DR A BHUVANESHWARI

@klh.edu.in

DEPARTMENT OF ECE
Koneru Lakshmaiah Education Foundation, Hyderabad-500075, Telangana, India.

DR A BHUVANESHWARI


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https://researchid.co/sbhuvaneshwari
Dr A.Bhuvaneshwari, obtained her B. E degree in the field of ECE, in 1995, M.Tech degree with specialization in Digital Systems & Computer Electronics (D.S.C.E), and PhD in Wireless Communications from Jawaharlal Nehru Technological University, Hyderabad (J.N.T.U.H) in 2019. She has worked as an Osmania University ratified Associate Professor in Deccan College of Engineering and Technology, Hyderabad from August 2004 till March 2026. Earlier she has worked in Vijayanagar Engineering college, Bellary renamed as (RYMEC), from 1997 to March 2000. She has 24 years of teaching experience, enriching research experience, published papers in standard journals and has guided several students. Her research interests include Wireless Mobile Communications, Neural Networks, Deep Learning, Image, Video & Speech Processing and Computer Vision.

EDUCATION

BE, MTech (Digital Systems and Computer Electronics ) , Phd (Wireless Communication)

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing
12

Scopus Publications

161

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • SEAL-MAC: An Energy-Anticipatory Adaptive LEACH Protocol using LSTM Forecasting for Solar-Powered IoT-WSNs
    Abdul Aleem, Rajesh Thumma, Md Imran V A, H. Abdul Wasay, A. Bhuvaneshwari, Amina Begum
    Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
    A fundamental challenge in solar-powered Internet of Things-based Wireless Sensor Networks (IoT-WSNs) is to ensure long-term energy sustainability as the harvested energy is intermittent and less predictable. The traditional clustering methods like LEACH and its variants are based on residual energy or random cluster-head selection. They do not take the advantage of future energy availability, which results in inefficient energy utilization and low network lifetime. The paper suggests Solar Energy Anticipatory Leach- Medium Access Control (SEAL-MAC), an energy-anticipatory adaptive LEACH that incorporates the solar energy forecasting with the application of Long Short-Term Memory (LSTM). In SEAL-MAC sensor nodes forecast the harvested short-term energy based on history of solar data. The forecasted energy is integrated in both cluster-head selection and MAC-layer scheduling. This cross-layer model allows energy-consciousness in role assignment, duty cycling and low communication overheads in dynamic energy scenarios. Simulation findings show that SEAL-MAC achieves energy savings of up to 9.7%, network lifetime of 10.5% and a packet delivery ratio of 1.6% as compared to the state-of-the-art LEACH-based protocols. The results verify that predictive energy management can be successful in resilient long-term deployments of IoT-WSN.
  • Dual Information Audio Watermarking with Modified Wavelet Based LSB Technique
    Lecture Notes in Engineering and Computer Science, 2023
  • Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation
    Bhuvaneshwari Achayalingam, Hemalatha Rallapalli, Satya Savithri Tirumala
    Learning and Analytics in Intelligent Systems, 2020
  • Path loss model optimization using stochastic hybrid genetic algorithm
    International Journal of Engineering and Technology Uae, 2018
  • Development of an Optimized Ray Tracing Path Loss Model in the Indoor Environment
    A. Bhuvaneshwari, R. Hemalatha, T. Satya Savithri
    Wireless Personal Communications, 2017
  • Performance evaluation of Dynamic Neural Networks for mobile radio path loss prediction
    A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri
    2016 IEEE Uttar Pradesh Section International Conference on Electrical Computer and Electronics Engineering Upcon 2016, 2017
    The prediction of path loss for the mobile radio signals is an important part in the design phase of the wireless cellular networks. In the process of modelling the path loss, the GSM 900 MHz signals are collected experimentally using Test Mobile System (TEMS) tool in the dense urban environment of Hyderabad city. In this paper, the best suited Cost 231 Hata empirical propagation model is implemented using three major dynamic neural networks namely, Focused Time Delay Neural Network (FTDNN), Distributed Time Delay Neural Network (DTDNN) which are feed forward dynamic neural networks and Layer Recurrent Neural Network (LRNN) which is a feedback dynamic neural network. The aim of these implementations is to minimise the errors between simulations and measurements. The dynamic neural networks are trained using Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms. Comparisons are made by varying the number of neurons in the hidden layer and changing the training epochs. The performance is analysed in terms of correlation with the measured data, standard deviation, mean error between the targets and outputs and computation times. From the results it is inferred that, the best correlation between simulations and measurements is 0.9972, standard deviation of error (0.04) and mean error (−5.379e-5) are least for Layer Recurrent Neural Network, trained by Levenberg method, but at the cost of increased computation time. With respect to the feed forward dynamic networks, the results show that FTDNN trained by Levenberg algorithm has a better performance compared to DTDNN.
  • Semi Deterministic Hybrid Model for Path Loss Prediction Improvement
