No abstract

When a self-adaptive system detects that its adaptation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals.

Flooding is a critical global problem, which is growing more severe due to the effects of climate change. This problem is particularly acute in the state of São Paulo, Brazil, where flooding during the rainy season incurs significant financial and human costs. Another critical problem associated with flooding is the high level of pollution present

Considerable research has been performed in applying run-time reconfigurable component models to the domain of wireless sensor networks. The ability to dynamically deploy and reconfigure software components has clear advantages in sensor networks, which are typically large in scale and expected to operate for long periods in the face of node mobility, dynamic environmental conditions, and changing application requirements. LooCI is a component and binding model that is optimized for use in resource-constrained environments such as Wireless Sensor Networks. LooCI components use a novel event-based binding model that allows developers to model rich component interactions, while providing support for run-time reconfiguration, reflection, and policy-based management. This paper reports on the design of LooCI and describes a prototype implementation for the Sun SPOT. This platform is then evaluated in context of a real-world river monitoring and warning scenario in the city of São Carlos, Brazil.

No abstract