SHANMUGASUNDARAM N

@sece.ac.in

Head, Electronics and Communication Engineering
sri eshwar college of engineering

SHANMUGASUNDARAM N


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https://researchid.co/raja_ns

EDUCATION

B.E. (ECE), M.E. (Communication Systems), Ph.D. (Wireless Sensor Networks)

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems, Software
25

Scopus Publications

809

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Advances and Trends in Low-Power Logic-Compatible Phase Change Memory (PCM): A Comprehensive Survey
    Saranyanandhini D, Shanmugasundaram N, Mohan Kumar M, Nandhini M
    2026 2nd International Conference on Intelligent Systems for Communication Iot and Security Iciscois 2026, 2026
    Phase Change Memory (PCM) is a non-volatile technology which has received significant interest as a next-generation compute in-memory technology and artificial intelligence, as well as a high performance and energy efficient embedded system. This paper contains an in-depth experimental study of lowpower, logic compatible 40 nm PCM cells, principally regarding their reliability, reliability, and compatibility with neuromorphic and artificial intelligence devices. The wide characterization of the devices included endurance cycling, resistance drift experiments, high temperature data retention tests, and variability tests. At 120° C, retention studies indicate that with a heat of more than 120° C a memory life of more than ten years is observed and endurance tests indicates good performance up to 200,000 program/erase cycles with no memory window erosion. The measurements of drift in high resistance and low resistance stages reflect the resistance development that is well controlled and statistical measures confirm narrow distributions that are vital to stable operation and multi-level programming. The potential of PCM arrays is also validated by analysis in binary neural network models, wherein the robustness of the performance of the simulated inference distributions is obtained with simulated noise and unpredictability in the devices. On the whole, the results make low power PCM one of the leaders in embedded and artificial intelligence computing systems that offer scalable integration of the system, a high endurance level, precise programmability, and long term data retention.
  • Optimized Tree Construction and Clustering-Based Data Aggregation for Heterogeneous Wireless Sensor Networks Using Ford-Fulkerson Algorithm
    T Kiruthiga, N Shanmugasundaram
    Journal of Intelligent and Fuzzy Systems, 2025
    The use of wireless sensor networks (WSNs) as a key technology for controlling and monitoring a range of applications has been accepted. Heterogeneous WSNs involve nodes with different functions, for instance, nodes with sensing, fusion, and routing duties. To maximize the performance of a heterogeneous WSN, an optimized tree construction and clustering-based data aggregation approach is proposed in this paper. The Ford-Fulkerson algorithm is used to construct an optimized spanning tree with minimum energy consumption. Clusters are then formed in a distributed manner, and data aggregation is performed among the clusters. The suggested strategy is effective, as shown by the simulation results, and adding a reliable optimization method greatly lowers energy usage and enhances network performance. The research provides a clustering-based data aggregation strategy meant to maximize data delivery from heterogeneous WSNs with low-power nodes. The employed technique combines two clustering algorithms, k-means, and fuzzy c-means, to ensure fast and reliable data forwarding among WSNs. The proposed FFA obtained 95.86% energy efficiency, 87.21% QoS, 95.25% transmission rate, 90.13% PDR, 8.86% delay, 96.21% network lifetime, 91.22% throughput, 89.42% scalability and 96.58% Fault Tolerance. The proposed method can significantly reduce energy consumption and communication overhead, improving network efficiency and utilization. This work has the potential to significantly impact the design and operation of wireless sensor networks in various applications.
  • Deep learning based Enhanced Data Aggregation with Multi-Objective Optimization method in Wireless Sensor Networks
    Thiyagarajan N, Shanmugasundaram N
    Peer to Peer Networking and Applications, 2025
  • Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection
    V. K. U. Ahamed Gani, N. Shanmugasundaram
    Aip Advances, 2025
    The escalating global prevalence of diabetes has underscored the critical need for effective screening and diagnosis of diabetic retinopathy (DR), a common complication of diabetes that can lead to irreversible vision loss. In this study, we propose a novel algorithm for automated DR detection in retinal fundus images using deep learning techniques. The algorithm incorporates a cheetah optimized convolutional neural network (CO-CNN) that draws inspiration from cheetah hunting behavior for efficient image processing, segmentation, feature extraction, and classification. Preprocessing steps involve median filter and contrast limited adaptive histogram equalization to enhance image quality. The segmented output is clustered using the cascaded fuzzy C-means algorithm and features are extracted with the speeded-up robust features algorithm. The experimental results on the Indian Diabetic Retinopathy Image Dataset demonstrate an accuracy of 98.64% in predicting various stages of DR. The proposed CO-CNN approach shows superior performance compared to that of state-of-the-art methods, offering potential applications in telemedicine, treatment planning, early detection, screening, and patient education. Integrating fuzzy logic enhances the model’s interpretability and robustness, paving the way for improved healthcare outcomes in diabetic retinopathy management.
  • Optimized Deep Learning Framework for Automated Diabetic Retinopathy Detection
    V. K. U. Ahamed Gani, N. Shanmugasundaram
    2025 2nd International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2025, 2025
    Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide, emphasizing the need for early detection and timely intervention. Traditional manual screening methods are time-consuming, subjective, and prone to variability, necessitating the development of automated and highly accurate diagnostic tools. This study introduces an advanced deep learning framework that integrates multiple state-of-the-art architectures to enhance DR detection performance. Vision Transformers (ViT) are utilized for superior feature extraction, capturing both global and local retinal abnormalities effectively. An attention-based U-Net is incorporated for precise lesion segmentation, improving localization and interpretability. Additionally, Efficient-Net, combined with Swarm Intelligence optimization, is employed for classification, ensuring high accuracy while maintaining computational efficiency. The proposed model is evaluated on the IDRiD and Messidor-2 datasets, demonstrating its robustness across diverse retinal images. Experimental results indicate that our framework achieves an outstanding accuracy of 99.12%, outperforming existing deep learning-based methods in DR detection. The integration of attention mechanisms and optimization techniques significantly enhances the model's reliability and generalization capability. Furthermore, the framework is designed for real-time applications, making it suitable for deployment in clinical settings. The high accuracy and efficiency of our model highlight its potential as a valuable tool for ophthalmologists, aiding in early diagnosis and treatment planning. By automating DR detection, this approach can help reduce the burden on healthcare professionals and improve patient outcomes. The findings underscore the effectiveness of deep learning-driven solutions in advancing retinal disease diagnosis and enhancing ophthalmic healthcare.
  • Simulation and Analysis Framework for STT-MRAM: Exploring Switching Dynamics, Energy Metrics, and System-Level Reliability
    Shivanandham R S, Shanmugasundaram N, Mohan Kumar M, Sharmila Parveen M
    IC Decon 2025 2025 International Conference on Data Energy and Communication Network Proceedings, 2025
