Browsing by Author "Talamadupula, K."

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    Conference paperPublicationMetadata only
    A knowledge driven policy framework for internet of things
    (ScitePress, 2017) Goynugur, Emre; De Mel, G.; Şensoy, Murat; Talamadupula, K.; Calo, S.; Computer Science; ŞENSOY, Murat; Goynugur, Emre
    With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it manufacturing or home-care automation. However, with the explosion of IoT deployments we have observed in recent years, manually governing the interactions between humans-to-devices—and especially devices-to- devices—is an impractical task, if not an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equip to maintain a bird’s-eye-view of the interaction space. Motivated by this observation, in this paper, we propose an end-to-end framework that (a) automatically dis- covers devices, and their associated services and capabilities w.r.t. an ontology; (b) supports representation of high-level—and expressive—user policies to govern the devices and services in the environment; (c) pro- vides efficient procedur es to refine and reason about policies to automate the management of interactions; and (d) delegates similar capable devices to fulfill the interactions, when conflicts occur. We then present our initial work in instrumenting the framework and discuss its details.
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    Policy conflict resolution in IoT via planning
    (Advances in Artificial Intelligence, 2017) Göynügür, Emre; Bernardini, S.; Mel, G. de; Talamadupula, K.; Şensoy, Murat; Computer Science; ŞENSOY, Murat; Göynügür, Emre
    With the explosion of connected devices to automate tasks, manually governing interactions among such devices—and associated services—has become an impossible task. This is because devices have their own obligations and prohibitions in context, and humans are not equipped to maintain a bird’s-eye-view of the environment. Motivated by this observation, in this paper, we present an ontology-based policy framework which can efficiently detect policy conflicts and automatically resolve such using an AI planner.