Optimization based Energy Efficient and Deep Learning based Target Tracking in WMSN

DOI

10.2991/978-94-6463-858-5_141How to use a DOI?

Keywords

Chaotic Offspring Distribution; Faster Region based Convolutional Neural Network; Feature Pyramid Network; Target tracking; Tuna Swam Optimization and Wireless Multimedia Sensor Network

Abstract

Wireless Multimedia Sensor Network (WMSN) is one of sensor-based environments that involved group of multimedia sensors for gathering data to deployed areas. To improve energyproficiency in the network clustering method is used in target tracking. The present techniques make use of recovery method to initiate huge number of nodes that results in incre energy consumption. In this research, proposed a Chaotic Offspring Distribution – Tuna Swam Optimization (COD-TSO) - Feature Pyramid Network – Faster Region based Convolutional Neural Network (FPN-Faster RCNN) method energy efficient and tracking algorithm in WMSN. Initially, COD-TSO is proposed to select cluster head in terms of fitness function like distance, energy and node centrality which attains energy efficiency. Next, FPN-Faster RCNN is proposed to track the target which gets executed through each sensor node and CH perform a tracking method to track targets. The proposed algorithm is analyzed with various metrics of tracking accuracy, prediction error, energy consumption, FND, HND and LND. The proposed method has high tracking accuracy of 98%, less prediction error of 0.059% and minimized energy consumption of 49 J which is effective than other existing methods like Trustworthy Target Tracking Scheme (3TS) and Cluster based Trustable Target Detection and Tracking Scheme (CTTDTS).

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Mamatha
AU  - R. Prabha
PY  - 2025
DA  - 2025/11/04
TI  - Optimization based Energy Efficient and Deep Learning based Target Tracking in WMSN
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1725
EP  - 1744
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_141
DO  - 10.2991/978-94-6463-858-5_141
ID  - Mamatha2025
ER  -