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University Assistant

B.Tech, M.Tech, PhD

+43 1 58801 18432

last edited: 08.09.2021

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Short CV

Current working as University Assistant (Postdoc) at Distributed Systems Group, Institut für Information Systems Engineering, TU Wien (Technical University of Vienna), Vienna, Austria since July 2021. He is received his Ph. D. at Indian Institute of Technology (Indian School of Mines), Dhanbad from the Department of Computer Science and Engineering in May 2021. From July 2019 to Jan 2020, he is a visiting Ph.D. fellow at Mobile & Cloud Lab, Institute of Computer Science, Faculty of Science and Technology, University of Tartu, Estonia, under the Dora plus grant provided by the Archimedes Foundation, Estonia. He received his Master in Technology and Bachelor in Technology from the Department of Computer Science and Engineering at JNTUA, Ananthapuramu, with Distinction in 2014 and 2012.

Research Interests

Editorial Board

Reviewer

Publications

Journal Articles

  1. Kumar, S., Kumar, D., Praveen Kumar, D., & Amgoth, T. (2021). Land Subsidence Prediction using Recurrent Neural Networks. Stochastic Environmental Research and Risk Assessment , 1, 1–17. [Paper]
  2. Gutam, B. G., Praveen Kumar, D., Annavarapu, C. S. R., & Hu, Y.-C. (2021). Optimal Rendezvous Points Selection and Mobile Sink Trajectory Construction for Data Collection in WSNs. Journal of Ambient Intelligence and Humanized Computing, 1, 1–12. [Paper]
  3. Praveen Kumar, D., Satsh Narayana, S., Tarachand, A., & Chandra Sekhara Rao, A. (2021). Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digital Communications and Networks, 1–17. https://doi.org/10.1016/j.dcan.2021.10.004 [Paper]
  4. Dinesh Kumar, S., Korhan, C., Praveen Kumar, D., Venkata N., I., & Amgoth, T. (2021). EDGF: Empirical dataset generation framework for wireless network networks. Computer Communications, 180, 48–56. https://doi.org/10.1016/j.comcom.2021.08.017 [Paper]
  5. Banoth, S. P. R., Donta, P. K., & Amgoth, T. (2021). Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Q-networks. Neural Computing and Applications, 1–13. https://doi.org/10.1007/s00521-021-06146-9 [Paper]
  6. Donta, P. K., Rao, B. S. P., Amgoth, T., Annavarapu, C. S. R., & Swain, S. (2020). Data Collection and Path Determination Strategies for Mobile Sink in 3D WSNs. IEEE Sensors Journal, 20(4), 2224–2233. https://doi.org/10.1109/JSEN.2019.2949146 [Paper]
  7. Donta, P. K., Amgoth, T., & Annavarapu, C. S. R. (2020). An extended ACO-based mobile sink path determination in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 1–16. https://doi.org/10.1007/s12652-020-02595-7 [Paper]
  8. Praveen Kumar, D., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1–25. https://doi.org/10.1016/j.inffus.2018.09.013 [Paper]
  9. D., P. K., Amgoth, T., & Annavarapu, C. S. R. (2018). ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints. Applied Soft Computing, 69, 528–540. https://doi.org/10.1016/j.asoc.2018.05.008 [Paper]

Conference Articles

  1. Srinivas, M., Donta, P. K., & Amgoth, T. (2021). Mobile Charger Utility Maximization through Preemptive Scheduling for Rechargeable WSNs. In 19th OITS International Conference on Information Technology (pp. 1–6). [Paper]
  2. Srinivas, M., Donta, P. K., & Amgoth, T. (2021). Efficient Algorithms for Point and Area Sweep–Coverage in Wireless Sensor Networks. In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 315–320). https://doi.org/10.1109/WiSPNET51692.2021.9419463 [Paper]
  3. Srinivas, M., Donta, P. K., & Amgoth, T. (2020). Finding the Minimum Number of Mobile Sinks for Data Collection in Wireless Sensor Networks. In 2020 IEEE International Conference on Communication, Networks and Satellite (Comnetsat) (pp. 256–260). https://doi.org/10.1109/Comnetsat50391.2020.9328947 [Paper]
  4. Donta, P. K., Amgoth, T., & Rao Annavarapu, C. S. (2020). Congestion-aware Data Acquisition with Q-learning for Wireless Sensor Networks. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1–6). https://doi.org/10.1109/IEMTRONICS51293.2020.9216379 [Paper]
  5. Raju, C. N., Kumar, D. P., Hussenappa, D., & Kulakarne, V. (2016). Fast Three Level DNA Cryptographic Technique to Provide Better Security. In Proceedings of the International Conference on Advances in Information Communication Technology & Computing. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2979779.2979792 [Paper]

Book Chapters

  1. Sah, D. K., Kumar, D. P., Shivalingagowda, C., & Jayasree, P. V. Y. (2019). 5G applications and architectures. In 5G Enabled Secure Wireless Networks (pp. 45–68). Springer. [Preprint]
  2. Sah, D. K., Shivalingagowda, C., & Kumar, D. P. (2018). Optimization problems in wireless sensors networks. In Soft computing in wireless sensor networks (pp. 29–50). Chapman and Hall/CRC. [Preprint]