Chronological Archery Algorithm for Image Compression Using Compressive Sensing in IoT-LTE System

Recent advancements in information technologies use the Internet of Things (IoT) for various communication tasks. However, many existing works neglect image data compression, especially in smooth regions, leading to higher costs and energy consumption. Additionally, the balance between compressed im...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics Vol. 71; no. 2; pp. 4391 - 4405
Main Authors: Sai Venkateshwar Rao, A., Amgoth, Tarachand, Bhattacharya, Ansuman
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Archery
Authentication
chronological archery algorithm
Clustering
Collaboration
Communication
Compressed sensing
Data communication
Data compression
Data models
Data recovery
Energy consumption
Energy costs
feature similarity indexing method
Image coding
Image compression
Image quality
Internet of Things
IoT
Long Term Evolution
Low energy adaptive clustering hierarchy
LTE
Noise measurement
Performance evaluation
Sensors
Signal to noise ratio
Similarity
ISSN:0098-3063, 1558-4127
Online Access:Get full text
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Summary:Recent advancements in information technologies use the Internet of Things (IoT) for various communication tasks. However, many existing works neglect image data compression, especially in smooth regions, leading to higher costs and energy consumption. Additionally, the balance between compressed image and quality was often not achieved. This research proposes an effective data compression and recovery method using the compressive sensing-based image compression FCSP-based Chronological Archery Algorithm (FCSP-based CAA) model for IoT-LTE systems. The process involves three phases: authentication, communication, and data compression. Data security is ensured through authentication, followed by communication using the Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol. The compressed image data is then recovered by addressing collaborative recovery issues using the proposed Chronological Archery Algorithm (CAA) model. This model is based on an objective function called "Feature similarity, Collaborative sparsity Structural Similarity and Peak signal to noise (FCSP)" measure. Experimental results demonstrate that the proposed methodology achieves enhanced compression performance compared to existing techniques, with a Peak Signal-To-Noise Ratio (PSNR) of 81.876dB, Mean Square Error (MSE) of 0.005, and Structural Similarity Index Measure (SSIM) of 70.876.
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2024.3477628