SEAMS 2024
Mon 15 - Tue 16 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Smart Cyber-Physical Systems (sCPS) operate in dynamic and uncertain environments, where anticipation to adverse situations through effective decision-making is crucial and decentralization is often necessary due to scalability, resilience, and effiency reasons. Addressing the limitations related to the lack of foresight of (decentralized) reactive self-adaptation (e.g., slower response, sub-optimal resource usage), this paper introduces a novel method that employs Predictive Coordinate Descent (PCD) to enable decentralized proactive self-adaptation in sCPS. Our study compares our proactive PCD approach with a reactive Deep Q-Network (DQN)-based strategy on Unmanned Aerial Vehicles (UAVs) in wildfire tracking adapta- tion scenarios. Results demonstrate the effectiveness of PCD, which outperforms DQN both under standard operational conditions, as well as in challenging scenarios with limited observability of the environment.

Mon 15 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
Session 3: Unmanned Aerial Vehicles and LLMs Research Track / Artifact Track at Luis de Freitas Branco
Chair(s): Gabriel A. Moreno Carnegie Mellon University Software Engineering Institute
14:00
25m
Talk
ADAM: Adaptive Monitoring of Runtime Anomalies in Small Uncrewed Aerial SystemsFULL
Research Track
Md Nafee Al Islam University of Notre Dame, Jane Cleland-Huang University of Notre Dame, Michael Vierhauser University of Innsbruck
14:25
15m
Talk
Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire TrackingSHORT
Research Track
Enrique Vilchez University of Malaga, Javier Troya Universidad de Málaga, Spain, Javier Camara University of Málaga
14:40
15m
Talk
Wildfire-UAVSim: An Exemplar for Evaluation of Adaptive Cyber-Physical Systems in Partially-Observable EnvironmentsARTIFACT
Artifact Track
Enrique Vilchez University of Malaga, Javier Troya Universidad de Málaga, Spain, Javier Camara University of Málaga
14:55
15m
Talk
Aloft: Self-Adaptive Drone Controller TestbedARTIFACT
Artifact Track
Calum Imrie University of York, Rhys Howard University of Oxford, Divya Thuremella University of Oxford, Nawshin Mannan Proma University of York, Tejas Pandey University of York, Paulina Lewinska University of York, Ricardo Cannizzaro University of Oxford, Richard Hawkins University of York, Colin Paterson University of York, Lars Kunze University of Oxford, Victoria J. Hodge University of York
15:10
15m
Talk
Exploring the Potential of Large Language Models in Self-adaptive SystemsSHORT
Research Track
Jialong Li Waseda University, Japan, Mingyue Zhang Southwest University, NIANYU LI ZGC Lab, China, Danny Weyns KU Leuven, Zhi Jin Peking University, Kenji Tei Waseda University