Mathavan

@nscet.org

Assistant Professor ECE
Nadar Saraswathi College of Engineeering and Technology

Mathavan


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https://researchid.co/mathavan

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Information Systems
6

Scopus Publications

23

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Efficient indoor localization by integrating RFID with adaptive emperor penguin colony-based deep Q learning
    P. Sampath, K. Vijayalakshmi, M. Kala Rathi, N. Mathavan
    Journal of the Chinese Institute of Engineers Transactions of the Chinese Institute of Engineers Series A, 2025
    Radio Frequency Identification (RFID) is exploited for localizing objects in an indoor environment. Indoor localization is a critical component of various applications, ranging from smart homes to industrial automation. Traditional methods often suffer from limitations in accuracy and scalability. The existing models found it complex to develop a map approach of the tag received signal strength indicator (RSSI) and the distance of the tag and reader in an indoor nature. This work presents an enhanced model that integrates RFID technology using deep Q learning (DQL) and adaptive emperor penguin colony optimizer (AEPCO) for improving the relationship between tag position and RFID signals. By utilizing RFID tags and readers, spatial data are collected and then processed through a proposed DQL-AEPCO designed to accurately estimate positions within indoor environments. Moreover, for enhancing the training quality, the dataset pre-processed using Gaussian filtering (GF) is presented for eliminating RSSI faults. The demonstration proved that the suggested DQL-AEPCO can able to locate the multi-tags with high stability and robustness and outperformed the conventional models.
  • Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID
    Nagaraj Mathavan, Seenivasan Siva Ranjani, Seetharaman Suresh, Ananthan Bhuvanesh
    International Journal of Communication Systems, 2024
    SummaryIn an indoor setting, the radio frequency communication is utilized to locate and track mobile objects utilizing Radio Frequency Identification (RFID) technology. Measurements from the Received Signal Strength Indicator (RSSI) are typically the foundation of the localization technique. In an RFID‐based interior setting, lowering tracking mistakes and increasing the precision of tracking remain difficult tasks. In order to address these issues, we developed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) framework that consists of four procedures: deep reinforced learning‐based tracking, quantum‐based localization, optimization‐based virtual reference tag allocation, and signal enhancement. In order to increase the signal's effectiveness, we initially suggested using the Extended Gradient Filter (EGF) technique to eliminate RSSI oscillations. In the second step, we suggested using the Emperor Penguin Colony (EPC) optimization technique to allocate the virtual reference tag while taking the number of tags, SNR, and temperature and humidity of the surroundings into account. In the third phase, we use a quantum neural network (QNN) for localization in order to estimate the position of the moving target. We introduced the SignRank approach to select the best virtual reference tag for localization, which lowers tracking mistakes. In conclusion, we presented the Twin Delayed Deep Deterministic Policy Gradient (TD3) method that boosts the tracking precision by tracking the moving target tag efficiently and taking into account stage, the orientation, distance, and valuable coordinates. The NS3.26 network simulator is used to run the simulation, and tracking precision, tracking error, and accumulated probabilities are used to assess effectiveness.
  • Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation
    N. Mathavan, Dr. S. Sivaranjani
    Procedia Computer Science, 2024
    By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques.
  • High gain hybrid composite of DRA for 5G millimeter wave application
    B. Manikandan, R. Athilingam, S. Prathap, N. Mathavan, R. Pradeep Kumar, P. Shantha Devi
    Aip Conference Proceedings, 2023
  • Virtual Reference Tag Assisted Radio Frequency Identification Localization and Tracking Using Artificial Intellect Techniques in Indoor Environment
    Mathavan Nagaraj, Siva Ranjani Seenivasan
    Informacije MIDEM, 2023
    Radio Frequency Identification (RFID) technology is used to localize and track mobile objects using radio frequency communication in an indoor environment. Generally, the localization method is based on Received Signal Strength Indicator (RSSI) readings. However, improving the tracking accuracy and reducing tracking errors is still challenging in an RFID-based indoor environment. To overcome these problems, we proposed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) model, which includes four processes: signal improvement, optimization-based virtual reference tag allocation, quantum-based localization and deep reinforcement learning-based tracking. In the first process, we proposed an Extended Gradient Filter (EGF) algorithm for removing the RSSI fluctuations to improve the efficiency of the signal. In the second process, we proposed the Emperor Penguin Colony (EPC) optimization algorithm for allocating the virtual reference tag by considering SNR, number of tags and environmental factors (temperature, humidity). Based on this information, the RFID reader allocates the virtual reference tags for each gird, increasing the tracking accuracy. In the third process, we estimate the moving target's position by performing localization using Quantum Neural Network (QNN). To choose the optimal virtual reference tag for localization, we proposed the SignRank algorithm, which reduces the errors during tracking. Finally, we proposed Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for tracking by considering the distance, phase, orientation and precious coordinates, which effectively tracks the moving target tag, thus increasing tracking accuracy. The NS3.26 network simulator conducts the simulation, and the performance is evaluated regarding tracking accuracy, tracking error, and cumulative probability.
  • Energy-optimal scheduling with dynamic channel attainment in wireless downlinks
    J. Rajalakshmi, N. Mathavan, T. Venkatesh
    IC Get 2015 Proceedings of 2015 Online International Conference on Green Engineering and Technologies, 2016
    Channel acquisition is central to opportunistic scheduling for transmission over wireless channels. Practically it comes with probing power overhead. In low traffic regime, disregarding the channel and transmitting packets blindly may suffice to stabilize the network, and it wastes no probing power. Under the additional probing power cost assumption, we explore when is appropriate to probe the channel so that the network capacity can be achieved with minimum power consumption.

