

Coefficient of absorption

\(g_{CH}\) :

Prearranged CH competition time

\(fi_{(Lq)}\) :

Link quality

\((Pa_{(Rr)} )\) :

Packet reception rate

\((pa_{(s)} )\) :

Total number of sent packets

\((Bi_{(Er)}\) :

Bit error rate

dt:

Node

\(N_{L}\) :

Noise loss

SL:

Source level

\(B_{0}\) :

Normalizing constant

\(D_{sd}\) :

Distance between source and destination

\((Depth_{cur\,nei} )\) :

Depth difference between the current node and neighbor node

f:

Perceived odor of butterflies

A:

Power exponent in need of fragrance

v:

Solution vector \(x_{q}^{v}\)

R:

Random number

\(x_{k}^{v}\) and \(x_{j}^{v}\) :

Kth and jth butterflies from the solution space

\(p_{t}\) :

Width of the t-th layer

\(R_{\max }\) :

Maximum competition radius of the CH

D:

Distance between sink node S and the farthest node from it

k:

Number of layers

\({E}_{avg}^{2}\) :

Average distance between neighbor and node

\({E}_{RE}\) :

Total energy of node

\({E}_{a}\) :

Energy used for aggregating data

\({E}_{c}\) :

Energy used for the collection of data

\(V_{{(l_{r} )}}\) :

Weight value

\(E(l_{r} )\) :

RE of \(l_{r}\)

\(\overline{{E(L_{r} }} )\) :

Average RE of neighbor nodes of \(l_{r}\)

\(R_{\max } (l_{r} )\) :

Maximum radius of \(l_{r}\)

\(TL_{loss} (u)\) :

Transmission loss

\(g(l_{r} )\) :

Hold time of a cluster

\(\lambda\) :

Random number

\((Pa_{(Er)} )\) :

Packet error rate

\((pa_{(r)} )\) :

Received number of packets

\((Si_{(Nr)} )\) :

Signal-to-noise ratio

l:

Packet length

\(P_{t}\) :

Transmitting power

\(T_{L}\) :

Transmission loss

\(S_{F}\) :

Spreading factor

fq:

Frequency

\({(\mathrm{fi}}_{\mathrm{ne CH}})\) :

Nearest to CH

\(Depth_{nei\,CH}\) :

Depth difference between the neighbor node and CH.

c:

Sensory modality

\(x_{q}\) :

Q-th butterfly iteration number

\(g *\) :

Current optimal solution found among all solutions in present iteration

\(f_{q}\) :

Odor of q-th butterfly

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Authors and Affiliations

  1. Research Scholar, Faculty of Electronics Engineering, Sathyabama Institute of Science and Technology, Tamil Nadu, 600119, Chennai, India

    T. R. Chenthil

  2. Professor and Head, Department of Information Technology, Jeppiaar Engineering College, Tamil Nadu, 600119, Chennai, India

    P. Jesu Jayarin

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All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

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Chenthil, T.R., Jesu Jayarin, P. An Energy-Aware Multilayer Clustering-Based Butterfly Optimization Routing for Underwater Wireless Sensor Networks. Wireless Pers Commun 122, 3105–3125 (2022). https://doi.org/10.1007/s11277-021-09042-6

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