Browsing by Author "Göynügür, Emre"

Now showing 1 - 4 of 4

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Combining semantic web and IoT to reason with health and safety policies
(IEEE, 2018-01-12) Göynügür, Emre; Şensoy, Murat; Mel, G. de; Computer Science; ŞENSOY, Murat; Göynügür, Emre
Monitoring and following health and safety regulations are especially important - but made difficult - in hazardous work environments such as underground mines to prevent work place accidents and illnesses. Even though there are IoT solutions for health and safety, every work place has different characteristics and monitoring is typically done by humans in control rooms. During emergencies, conflicts may arise among prohibitions and obligations, and humans may not be better placed to make decision without any assistance as they do not have a bird's-eye-view of the environment. Motivated by this observations, in this paper, we discuss how health and safety regulations can be implemented using a semantic policy framework. We then show how this framework can be integrated into an in-use smart underground mine solution. We also evaluate the performance of our framework to show that it can cope with the complexity and the amount of data generated by the system.
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Enabling smart environments through scalable policy reasoning and Internet of Things
(Wiley, 2019-04) Göynügür, Emre; Şensoy, Murat; de Mel, G.; Computer Science; ŞENSOY, Murat; Göynügür, Emre
In this paper, we discuss how to combine ontology-based policy reasoning mechanisms with in-use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that it is scalable under high load of data and devices.
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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.
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A semantic policy framework for internet of things
(2018-10-31) Göynügür, Emre; Şensoy, Murat; Şensoy, Murat; Tekin, Ahmet; Sözer, Hasan; Alkaya, A. F.; Özgür, A.; Department of Computer Science; Göynügür, 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 environments. However, with the explosion of IoT deployments we have observed in recent years, manually managing 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 equipped to maintain a bird's-eye-view of the interaction space. Motivated by this observation, in this thesis, we propose a semantic policy framework that (a) supports representation of high-level and expressive user policies to govern the devices and services in the environment; (b) provides e cient procedures to re ne and reason about policies to automate the management of interactions; and (c) delegates similar capable devices to ful ll the interactions, when con icts occur. We then describe how to combine ontology-based policy reasoning mechanisms with in-use IoT applications to customize and automate device behaviors and discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that our approach is scalable under high load of data and devices.