
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Volume 7 Issue 4
July-August 2025
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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 |
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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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E-ISSN 2582-2160

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