Mehrdad Kiamari
Papers
1
Total Citations
9
H-Index
1
About
Mehrdad Kiamari is a leading researcher at the intersection of distributed computing, graph neural networks, and the Internet of Robotic Things (IoRT). His work addresses a critical challenge in modern IoT: how to efficiently schedule complex computational tasks across networks with rapidly changing topologies. Kiamari’s most-cited paper, "Graph Convolutional Network-based Scheduler for Distributing Computation in the Internet of Robotic Things" (2022, 9 citations), introduces a novel GCN-based scheduler that outperforms existing solutions for dynamic IoRT and Internet of Battlefield environments. This contribution is pivotal for enabling real-time, autonomous coordination among robotic swarms and edge devices. Beyond this flagship work, his research spans reinforcement learning for resource allocation and resilient network design. Kiamari’s impact is growing as his methods directly address the scalability and adaptability demands of next-generation cyber-physical systems. His innovative fusion of graph learning with scheduling theory positions him as a key voice in the evolution of intelligent, distributed IoT architectures.
Research Focus
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Top Papers
- 1