Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks

In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To...

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Published in:Cognitive neurodynamics Vol. 19; no. 1; p. 11
Main Authors: Shanthi, K. G., Kinol, A. Mary Joy, Devi, S. Rukmani, Kannan, K.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.12.2025
Springer Nature B.V
Subjects:
Algorithms
Architecture
Arithmetic
Arithmetic coding
Artificial Intelligence
Binary codes
Biochemistry
Biomedical and Life Sciences
Biomedicine
Cognitive Psychology
Comparative analysis
Computer Science
Data processing
Data transmission
Decision trees
Deep learning
Energy consumption
Energy efficiency
Impulse response
Information processing
Innovations
Life span
Lookup tables
Neurosciences
Noise reduction
Optimization techniques
Partial differential equations
Power consumption
Research Article
Sensors
Signal processing
Wireless sensor networks
Offset binary coding
Look-up tables
Modified distributed arithmetic
Adaptive finite impulse response
Least mean squares
Multiply and accumulate
ISSN:1871-4080, 1871-4099
Online Access:Get full text
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Summary:In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework. By leveraging Modified Distributed Arithmetic (MDA) which optimizes arithmetic operations by replacing the multipliers with lookup tables (LUT) hence minimizing energy consumption as well as computational complexity. Offset Binary Coding (OBC) enhanced the efficiency of data transmission by minimizing the data representation overhead. In addition to this, the adaptive strategy is incorporated with the Adaptive Finite Impulse Response (AFIR) framework permitting the filters to dynamically adjust to varying signal characteristics, thus offering high noise suppression and low distortion rates. Comprehensive simulations and comparative analysis validate the effectiveness of the proposed MDA-OBC-based AFIR method. The proposed method attained a lower energy consumption of 1.5 J and 130 W power consumption than the traditional implementations, resulting in significant energy efficiency and data transmission in signal preprocessing and noise suppression in WSNs.
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ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-024-10190-1