A Declarative Goal-oriented Framework for Smart Environments with LPaaS

Smart environments powered by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters (e.g. temperature, interior light) and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not consider mediating conflicting goals among different users and/or system administrators, and feature limited compatibility across different IoT verticals. In this article, we propose a declarative framework to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals encompassing multiple IoT systems. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples

Autonomic goal-oriented device management for Smart Environments

Abstract Modern Smart Environments (SmE) are equipped with a multitude of devices and sensors aimed at intelligent services. The variety of devices has raised a major problem of managing SmE. An increasingly adopted solution to the problem is the modeling of goals and intentions, and then using artificial intelligence to control the respective SmE accordingly. Generally, the solution advocates that the goals can be achieved by controlling the evolution of the states of the devices. In order to automatically reach a particular state, a sophisticated solution is required through which the respective commands, notifications and their correct sequence can be discovered and enforced on the real devices. In this paper, a comprehensive methodology is proposed by considering a ) the composite nature of the state of an individual device; b ) the possible variation of specific commands, notifications and their sequence based on the current states of the devices. The methodology works at two levels: design-time and runtime. At design-time, it constructs the extended data and control flow behavioral graphs of the devices by using the concepts of a model checking approach. Then, at runtime, it uses these graphs for finding the reliable evolution through which the desired goal can be fulfilled. The proposed methodology is implemented over the Domotic Effects framework and a home automation system, i.e. Domotic OSGi Gateway (Dog). The implementation and experimentation details indicate the effectiveness of the proposed approach