Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire Tracking
Pages 56 - 62
Abstract
Smart Cyber-Physical Systems (sCPS) operate in dynamic and uncertain environments, where anticipation to adverse situations is crucial and decentralization is often necessary due to e.g., scalability issues. 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 PCD with a reactive Deep Q-Network (DQN) strategy on Unmanned Aerial Vehicles (UAV) in wildfire tracking adaptation scenarios. Results show how PCD outperforms DQN when furnished with high-quality predictions of the environment, but progressively degrades in effectiveness with predictions of decreasing quality.
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Index Terms
- Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire Tracking
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Published In
April 2024
233 pages
ISBN:9798400705854
DOI:10.1145/3643915
- Chair:
- Luciano Baresi,
- Program Chair:
- Xiaoxing Ma,
- Program Co-chair:
- Liliana Pasquale
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Publication History
Published: 07 June 2024
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- Research-article
Funding Sources
- Spanish Government (FEDER, Ministerio de Ciencia e Innovación?Agencia Estatal de Investigación)
- Spanish Government (Ministerio de Ciencia e Innovación?Agencia Estatal de Investigación)
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SEAMS '24
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SEAMS '24: 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
April 15 - 16, 2024
AA, Lisbon, Portugal
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Overall Acceptance Rate 17 of 31 submissions, 55%
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