Dynamically Allocating Network Resources for Teleoperation Applications using Intent-based Inference
Perustieteiden korkeakoulu | Master's thesis
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Master's Programme in ICT Innovation
AbstractDue to the limitation of network resource capacity, maintaining high-quality communication for large-scale teleoperation applications, in terms of ultra-low latency and ultra-reliability requirements, is challenging. Most network infrastructures are not designed for such extreme demands compared to other applications. However, if the future behavior of the teleoperation application is captured, we can allocate network resources more efficiently. Using the intent-based inference mechanism to predict future behaviors, the goal of this research is to deliver a low latency environment while guaranteeing high-reliability requirements. Considering a group of remote-controlled robots working in an industrial environment, the intent is interpreted as the target destination to which the robot is heading. This research proposes a Recurrent Neural Network (RNN) solution for network systems, which predicts the target destination and allocates network resources based on the predicted result. The objective is to minimize the unproductive time of robots, which is the duration robots stay idle. To quantify the performance, the proposed solution is tested under a comprehensive simulator that allows multiple actors and data streams to work simultaneously. Results show that the RNN integration for robotic applications can significantly improve network performance under different scenarios. However, care should be taken when applying since not all network systems benefit positively and equally from proposed solutions.
SupervisorLe, Viet Duc
Thesis advisorElbamby, Mohammed
robot destination prediction, recurrent neural network, scheduling and matching, network simulation