    A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri
    Procedia Computer Science, 2016
    Mobile radio path loss modeling is an important aspect of network planning which ensures an improved quality of service to the subscribers. In this paper, the predictions made by COST 231 Walfisch-Ikegami propagation model to estimate the path loss of the mobile signals are improved by proposing a hybrid model. The modified model combines the deterministic aspects of ray tracing along with statistical processing of the empirical measurements. It includes a loss term computed for multiple reflections by using the method of images. The ten ray urban street canyon model is extended to compute reflections from multiple paths. The deterministic approach is merged with the statistical variations in the original Walfisch-Ikegami model. It consists of modeling the multi screen diffraction loss, roof to street loss and street orientation function by considering the height of the buildings, the separation and the road orientation angle as random variables with Gaussian distribution. The proposed hybrid model is validated by measurements of GSM 900MHz signals collected in Hyderabad city of Southern India. The performance of the proposed model is evaluated in terms of Prediction Error, Root Mean Square Error and other metrics. The results justify the improvement in the path loss prediction of the proposed semi deterministic hybrid model. This could be useful in link budget analysis and deployment of future cellular networks for the specified urban region and similar scenarios.
  • Path loss prediction analysis by ray tracing approach for NLOS indoor propagation
    A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri
    International Conference on Signal Processing and Communication Engineering Systems Proceedings of Spaces 2015 in Association with IEEE, 2015
    The performance of the wireless systems is significantly influenced by multiple reflections in addition to diffraction and scattering propagation effects. The geometric and dielectric properties of the obstacles vary to a large extent in the indoor environment and it is required to model these propagation effects accurately. In this paper, indoor mobile signal strengths are recorded at 2.4 GHz frequency for a wide corridor with glass partitions, in the premises of Deccan College of Engineering and Technology at Hyderabad. The data is collected within 10m from the source of the wireless router. Path loss is extracted from the measurements and comparisons are made with results derived by using two ray, four ray, six ray, and ten ray model. An N ray model is also implemented. Further a generalised ray tracing model is proposed by including diffraction and scattering effects. Diffraction losses due to the partitions are modelled using Fresnel-Kirchoff diffraction parameter and the spreading loss due to scattering is estimated using radar bi static equation. The performance of the proposed ray tracing model is evaluated by computing the error between the measurements and the proposed model. The least values of the error metrics for the proposed model indicate its accuracy in predicting the path loss for Wireless LAN mobile signals in the indoor environment.
  • Comparative analysis of mobile radio path loss models for suburban environment in Southern India
    A. Bhuvaneshwari, T. Sathyasavithri
    2013 International Conference on Emerging Trends in VLSI Embedded System Nano Electronics and Telecommunication System Icevent 2013, 2013
    Path loss models are widely used in the planning and implementation of a mobile radio system, and to evaluate the quality of service. Path loss indicates the attenuation of the radio signal as it propagates through various terrain conditions. Empirical path loss models for mobile systems are largely used in research due to their high speed, and improved accuracy. This paper estimates the path loss of mobile signals, recorded during a set of experiments performed in the areas around Osmania University at Hyderabad city in southern India. The data is collected in the frequency range of 940-950 MHz, within the coverage area of the base stations, by using suitable outdoor equipment. The field strengths obtained, are used to calculate the path loss. Least square regression analysis is performed on the measured values, and the results are compared with path loss computed from standard empirical models such as Cost-231 Hata model and Stanford University Interim (SUI) channel model. The performance of the path loss models are evaluated in terms of mean prediction error, and average relative error. Compared to Least Square analysis, the average relative error is 5.56% for SUI model, and 39.72% for Cost 231-Hata model. The lesser values of mean prediction error and relative error of the SUI model, suggests that it is more suitable for the specified environment.
  • Statistical tuning of the best suited prediction model for measurements made in hyderabad city of southern India
    Lecture Notes in Engineering and Computer Science, 2013
  • Development of an empirical power model and path loss investigations for dense urban region in Southern India
    A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri
    2013 IEEE 11th Malaysia International Conference on Communications Micc 2013, 2013
  • Design and simulation of reconfigurable antenna for cognitive radio
    B. Manimegalai, A. Bhuvaneshwari, S. Supraja
    Acwr 2011 Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief, 2011

