A behavioral reflective architecture for managing the integration of personal ubicomp systems: automatic SNMP-based discovery and management of behavior context in smart-spaces. Issue 2 (April 2016) - Moreira, Rui; Morla, Ricardo; Moreira, Luís; Soares, Christophe
Abstract Context-aware ubiquitous computing systems should be able to introspect the surrounding environment and adapt their behavior according to other existing systems and context changes. Although numerous ubiquitous computing systems have been developed that are aware of different types of context such as location, social situation, and available computational resources, few are aware of their computational behavior. Computational behavior introspection is common in reflective systems and can be used to improve the awareness and autonomy of ubicomp systems. In this paper, we propose a decentralized approach based on Simple Network Management Protocol (SNMP) and Universal Plug and Play (UPnP), and on state transition models to model and expose computational behavior. Typically, SNMP and UPnP are targeted to retrieve raw operational variables from managed network devices and consumer electronic devices, e.g., checking network interface bandwidth and automating device discovery and plug and play operations. We extend the use of these protocols by exposing the state of different ubicomp systems and associated state transitions statistics. This computational behavior may be collected locally or remotely from ubicomp systems that share a physical environment, and sent to a coordinator node or simply shared among ubicomp systems. We describe the implementation of this behavior awareness approach in a home health-care environment equipped with a VoIP Phone and a drug dispenser. We provide the means for exposing and using the behavior context in managing a simple home health-care setting. Our approach relies on a system state specification being provided by manufacturers. In the case where the specification is not provided, we show how it can be automatically discovered. We propose two machine learning approaches for automatic behavior discovery and evaluate them by determining the expected state graphs of our two systems (a VoIP Phone and a drug dispenser). These two approaches are also evaluated regarding the effectiveness of generated behavior graphs.