    This paper examines the behaviour of Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) using a model that is designed in MATLAB with a base paper reference from TSMC. The framework describes magnetization dynamics on the basis of the Landau-Lifshitz-Gilbert (LLG) equation and controls the switching properties through Monte Carlo simulations, including thermal noise. The findings reveal consistent coherent precessional switching whereby the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{z}$</tex> is changed between -1 to +1 in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0 - 5 0}$</tex> ns, which is just consistent with the experiments. Switching probability also changes abruptly between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0-100$</tex> percent in the current density of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \times 10^{6}-5 \times 10^{6} \mathbf{A} / \mathbf{c m}^{2}$</tex>, which confirms practical device operation. Energy-delay analysis shows switching energy between 100500 fJ and delay between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20-40 ~\text{ns}$</tex> which similar results are reported in the literature. The equipment is stable to thermal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(250-400 ~\mathrm{K})$</tex> and damping (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.005-0.03$</tex>) changes and performance measures have a variation less than 10 percent. Endurance Endurance is more than 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> write cycles, and bit-error rates less than 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup>, and magnetic immunity is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 0 0 0 O e}$</tex>. System- level testing ensures stable operation and zero failures during Monte Carlo testing as well as write operations were more powered and took longer than read. Altogether, this framework offers an effective and tested study instrument to analyze and optimize and comprehend the performance features of STT-MRAM.
  • Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN
    J. Logeshwaran, R. N. Shanmugasundaram, Jaime Lloret
    Wireless Networks, 2024
    The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.
  • Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless Sensor Networks
    N. Thiyagarajan, N. Shanmugasundaram
    Indiscon 2024 5th IEEE India Council International Subsections Conference Science Technology and Society, 2024
    The real-time data collection and monitoring process demands Wireless Sensor Networks (WSNs) as a pivotal technology that employs randomly distributed battery-operated sensor modules to facilitate seamless data transmission from remote and often hostile environments. The versatility of WSNs extends across various domains, including monitoring of smart cities. By harnessing the power of WSNs, we can optimise processes, enhance productivity, and mitigate risks more effectively. Clustering is the widely discussed concrete part of the energy mitigating process. We wish to evaluate the clustering in WSN with the Fuzzy C-Means Clustering, K-medoid clustering, and K-means. The clustering process was analysed with the estimation of the silhouette score. In order to assess their performance, numerous simulations are conducted in the MATLAB environment. Furthermore, we discussed their mean silhouette score, clustering ability for different scenarios in WSN.
  • Energy-efficient resource allocation model for device-to-device communication in 5G wireless personal area networks
    Jaganathan Logeshwaran, Nallasamy Shanmugasundaram, Jaime Lloret
    International Journal of Communication Systems, 2023
    SummaryIn general, there are several many devices that can overload the network and reduce performance. Devices can minimize interference and optimize bandwidth usage by using directional antennas and by avoiding overlapping communication ranges. In addition, devices need to carefully manage their use of resources, such as bandwidth and energy. Bandwidth is limited in wireless personal area networks (WPANs), so devices need to carefully select which data to send and receive. In this paper, an intelligent performance analysis of energy‐efficient resource optimization model has been proposed for device‐to‐device (D2D) communication in fifth‐generation (5G) WPAN. The proposed energy‐efficient resource allocation in D2D communication is important because it helps reduce energy consumption and extend the lifespan of devices that are communicating with each other. By allocating resources in an efficient manner, communication between two devices can be optimized for maximum efficiency. This helps reduce the amount of