An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things (2022)
Journal Article
Rahmani, A. M., Ali, S., Malik, M. H., Yousefpoor, E., Yousefpoor, M. S., Mousavi, A., …Hosseinzadeh, M. (2022). An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things. Scientific Reports, 12(1), https://doi.org/10.1038/s41598-022-12181-w

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as th... Read More about An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things.

E-learning development based on internet of things and blockchain technology during Covid-19 Pandemic (2021)
Journal Article
Rahmani, A. M., Naqvi, R., Malik, M., Malik, T. S., Sadrishojaei, M., Hosseinzadeh, M., & Al-Musawi, A. (2021). E-learning development based on internet of things and blockchain technology during Covid-19 Pandemic. Mathematics, 9(24), 3151. https://doi.org/10.3390/math9243151

The suspension of institutions around the world in early 2020 due to the COVID-19 virus did not stop the learning process. E-learning concepts and digital technologies enable students to learn from a safe distance while continuing their educational p... Read More about E-learning development based on internet of things and blockchain technology during Covid-19 Pandemic.

Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach (2021)
Journal Article
Shahidinejad, A., Farahbakhsh, F., Ghobaei-Arani, M., Malik, M. H., & Anwar, T. (2021). Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach. Journal of Grid Computing, 19(2), https://doi.org/10.1007/s10723-021-09559-x

Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the exist... Read More about Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach.