Dongare, Sumedh Jitendra ; Jovovich, Aleksandar ; De Sombre, Wanja ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja (2024)
Minimizing the Age of Incorrect Information for Status Update Systems with Energy Harvesting.
59th IEEE Internartional Conference on Communications (ICC'24). Denver, USA (09.06.2024 - 13.06.2024)
doi: 10.1109/ICC51166.2024.10622719
Conference or Workshop Item, Bibliographie
Abstract
Status Update Systems (SUSs) are central components in applications like environmental sensing or smart cities. They consist of a sender monitoring a remote process and sending the sensed information to a receiver. The sender aims to deliver fresh information about the monitored process's state to allow the receiver to timely respond to the process's changes. In SUSs, the sender is usually battery operated. Therefore, to increase the available energy we consider Energy Harvesting (EH). Moreover, as at the receiver the information transmitted by the sender is only relevant when the process's state changes, we measure the information's freshness using Age of Incorrect Information (AoII). Finding the optimal transmission strategy at the sender that minimizes the AoII requires perfect system knowledge, i.e., the behavior of the monitored process, the channel quality, and the available energy. However, in real applications this knowledge is usually not available. To overcome this challenge, we first establish the optimality of threshold-based policies for AoII minimization in SUSs with EH capabilities by proving that there exists an AoII value depending on the observed state of the monitored process, the battery level and the receiver's estimation of the monitored process's state beyond which transmitting is preferable over idling. Next, we exploit the threshold-based policies' structure and deploy a learning algorithm based on Finite-Difference Policy Gradient (FDPG). Our proposed approach finds the AoII thresholds without requiring perfect system knowledge. Simulations show that our approach outperforms reference algorithms by at least 20% and efficiently learns near-optimal policies for AoII minimization.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2024 |
| Creators: | Dongare, Sumedh Jitendra ; Jovovich, Aleksandar ; De Sombre, Wanja ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja |
| Type of entry: | Bibliographie |
| Title: | Minimizing the Age of Incorrect Information for Status Update Systems with Energy Harvesting |
| Language: | English |
| Date: | 20 August 2024 |
| Publisher: | IEEE |
| Book Title: | ICC 2024 - IEEE International Conference on Communications |
| Event Title: | 59th IEEE Internartional Conference on Communications (ICC'24) |
| Event Location: | Denver, USA |
| Event Dates: | 09.06.2024 - 13.06.2024 |
| DOI: | 10.1109/ICC51166.2024.10622719 |
| Abstract: | Status Update Systems (SUSs) are central components in applications like environmental sensing or smart cities. They consist of a sender monitoring a remote process and sending the sensed information to a receiver. The sender aims to deliver fresh information about the monitored process's state to allow the receiver to timely respond to the process's changes. In SUSs, the sender is usually battery operated. Therefore, to increase the available energy we consider Energy Harvesting (EH). Moreover, as at the receiver the information transmitted by the sender is only relevant when the process's state changes, we measure the information's freshness using Age of Incorrect Information (AoII). Finding the optimal transmission strategy at the sender that minimizes the AoII requires perfect system knowledge, i.e., the behavior of the monitored process, the channel quality, and the available energy. However, in real applications this knowledge is usually not available. To overcome this challenge, we first establish the optimality of threshold-based policies for AoII minimization in SUSs with EH capabilities by proving that there exists an AoII value depending on the observed state of the monitored process, the battery level and the receiver's estimation of the monitored process's state beyond which transmitting is preferable over idling. Next, we exploit the threshold-based policies' structure and deploy a learning algorithm based on Finite-Difference Policy Gradient (FDPG). Our proposed approach finds the AoII thresholds without requiring perfect system knowledge. Simulations show that our approach outperforms reference algorithms by at least 20% and efficiently learns near-optimal policies for AoII minimization. |
| Uncontrolled Keywords: | BMBF Open6GHub, DAAD, emergenCITY, emergenCITY_KOM |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering DFG-Collaborative Research Centres (incl. Transregio) DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C1: Network-centred perspective |
| Date Deposited: | 25 Oct 2024 11:53 |
| Last Modified: | 25 Oct 2024 11:53 |
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