energy needed to power the communication, as well as the amount of energy needed to power the device that is communicating with another device. Additionally, efficient resource allocation helps reduce the overall cost of communication, as the use of fewer resources results in a lower overall cost. The proposed efficient resource allocation helps reduce the environmental impact of communication, as less energy is used for communication. The proposed energy‐efficient resource allocation model (EERAM) has reached 92.97% of energy allocation, 88.72% of power allocation, 87.79% of bandwidth allocation, 87.93% of spectrum allocation, 88.43% of channel allocation, 25.47% of end‐to‐end delay, 94.33% of network data speed, and 90.99% of network throughput.
  • L-RUBI: An efficient load-based resource utilization algorithm for bi-partite scatternet in wireless personal area networks
    Jaganathan Logeshwaran, Nallasamy Shanmugasundaram, Jaime Lloret
    International Journal of Communication Systems, 2023
    SummaryRecently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR.
  • Security Provisioning as Integrity in wireless sensor networks (WSN): A Survey
    P. Venkateswari, N. Shanmugasundaram
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • Smart Agriculture Using Modern Technologies
    N Shanmugasundaram, G Santhip Kumar, S Sankaralingam, S Vishal, N Kamaleswaran
    2023 9th International Conference on Advanced Computing and Communication Systems Icaccs 2023, 2023
  • Estimation of a Channel Model and Power Control Scheme for Radio Environment based on Multiple Access
    Kalaiarasi Arumugam, N Rajesha, N Shanmugasundaram, M Prasad, R Agilesh Sarvanan, Santhi Chebiyyam
    2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
  • Spectrum Sensing Framework and Energy-Efficient Resource Allocation for Cognition Enhancement Network
    Kalaiarasi Arumugam, N Rajesha, M Prasad, N Shanmugasundaram, D Sreenivasa Rao, B. Suneela
    2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
  • An Investigation on Energy Consumption in Wireless Sensor Network
    N. Thiyagarajan, N. Shanmugasundaram
    8th International Conference on Advanced Computing and Communication Systems Icaccs 2022, 2022
  • Computerized Framework Used to Detect Glaucoma: A Review
    V.K.U. Ahamed Gani, N. Shanmugasundaram
    8th International Conference on Advanced Computing and Communication Systems Icaccs 2022, 2022
  • Recent advancements in automated screening techniques for diabetic retinopathy
    V.K.U. Ahamed Gani, N. Shanmugasundaram, N. Thiyagarajan
    Proceedings of the 2022 3rd International Conference on Communication Computing and Industry 4 0 C2i4 2022, 2022
  • JAYA Algorithm and its Performance on Energy Efficiency for Wireless Sensor Networks
    N. Thiyagarajan, N. Shanmugasundaram, V.K.U. Ahamed Gani
    3rd International Conference on Smart Electronics and Communication Icosec 2022 Proceedings, 2022
  • Smart Lighting System Using the Internet of Things
    N Shanmugasundaram, S Ramalingam, Alim Khan, SB Bharath, N Kalikumar, B Kishore
    8th International Conference on Advanced Computing and Communication Systems Icaccs 2022, 2022
  • In-network data aggregation techniques for wireless sensor networks: A survey
    T. Kiruthiga, N. Shanmugasundaram
    Lecture Notes on Data Engineering and Communications Technologies, 2021
  • Enhancements of Resource Management for Device to Device (D2D) Communication: A Review
    J Logeshwaran, R.N. Shanmugasundaram
    Proceedings of the 3rd International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2019, 2019
  • An Improved Low Complex Offset Min-Sum Based Decoding Algorithm for LDPC Codes
    Michaelraj Kingston Roberts, S. Sudha Mohanram, N. Shanmugasundaram
    Mobile Networks and Applications, 2019
  • Clock Gating Techniques: An Overview
    Tamil Chindhu S., N. Shanmugasundaram
    Proc IEEE Conference on Emerging Devices and Smart Systems Icedss 2018, 2018
  • Performance Evaluation of Balanced Partitioning Dynamic Cluster Head Algorithm (BP-DCA) for Wireless Sensor Networks
    M. Madheswaran, R. N. Shanmugasundaram
    Wireless Personal Communications, 2016
  • Distance based residual energy for cluster head selection in wireless sensor networks
    International Journal of Applied Engineering Research, 2015

