Renuga Devi Subramanian
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Renuga Devi Subramanian has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Pachaiyappan Arulmozhivarman
VIT University
2 shared publications Aryan Sharma
SCOPE, Vellore Institute of Technology
1 shared publicationC. Venkatesh
Sengunthar Engineering College
1 shared publication- D. Sumathi
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
1 shared publication Pranay Agarwal
VIT University
1 shared publication- Sean Laurel Rex Bashyam
School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
1 shared publication
Latest Publications
- Open Access
- 19 March 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (3) , 163
Energy-Efficient Wireless Sensor Networks Through Coverage Hole Detection and Mitigation Using a Hybrid Raccoon–Hermit Crab Optimization Algorithm
Wireless sensor networks encounter issues like irregular deployment, node failures, and uneven energy consumption that create coverage holes, leading to a reduction in network lifetime in critical or disaster-based applications. Most existing approaches focus on coverage enhancement during the initial deployment and perform mitigation only at the beginning of the network operation. However, the coverage holes may also occur later due to node failures and energy depletion. To address this issue, a Hybrid Raccoon–Hermit crab optimization algorithm that advocates both initial coverage enhancement and adaptive mitigation due to future coverage holes is proposed. The proposed algorithm uses the global exploration ability of the raccoon optimization algorithm to find optimal cluster heads and the exploitation ability of the Hermit crab optimization to determine the optimal position and to relocate the static nodes logically to mitigate coverage holes. The proposed algorithm is evaluated under different node densities (50, 100, 200, 500, and 1000), with the sink at . It results in an enhanced network lifetime of , an improved coverage ratio from to , increased throughput by delivering (3,139,293) bits, and a reduced delay of s for 1000 nodes compared with other existing methods.- 19 March 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (3) , 163
Open AccessEnergy-Efficient Wireless Sensor Networks Through Coverage Hole Detection and Mitigation Using a Hybrid Raccoon–Hermit Crab Optimization Algorithm
Wireless sensor networks encounter issues like irregular deployment, node failures, and uneven energy consumption that create coverage holes, leading to a reduction in network lifetime in critical or disaster-based applications. Most existing approaches focus on coverage enhancement during the initial deployment and perform mitigation only at the beginning of the network operation. However, the coverage holes may also occur later due to node failures and energy depletion. To address this issue, a Hybrid Raccoon–Hermit crab optimization algorithm that advocates both initial coverage enhancement and adaptive mitigation due to future coverage holes is proposed. The proposed algorithm uses the global exploration ability of the raccoon optimization algorithm to find optimal cluster heads and the exploitation ability of the Hermit crab optimization to determine the optimal position and to relocate the static nodes logically to mitigate coverage holes. The proposed algorithm is evaluated under different node densities (50, 100, 200, 500, and 1000), with the sink at . It results in an enhanced network lifetime of , an improved coverage ratio from to , increased throughput by delivering (3,139,293) bits, and a reduced delay of s for 1000 nodes compared with other existing methods. - 29 August 2025
- Published by Springer Nature
- p. 103-114
Hybridizing CNN with an LSTM Back-End for Univariate Rainfall Forecasting
- 29 August 2025
- Published by Springer Nature
- p. 103-114
Hybridizing CNN with an LSTM Back-End for Univariate Rainfall Forecasting
- 18 May 2017
- Published by Springer Nature
- p. 175-182
ANN Based Rainfall Prediction—A Tool for Developing a Landslide Early Warning System
- 18 May 2017
- Published by Springer Nature
- p. 175-182
ANN Based Rainfall Prediction—A Tool for Developing a Landslide Early Warning System
- Open Access
- 6 October 2016
- Published by Indian Society for Education and Environment in Indian Journal of Science and Technology
- Vol. 9 (37)
Dynamic Weight Assignment based Vertical Handoff Algorithm for Load Optimization
Objectives: Consolidating any two dissimilar networks leads to a formation of a heterogeneous wireless network. Vision to achieve distinct networks to get converged such that characterizing of the upcoming wireless networks becomes a reality. This in turn ushers in vertical handoff such that handoff among different technologies is efficient. And at the same time it is so smooth that naming it as seamless is justifiable. Pragmatic consideration of the network characteristics and the dynamics are very essential to choose one of the best available network handoff decisions. Methods: Stating conventionally, when a mobile client is roaming, a single criterion- for example, received signal strength is employed to realize the vertical handoff. But, single criterion consideration is not sufficient and taking into account of other parameters is needed for a proper handoff decision. The present paper puts forth a strong load balancing algorithm M-OPTF that is based on dynamic weight assignment technique for allocation of fresh calls and handoff calls. Findings: In the present scheme assignment of weight factor is done to attachment points basing on the distribution of load on them. In addition it contains a process for handoff decision that takes into account the velocity of mobile node to do triggering of handoff. The put forth M-OPTF algorithm is compared along with Remote Sensing Systems (RSS) and OPT-F algorithm in this paper. Application/Improvements: The simulation results confirm the fact that M-OPTF algorithm apart from balancing the network load it can effectively decrease congestion in network, reduce the number of handoffs that are unnecessary, and increases the battery life time, thus improve the overall performance of the system.
- 6 October 2016
- Published by Indian Society for Education and Environment in Indian Journal of Science and Technology
- Vol. 9 (37)
Open AccessDynamic Weight Assignment based Vertical Handoff Algorithm for Load Optimization
Objectives: Consolidating any two dissimilar networks leads to a formation of a heterogeneous wireless network. Vision to achieve distinct networks to get converged such that characterizing of the upcoming wireless networks becomes a reality. This in turn ushers in vertical handoff such that handoff among different technologies is efficient. And at the same time it is so smooth that naming it as seamless is justifiable. Pragmatic consideration of the network characteristics and the dynamics are very essential to choose one of the best available network handoff decisions. Methods: Stating conventionally, when a mobile client is roaming, a single criterion- for example, received signal strength is employed to realize the vertical handoff. But, single criterion consideration is not sufficient and taking into account of other parameters is needed for a proper handoff decision. The present paper puts forth a strong load balancing algorithm M-OPTF that is based on dynamic weight assignment technique for allocation of fresh calls and handoff calls. Findings: In the present scheme assignment of weight factor is done to attachment points basing on the distribution of load on them. In addition it contains a process for handoff decision that takes into account the velocity of mobile node to do triggering of handoff. The put forth M-OPTF algorithm is compared along with Remote Sensing Systems (RSS) and OPT-F algorithm in this paper. Application/Improvements: The simulation results confirm the fact that M-OPTF algorithm apart from balancing the network load it can effectively decrease congestion in network, reduce the number of handoffs that are unnecessary, and increases the battery life time, thus improve the overall performance of the system.
- 2 September 2016
- Published by Springer Nature in International Journal of Automation and Computing
- Vol. 13 (5) , 417-427
Performance comparison of artificial neural network models for daily rainfall prediction
- 2 September 2016
- Published by Springer Nature in International Journal of Automation and Computing
- Vol. 13 (5) , 417-427
Performance comparison of artificial neural network models for daily rainfall prediction