@inproceedings{2476, author = {{Shiferaw Heyi, Binyam and Karl, Holger}}, publisher = {{Proc. of IEEE Wireless Communications and Networking Conference (WCNC)}}, title = {{{Modelling Time-Limited Capacity of a Wireless Channel as aMarkov Reward Process}}}, year = {{2018}}, } @inproceedings{3217, author = {{Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel and Karl, Holger}}, title = {{{DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling}}}, doi = {{10.1109/LCSYS.2018.2847721}}, year = {{2018}}, } @article{15741, abstract = {{ In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario}}, author = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, title = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}}, doi = {{10.1016/j.automatica.2019.108759}}, year = {{2019}}, } @inproceedings{30793, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 14th International Conference on Agents and Artificial Intelligence}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}}}, doi = {{10.5220/0010845400003116}}, year = {{2022}}, }