RECENT SCHOLAR PUBLICATIONS

  • Optimized Deep Learning Framework for Automated Diabetic Retinopathy Detection
    VKUA Gani, N Shanmugasundaram
    2025 Second International Conference on Intelligent Technologies for … , 2025
    2025
  • Optimized Tree Construction and Clustering-Based Data Aggregation for Heterogeneous Wireless Sensor Networks Using Ford-Fulkerson Algorithm
    T Kiruthiga, N Shanmugasundaram
    Journal of Intelligent & Fuzzy Systems 49 (4), 1039-1056 , 2025
    2025
  • Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection
    VKU Ahamed Gani, N Shanmugasundaram
    AIP Advances 15 (5) , 2025
    2025
    Citations: 2
  • Deep learning based enhanced data aggregation with multi-objective optimization method in wireless sensor networks
    N Thiyagarajan, N Shanmugasundaram
    PEER-TO-PEER NETWORKING AND APPLICATIONS 18 (3) , 2025
    2025
    Citations: 2
  • Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless Sensor Networks
    N Thiyagarajan, N Shanmugasundaram
    2024 IEEE 5th India Council International Subsections Conference (INDISCON), 1-5 , 2024
    2024
    Citations: 1
  • Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN
    J Logeshwaran, RN Shanmugasundaram, J Lloret
    Wireless Networks 30 (4), 2477-2509 , 2024
    2024
    Citations: 72
  • Energy‐efficient resource allocation model for device‐to‐device communication in 5G wireless personal area networks
    J Logeshwaran, N Shanmugasundaram, J Lloret
    International Journal of Communication Systems 36 (13), e5524 , 2023
    2023
    Citations: 143
  • Security Provisioning as Integrity in wireless sensor networks (WSN): A Survey
    P Venkateswari, N Shanmugasundaram
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 1
  • L‐RUBI: An efficient load‐based resource utilization algorithm for bi‐partite scatternet in wireless personal area networks
    J Logeshwaran, N Shanmugasundaram, J Lloret
    International Journal of Communication Systems 36 (6), e5439 , 2023
    2023
    Citations: 201
  • Smart agriculture using modern technologies
    N Shanmugasundaram, GS Kumar, S Sankaralingam, S Vishal, ...
    2023 9th international conference on advanced computing and communication … , 2023
    2023
    Citations: 21
  • Spectrum Sensing Framework and Energy-Efficient Resource Allocation for Cognition Enhancement Network
    K Arumugam, N Rajesha, M Prasad, N Shanmugasundaram, DS Rao, ...
    2023 International Conference on Computer Communication and Informatics … , 2023
    2023
    Citations: 1
  • Estimation of a Channel Model and Power Control Scheme for Radio Environment based on Multiple Access
    K Arumugam, N Rajesha, N Shanmugasundaram, M Prasad, ...
    2023 International Conference on Computer Communication and Informatics … , 2023
    2023
  • Recent advancements in automated screening techniques for diabetic retinopathy
    VKUA Gani, N Shanmugasundaram, N Thiyagarajan
    2022 3rd International Conference on Communication, Computing and Industry 4 … , 2022
    2022
    Citations: 3
  • Jaya algorithm and its performance on energy efficiency for wireless sensor networks
    N Thiyagarajan, N Shanmugasundaram, VKUA Gani
    2022 3rd International Conference on Smart Electronics and Communication … , 2022
    2022
    Citations: 1
  • Smart Lighting System Using the Internet of Things
    N Shanmugasundaram, S Ramalingam, A Khan, SB Bharath, ...
    2022 8th International Conference on Advanced Computing and Communication … , 2022
    2022
    Citations: 3
  • An investigation on energy consumption in wireless sensor network
    N Thiyagarajan, N Shanmugasundaram
    2022 8th International Conference on Advanced Computing and Communication … , 2022
    2022
    Citations: 17
  • Computerized framework used to detect glaucoma: A review
    VKUA Gani, N Shanmugasundaram
    2022 8th International Conference on Advanced Computing and Communication … , 2022
    2022
    Citations: 1
  • In-Network Data Aggregation Techniques for Wireless Sensor Network: A Survey
    Kiruthiga T, Shanmugasundaram RN
    3rd International Conference on Computer Networks, Big Data and IoT (ICCBI … , 2020
    2020
    Citations: 27
  • Enhancements of Resource Management for Device to Device (D2D) Communication: A Review
    Logeshwaran J, Shanmugasundaram RN
    Proceedings of Third International Conference on I-SMAC (IoT in Social … , 2019
    2019
    Citations: 172
  • An Improved Low Complex Offset Min-Sum Based Decoding Algorithm for LDPC Codes
    N Roberts, M.K., Mohanram, S.S., Shanmugasundaram
    Mobile Networks and Applications 24 (6), 1848-1852 , 2019
    2019
    Citations: 23

MOST CITED SCHOLAR PUBLICATIONS

  • L‐RUBI: An efficient load‐based resource utilization algorithm for bi‐partite scatternet in wireless personal area networks
    J Logeshwaran, N Shanmugasundaram, J Lloret
    International Journal of Communication Systems 36 (6), e5439 , 2023
    2023
    Citations: 201
  • Enhancements of Resource Management for Device to Device (D2D) Communication: A Review
    Logeshwaran J, Shanmugasundaram RN
    Proceedings of Third International Conference on I-SMAC (IoT in Social … , 2019
    2019
    Citations: 172
  • Energy‐efficient resource allocation model for device‐to‐device communication in 5G wireless personal area networks
    J Logeshwaran, N Shanmugasundaram, J Lloret
    International Journal of Communication Systems 36 (13), e5524 , 2023
    2023
    Citations: 143
  • Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN
    J Logeshwaran, RN Shanmugasundaram, J Lloret
    Wireless Networks 30 (4), 2477-2509 , 2024
    2024
    Citations: 72
  • Clock Gating Techniques: An Overview
    SN Tamil Chindhu S
    Proc. IEEE Conference on Emerging Devices and Smart Systems (ICEDSS 2018 … , 2018
    2018
    Citations: 45
  • Enhancements of leach algorithm for wireless networks: A review.
    M Madheswaran, RN Shanmugasundaram
    Economy of Industry 4 (4) , 2013
    2013
    Citations: 35
  • In-Network Data Aggregation Techniques for Wireless Sensor Network: A Survey
    Kiruthiga T, Shanmugasundaram RN
    3rd International Conference on Computer Networks, Big Data and IoT (ICCBI … , 2020
    2020
    Citations: 27
  • An Improved Low Complex Offset Min-Sum Based Decoding Algorithm for LDPC Codes
    N Roberts, M.K., Mohanram, S.S., Shanmugasundaram
    Mobile Networks and Applications 24 (6), 1848-1852 , 2019
    2019
    Citations: 23
  • Smart agriculture using modern technologies
    N Shanmugasundaram, GS Kumar, S Sankaralingam, S Vishal, ...
    2023 9th international conference on advanced computing and communication … , 2023
    2023
    Citations: 21
  • An investigation on energy consumption in wireless sensor network
    N Thiyagarajan, N Shanmugasundaram
    2022 8th International Conference on Advanced Computing and Communication … , 2022
    2022
    Citations: 17
  • Performance Evaluation of Balanced Partitioning Dynamic Cluster Head Algorithm (BP-DCA) for Wireless Sensor Networks
    MM R. N. Shanmugasundaram
    Wireless Personal Communications 89 (1), 195-210 , 2016
    2016
    Citations: 15
  • Clock-Gating Techniques: An Overview
    SC Tamil, N Shanmugasundaram
    2018 Conference on Emerging Devices and Smart Systems (ICEDSS), 217-221 , 2018
    2018
    Citations: 11
  • D-LEACH: Dynamic Cluster Head Selection Algorithm for LEACH Protocol in Wireless Sensor Networks
    RNS M.Madheswaran
    Australian Journal of Basic and Applied Sciences 18 (8), 389-398 , 2014
    2014
    Citations: 8
  • Distance Based Residual Energy For Cluster Head Selection in Wireless Sensor Networks
    MM R.N.Shanmugasundaram
    International Journal of Applied Engineering Research 10 (9), 22779-22790 , 2015
    2015
    Citations: 4
  • Recent advancements in automated screening techniques for diabetic retinopathy
    VKUA Gani, N Shanmugasundaram, N Thiyagarajan
    2022 3rd International Conference on Communication, Computing and Industry 4 … , 2022
    2022
    Citations: 3
  • Smart Lighting System Using the Internet of Things
    N Shanmugasundaram, S Ramalingam, A Khan, SB Bharath, ...
    2022 8th International Conference on Advanced Computing and Communication … , 2022
    2022
    Citations: 3
  • Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection
    VKU Ahamed Gani, N Shanmugasundaram
    AIP Advances 15 (5) , 2025
    2025
    Citations: 2
  • Deep learning based enhanced data aggregation with multi-objective optimization method in wireless sensor networks
    N Thiyagarajan, N Shanmugasundaram
    PEER-TO-PEER NETWORKING AND APPLICATIONS 18 (3) , 2025
    2025
    Citations: 2
  • Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless Sensor Networks
    N Thiyagarajan, N Shanmugasundaram
    2024 IEEE 5th India Council International Subsections Conference (INDISCON), 1-5 , 2024
    2024
    Citations: 1
  • Security Provisioning as Integrity in wireless sensor networks (WSN): A Survey
    P Venkateswari, N Shanmugasundaram
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 1