Report

Golamnsl

September 5, 2022 - September 11, 2022

Papers

Paper 1: Deep-Block: Blockchain-assisted Secure and Energy Efficient UAV-BS Deployment Using Deep Learning (Target: IEEE Transaction on Vehicular Technology)

Paper 2: Anti-Drone Design Techniques and Technology: Challenges, Oppurtunities, and Future Directions (Target: IEEE Journal on Selected Areas in Communications)

Paper 3: Blockchain Inspired Intruder UAV Localization Using Lightweight CNN for Internet of Battlefield Things (Target: MILCOM 2022 [Submitted])

Paper 4: Lightweight Blockchain Assisted Unauthorized UAV Access Prevention in the Internet of Military Things (Target: ICTC 2022)

50% Progress
Last Week's Progress

Paper 4:
* Finished the paper simulation work and Performance evaluation
* Complete the paper writing and fine-tune again for final version

Paper 3:
* Presented the final version to NSL memebers and professors

This Week Todo's

Paper 3:
* Submit the final version of the paper

Paper 4:
* Working on the problem formulation based on Prof. Kim suggestion
* Re-desing the system model baswed on the suggestion of professor

Project Progress

Black Ice Detection

20% Progress
Last Week's Project

Project 1:
* Had a meeting with Prof Junghyun Kim and the project memebers
* Briefly discussed about the project objective and task allocation
* Start the initial survey about the Black Ice detection tecnhonology

This Week's Project

Project 1:
* Have a meeting with Prof. Junghyun Kim about the working progress
* Continue the survey works about the Black Ice Detection technology
* Discussed the project methedology and start developing the model

Monthly Goals

* Finish working on the extension of MILCOM paper for Journal version
* Complete the ICTC paper and submit (Done)
* Finish the Intensive paper and submit by the end of this month if possible

Annual Goals

* Get accepted at least two SCI journal and submit as many as possible

* Attend as many competetive international conferences

* Make sure to learn and gain depth knowledge about the anti-drone system