| Title | Graph Convolutional Network-based Scheduler for Distributing Computation in the Internet of Robotic Things |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Coleman, Jared, Kiamari, Mehrdad, Clark, Lillian, D'Souza, Daniel, Krishnamachari, Bhaskar |
| Conference Name | MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) |
| Date Published | nov |
| Keywords | GCN, graph convolutional network, human factors, Internet of Things, iobt, machine learning, machine learning algorithms, military communication, Network topology, Processor scheduling, pubcrawl, resilience, Resiliency, Robotics, Scalability, scheduling |
| Abstract | Existing solutions for scheduling arbitrarily complex distributed applications on networks of computational nodes are insufficient for scenarios where the network topology is changing rapidly. New Internet of Things (IoT) domains like the Internet of Robotic Things (IoRT) and the Internet of Battlefield Things (IoBT) demand solutions that are robust and efficient in environments that experience constant and/or rapid change. In this paper, we demonstrate how recent advancements in machine learning (in particular, in graph convolutional neural networks) can be leveraged to solve the task scheduling problem with decent performance and in much less time than traditional algorithms. |
| DOI | 10.1109/MILCOM55135.2022.10017673 |
| Citation Key | coleman_graph_2022 |
Graph Convolutional Network-based Scheduler for Distributing Computation in the Internet of Robotic Things