RECENT SCHOLAR PUBLICATIONS

  • Efficient indoor localization by integrating RFID with adaptive emperor Penguin colony-based deep Q learning
    P Sampath, K Vijayalakshmi, M Kala Rathi, N Mathavan
    Journal of the Chinese Institute of Engineers 48 (2), 183-194 , 2025
    2025
    Citations: 1
  • MINIATURISED HIGH GAIN ANTENNA FOR IOT DEVICES
    DJRMMMDSSRMTMMNMMJM Raj
    IN Patent 409954-001 , 2024
    2024
  • Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID
    N Mathavan, S Siva Ranjani, S Suresh, A Bhuvanesh
    International Journal of Communication Systems 37 (12), e5807 , 2024
    2024
    Citations: 4
  • Artificial intelligence and deep learning-based model for indoor environment: With virtual reference tag allocation
    N Mathavan, S Sivaranjani
    Procedia Computer Science 233, 254-268 , 2024
    2024
    Citations: 3
  • High gain hybrid composite of DRA for 5G millimeter wave application
    B Manikandan, R Athilingam, S Prathap, N Mathavan, R Pradeep Kumar, ...
    AIP Conference Proceedings 2831 (1), 070006 , 2023
    2023
    Citations: 1
  • Virtual Reference Tag Assisted Radio Frequency Identification Localization And Tracking Using Artificial Intellect Techniques In Indoor Environment
    M Nagaraj, SR Seenivasan
    Informacije MIDEM 53 (4), 207-233 , 2023
    2023
  • Electrical Switch with State Indication
    NM Co-Inventor)
    IN Patent App. 202,041,049,843 , 2020
    2020
  • RFID BASED TRACKING SYSTEM FOR INDICISIVENESS AREA (MINING SECURITY)
    DSSR Mr.N.Mathavan
    International Journal of New Technologies in Science and Engineering 5 (10), 5 , 2018
    2018
  • Cognitive radio spectrum sensing techniques–a survey
    G Manikandan, N Mathavan, M Suresh, M Paramasivam, V Lavanya
    International Journal of Advanced Engineering Technology 7 (2), 48-52 , 2016
    2016
    Citations: 11
  • Energy-optimal scheduling with dynamic channel attainment in wireless downlinks
    J Rajalakshmi, N Mathavan, T Venkatesh
    2015 Online International Conference on Green Engineering and Technologies … , 2015
    2015
  • FPGA BASED OPTIMAL SECURED COMMUNICATION
    MXB Mr.G. Manikandan, Mr. M.Paramasivan, Mr.N.Mathavan
    International Journal of Engineering Research and General Science ISSN 2091 … , 2015
    2015
  • A Compact CPW Antenna for UWB-USB Dongle Applications
    NM S.ARULOLI, T.VENKATESH, M.IDHAYACHANDRAN
    International Journal of Emerging Technology and Innovative Engineering ISSN … , 2015
    2015
  • RFID-Based Tracking System
    G Manikandan, N Mathavan, M Paramasivan, T Ashly
    International Journal Of Engineering And Computer Science 4 (3), 10755-10759 , 2015
    2015
