Nahina Islam completed her PhD from RMIT University in 2017, her Masters in Electronic Engineering from Latrobe University in 2012, and her Bachelors degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) in 2009. Her PhD research topic was ‘Energy Efficient Wireless Communication’, proposing energy-efficient algorithms for terrestrial networks and aerial base stations (UAVs). She has proposed a reinforcement learning-based (i.e., a Markov decision process) decision-making algorithm for energy efficient systems. This algorithm can be applied to different applications, such as the design of energy efficient base stations in terrestrial and aerial communication networks and energy-efficient and delay-aware handover decisions in LTE-A heterogeneous networks. Currently, her research work is focused on applying deep learning in IoT-based smart farming and smart environmental management.
Research Keywords & Expertise
Smart Cities
IoT
smart farming
Green Communications
machine learning
Machine Learning and a...
IoT applications
Fingerprints
28%
smart farming
17%
machine learning
17%
Machine Learning and artificial intelligence
12%
IoT
Short Biography
Nahina Islam completed her PhD from RMIT University in 2017, her Masters in Electronic Engineering from Latrobe University in 2012, and her Bachelors degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) in 2009. Her PhD research topic was ‘Energy Efficient Wireless Communication’, proposing energy-efficient algorithms for terrestrial networks and aerial base stations (UAVs). She has proposed a reinforcement learning-based (i.e., a Markov decision process) decision-making algorithm for energy efficient systems. This algorithm can be applied to different applications, such as the design of energy efficient base stations in terrestrial and aerial communication networks and energy-efficient and delay-aware handover decisions in LTE-A heterogeneous networks. Currently, her research work is focused on applying deep learning in IoT-based smart farming and smart environmental management.