RECENT SCHOLAR PUBLICATIONS

  • Smart Sprout – An IOT Integrated Hydroponic system
    SBMMRMADA Bhuvaneshwari.
    International Journal of Emerging Technologies and Innovative Research 11 (6 … , 2024
    2024.0
  • Dual Information Audio Watermarking with Modified Wavelet Based LSB Technique
    NAHFDA Bhuvaneshwari.
    Lecture Notes in Engineering and Computer Science 2245 (2023): 129-137., 129-137 , 2023
    2023.0
  • Dual information audio watermarking with modified wavelet based lsb technique
    N Arava, A Bhuvaneshwari, H Fathima
    Lecture Notes in Engineering and Computer Science 2245, 129-137 , 2023
    2023.0
    Citations: 1
  • Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation.
    DTSS Dr A. Bhuvaneshwari, Dr R. Hemalatha
    Advances in Decision Sciences, Image Processing, Security and Computer … , 2020
    2020.0
  • Comparison of Meta-Heuristic Algorithms for Mobile Radio Path Loss Model Optimization
    TS A. BHUVANESHWARI, R.HEMALATHA
    International Conference on Recent Innovations in Engineering and Technology … , 2019
    2019.0
  • Comparison of Meta-Heuristic Algorithms for Mobile Radio Path Loss model Optimization
    TS A. Bhuvaneshwari, R.Hemalatha
    Proceedings of 166th ISERD International Conference, Berlin, Germany, 2nd … , 2019
    2019.0
  • Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation
    SST Bhuvaneshwari Achayalingam, Hemalatha Rallapalli
    Advances in Decision Sciences, Image Processing, Security and Computer … , 2019
    2019.0
  • Path loss model optimization using stochastic hybrid genetic algorithm
    A Bhuvaneshwari, R Hemalatha, T SatyaSavithri
    International Journal of Engineering and Technology (UAE) 7, 464-469 , 2018
    2018.0
    Citations: 14
  • Path Loss Model Optimization using Stochastic Hybrid Genetic Algorithm
    TSS Bhuvaneshwari, A., R. Hemalatha
    International Journal of Engineering & Technology 7 (4.10), pp 464-469 , 2018
    2018.0
  • Development of an optimized ray tracing path loss model in the indoor environment
    A Bhuvaneshwari, R Hemalatha, T Satya Savithri
    Wireless Personal Communications 96 (1), 1039-1064 , 2017
    2017.0
    Citations: 9
  • Performance evaluation of dynamic neural networks for mobile radio path loss prediction
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2016 IEEE Uttar Pradesh Section International Conference on Electrical … , 2016
    2016.0
    Citations: 17
  • Semi deterministic hybrid model for path loss prediction improvement
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    Procedia Computer Science 92, 336-344 , 2016
    2016.0
    Citations: 35
  • Path loss prediction analysis by ray tracing approach for NLOS indoor propagation
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2015 International Conference on Signal Processing and Communication … , 2015
    2015.0
    Citations: 37
  • Statistical Validations of the Developed Empirical Power model for dense urban region
    TSS Bhuvaneshwari, A., R. Hemalatha
    International Conference on Systems Engineering, Management, and Innovation … , 2014
    2014.0
  • Development of an empirical power model and path loss investigations for dense urban region in Southern India
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2013 IEEE 11th Malaysia International Conference on Communications (MICC … , 2013
    2013.0
    Citations: 8
  • Statistical tuning of the best suited prediction model for measurements made in Hyderabad city of Southern India
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    Proceedings of the world congress on engineering and computer science 2 (7) , 2013
    2013.0
    Citations: 33
  • Path Loss Modeling and Optimisation of COST-231 Hata Prediction Model”,
    TSS Bhuvaneshwari, A., R. Hemalatha
    2nd International Conference on Innovations in Electronics and Communication … , 2013
    2013.0
  • Comparative analysis of mobile radio path loss models for suburban environment in Southern India
    A Bhuvaneshwari, T Sathyasavithri
    2013 International Conference on Emerging Trends in VLSI, Embedded System … , 2013
    2013.0
    Citations: 7
  • Modified Empirical Mobile Radio Path Loss model for Indoor Propagation
    A Bhuvaneshwari, R Hemalatha
  • COMPARATIVE ANALYSIS OF ADVANCED STATISTICAL TECHNIQUES FOR OPTIMIZATION OF HYBRID MOBILE RADIO PATH LOSS MODEL
    A Bhuvaneshwari, R Hemalatha, TS Savithri