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Cognitive radio spectrum sensing techniques–a survey
    G Manikandan, N Mathavan, M Suresh, M Paramasivam, V Lavanya
    International Journal of Advanced Engineering Technology 7 (2), 48-52 , 2016
    2016
    Citations: 11
  • Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID
    N Mathavan, S Siva Ranjani, S Suresh, A Bhuvanesh
    International Journal of Communication Systems 37 (12), e5807 , 2024
    2024
    Citations: 4
  • Artificial intelligence and deep learning-based model for indoor environment: With virtual reference tag allocation
    N Mathavan, S Sivaranjani
    Procedia Computer Science 233, 254-268 , 2024
    2024
    Citations: 3
  • RFID-Based Tracking System
    G Manikandan, N Mathavan, M Paramasivan, T Ashly
    International Journal Of Engineering And Computer Science 4 (3), 10755-10759 , 2015
    2015
    Citations: 3
  • Efficient indoor localization by integrating RFID with adaptive emperor Penguin colony-based deep Q learning
    P Sampath, K Vijayalakshmi, M Kala Rathi, N Mathavan
    Journal of the Chinese Institute of Engineers 48 (2), 183-194 , 2025
    2025
    Citations: 1
  • High gain hybrid composite of DRA for 5G millimeter wave application
    B Manikandan, R Athilingam, S Prathap, N Mathavan, R Pradeep Kumar, ...
    AIP Conference Proceedings 2831 (1), 070006 , 2023
    2023
    Citations: 1
  • MINIATURISED HIGH GAIN ANTENNA FOR IOT DEVICES
    DJRMMMDSSRMTMMNMMJM Raj
    IN Patent 409954-001 , 2024
    2024
  • Virtual Reference Tag Assisted Radio Frequency Identification Localization And Tracking Using Artificial Intellect Techniques In Indoor Environment
    M Nagaraj, SR Seenivasan
    Informacije MIDEM 53 (4), 207-233 , 2023
    2023
  • Electrical Switch with State Indication
    NM Co-Inventor)
    IN Patent App. 202,041,049,843 , 2020
    2020
  • RFID BASED TRACKING SYSTEM FOR INDICISIVENESS AREA (MINING SECURITY)
    DSSR Mr.N.Mathavan
    International Journal of New Technologies in Science and Engineering 5 (10), 5 , 2018
    2018
  • Energy-optimal scheduling with dynamic channel attainment in wireless downlinks
    J Rajalakshmi, N Mathavan, T Venkatesh
    2015 Online International Conference on Green Engineering and Technologies … , 2015
    2015
  • FPGA BASED OPTIMAL SECURED COMMUNICATION
    MXB Mr.G. Manikandan, Mr. M.Paramasivan, Mr.N.Mathavan
    International Journal of Engineering Research and General Science ISSN 2091 … , 2015
    2015
  • A Compact CPW Antenna for UWB-USB Dongle Applications
    NM S.ARULOLI, T.VENKATESH, M.IDHAYACHANDRAN
    International Journal of Emerging Technology and Innovative Engineering ISSN … , 2015
    2015