A Prediction-Driven Adaptation Approach for Self-Adaptive Sensor Networks

Ivan Dario Paez Anaya (1) , Viliam Simko (2) , Johann Bourcier (1) , Noël Plouzeau (1) , Jean-Marc Jézéquel (1)

1 DiverSe - Diversity-centric Software Engineering
2 KIT - Karlsruhe Institute of Technology

Engineering self-adaptive software in unpredictable environments such as pervasive systems, where network's ability, remaining battery power and environmental conditions may vary over the lifetime of the system is a very challenging task. Many current software engineering approaches leverage run-time architectural models to ease the design of the autonomic control loop of these self-adaptive systems. While these approaches perform well in reacting to various evolutions of the runtime environment, implementations based on reactive paradigms have a limited ability to anticipate problems, leading to transient unavailability of the system, useless costly adaptations, or resources waste. In this paper, we follow a proactive self-adaptation approach that aims at overcoming the limitation of reactive approaches. Based on predictive analysis of internal and external context information, our approach regulates new architecture recon figurations and deploys them using models at runtime. We have evaluated our approach on a case study where we combined hourly temperature readings provided by National Climatic Data Center (NCDC) with re reports from Moderate Resolution Imaging Spectroradiometer (MODIS) and simulated the behavior of multiple systems. The results confirm that our proactive approach outperforms a typical reactive system in scenarios with seasonal behavior.

Domaines: Génie logiciel [cs.SE]

Ivan Dario Paez Anaya, Viliam Simko, Johann Bourcier, Noël Plouzeau, Jean-Marc Jézéquel. A Prediction-Driven Adaptation Approach for Self-Adaptive Sensor Networks. 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, IEEE/ACM, Jun 2014, Hyderabad, India. ⟨hal-00983046⟩