MOST CITED SCHOLAR PUBLICATIONS

  • Path loss prediction analysis by ray tracing approach for NLOS indoor propagation
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2015 International Conference on Signal Processing and Communication … , 2015
    2015.0
    Citations: 37
  • Semi deterministic hybrid model for path loss prediction improvement
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    Procedia Computer Science 92, 336-344 , 2016
    2016.0
    Citations: 35
  • Statistical tuning of the best suited prediction model for measurements made in Hyderabad city of Southern India
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    Proceedings of the world congress on engineering and computer science 2 (7) , 2013
    2013.0
    Citations: 33
  • Performance evaluation of dynamic neural networks for mobile radio path loss prediction
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2016 IEEE Uttar Pradesh Section International Conference on Electrical … , 2016
    2016.0
    Citations: 17
  • Path loss model optimization using stochastic hybrid genetic algorithm
    A Bhuvaneshwari, R Hemalatha, T SatyaSavithri
    International Journal of Engineering and Technology (UAE) 7, 464-469 , 2018
    2018.0
    Citations: 14
  • Development of an optimized ray tracing path loss model in the indoor environment
    A Bhuvaneshwari, R Hemalatha, T Satya Savithri
    Wireless Personal Communications 96 (1), 1039-1064 , 2017
    2017.0
    Citations: 9
  • Development of an empirical power model and path loss investigations for dense urban region in Southern India
    A Bhuvaneshwari, R Hemalatha, T Satyasavithri
    2013 IEEE 11th Malaysia International Conference on Communications (MICC … , 2013
    2013.0
    Citations: 8
  • Comparative analysis of mobile radio path loss models for suburban environment in Southern India
    A Bhuvaneshwari, T Sathyasavithri
    2013 International Conference on Emerging Trends in VLSI, Embedded System … , 2013
    2013.0
    Citations: 7
  • Dual information audio watermarking with modified wavelet based lsb technique
    N Arava, A Bhuvaneshwari, H Fathima
    Lecture Notes in Engineering and Computer Science 2245, 129-137 , 2023
    2023.0
    Citations: 1
  • Smart Sprout – An IOT Integrated Hydroponic system
    SBMMRMADA Bhuvaneshwari.
    International Journal of Emerging Technologies and Innovative Research 11 (6 … , 2024
    2024.0
  • Dual Information Audio Watermarking with Modified Wavelet Based LSB Technique
    NAHFDA Bhuvaneshwari.
    Lecture Notes in Engineering and Computer Science 2245 (2023): 129-137., 129-137 , 2023
    2023.0
  • Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation.
    DTSS Dr A. Bhuvaneshwari, Dr R. Hemalatha
    Advances in Decision Sciences, Image Processing, Security and Computer … , 2020
    2020.0
  • Comparison of Meta-Heuristic Algorithms for Mobile Radio Path Loss Model Optimization
    TS A. BHUVANESHWARI, R.HEMALATHA
    International Conference on Recent Innovations in Engineering and Technology … , 2019
    2019.0
  • Comparison of Meta-Heuristic Algorithms for Mobile Radio Path Loss model Optimization
    TS A. Bhuvaneshwari, R.Hemalatha
    Proceedings of 166th ISERD International Conference, Berlin, Germany, 2nd … , 2019
    2019.0
  • Dynamic Neural Networks with Semi Empirical Model for Mobile Radio Path Loss Estimation
    SST Bhuvaneshwari Achayalingam, Hemalatha Rallapalli
    Advances in Decision Sciences, Image Processing, Security and Computer … , 2019
    2019.0
  • Path Loss Model Optimization using Stochastic Hybrid Genetic Algorithm
    TSS Bhuvaneshwari, A., R. Hemalatha
    International Journal of Engineering & Technology 7 (4.10), pp 464-469 , 2018
    2018.0
  • Statistical Validations of the Developed Empirical Power model for dense urban region
    TSS Bhuvaneshwari, A., R. Hemalatha
    International Conference on Systems Engineering, Management, and Innovation … , 2014
    2014.0
  • Path Loss Modeling and Optimisation of COST-231 Hata Prediction Model”,
    TSS Bhuvaneshwari, A., R. Hemalatha
    2nd International Conference on Innovations in Electronics and Communication … , 2013
    2013.0
  • Modified Empirical Mobile Radio Path Loss model for Indoor Propagation
    A Bhuvaneshwari, R Hemalatha
  • COMPARATIVE ANALYSIS OF ADVANCED STATISTICAL TECHNIQUES FOR OPTIMIZATION OF HYBRID MOBILE RADIO PATH LOSS MODEL
    A Bhuvaneshwari, R Hemalatha, TS Savithri