Paper 1: UAV-assisted Solar Energy Distribution Technique Based on Partial Brownian Movement for the Internet of Things (Target: JCN).
Paper 2: Development of Routing Protocol in UAV Network for Military Environment, (Target: ICT Express [Project])
Paper 3: LSTM Based Solar Irradiance Prediction From Remote Sensing of Meteorological Data, (Target: IEEE Geoscience and Remote Sensing Letters [Resubmission])
Paper 4: A Machine Learning-based Energy Prediction Scheme for Energy-Harvesting Base Station, ()
Paper 5: Accuracy Improvement of Solar Energy Prediction for Energy Harvesting WSN using Hybrid Deep Learning Technique[Target: ICT Express or Renewable Energy (Elsevier)]
Paper 3:
* Worked on the review comment (Provide flowchart of the proposed methodology based on reviewer comment)
* Also survey papers for the explanation of framework of photovoltaic power generation
Paper 4:
* Prepared the dataset for the simulation of energy-efficient drone base station deployment
Paper 3:
* Survey more paper for the comparison of the proposed method with the existing method
* Perform re-simulation for the comparison with the existing solution.
Paper 4:
* Start pre-processing the dataset for the simulation
* Survey papers regarding the energy-efficient drone base station deployment (for simulation).
Development of UAV routing protocol for civil and military, Anti-Drone project
Project 1:
* Worked on the energy-efficient OLSR simulation in OPNET
* Surveyed papers regarding OLSR implementation in an indoor environment
Project 2:
* Labelled the dataset for the DJI MAVIC drone
Project 1:
* Continue the simulation of energy-efficient OLSR touting on OPNET
* Start working on the indoor implementation of OLSR with raspberry-pi
Project 2
* No task assigned yet
1. Complete working on the revision of the journal of IEEE Geoscience and Remote Sensing Letters
2. Complete the winter intensive paper and submit it
* Submit at least two Journal in one year
* Attend one International Conference
* Make sure to learn as many simulation tools as possible