International Journal For Multidisciplinary Research

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ADMM-Driven Data Recovery for Reliable Vehicle Counting in IoT-Based Wireless Sensor Networks

Author(s) Ms. PAVANI TEJA ADDANKI, Ms. Rithani M
Country India
Abstract In the context of IoT-based applications, Wireless
Sensor Networks (WSNs) are integrated into the Automatic Vehicle
Counting (AVC) systems for the purpose of advanced traffic man-
agement. As with many other applications, sensor node dropout,
data transmission issues, and noise from the environment can
lead data loss in WSN, which affects the precision of vehicle
detection. We suggest the use of the data recovery framework that
combines signal processing methods with the Alternating Direction
Method of Multipliers (ADMM) for data reclamation in WSN-
based AVC systems. Compressed sensing (CS) techniques ensure
that less data is sent over the network and increases the efficiency
of data retrieval. Total Variation (TV) regularization enforces signal
smoothness constraints, Non-Local Means (NLM) Filter performs
noise reduction, and both wavelet transformation and multi-
resolution analysis are applied for signal denoising.Incorporating
ADMM into the framework as an optimization engine allows for
the iterative refinement of the recovered signal by the ADMM
framework while ensuring data accuracy and preservation of the
signal structures. As is shown in the experiments, the algorithm
maintains high accuracy even with missing or degraded sensor
readings. Improved precision in vehicle counting is achieved,
especially in noisy and low-resource wireless environments. The
framework is shown to enhance the router integrity in IoT systems
and provides solutions for real-time data issues in traffic monitoring
systems
Keywords ADMM, WSNs, IoT, AVC, Signal processing, CS, TV, NLM, Wavelet transform
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-29
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.52438
Short DOI https://doi.org/g9vpnb
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