Browsing by Subject "Internet of Things"

Now showing items 1-6 of 6

    • Approximate Semantic Matching of Events for the Internet of Things 

      Hasan, Souleiman; Curry, Edward (ACM, 2014-07-01)
      Event processing follows a decoupled model of interaction in space, time, and synchronization. However,another dimension of semantic coupling also exists and poses a challenge to the scalability of event processing systems ...

    • De-camouflaging chameleons: Requiring transparency for consumer protection in the Internet of Things 

      Kennedy, Rónán (Queen's University Belfast, School of Law, 2019)
      Information and communications technology (ICT) and the development of the so-called ‘Internet of Things’ (IoT) provide new and valuable affordances to businesses and consumers. The use of sensors, software, and interconnectivity ...

    • Entity summarisation for entity-centric publish/subscribe systems 

      Pavlopoulou, Niki (NUI Galway, 2021-12-21)
      The Internet of Things (IoT) has contributed to physical devices generating entity-centric data (e.g. smart buildings). To bridge the gap between the devices’ data and the users’ interests, Publish/Subscribe systems (Pub/Sub) ...

    • A scalable spatio-temporal query processing engine for linked sensor data 

      Nguyen, Hoan Mau Quoc (NUI Galway, 2020-02-25)
      The ever-increasing amount of Internet of Things (IoT) data emanating from sensors and mobile devices is creating new capabilities and unprecedented economic opportunity for individuals, organizations, and states. To fully ...

    • Thematic Event Processing 

      Hasan, Souleiman; Curry, Edward (ACM, 2014-12-08)
      Event-based systems follow a decoupled mode of interaction between event producers and consumers in space, time, and synchronization to enable scalability within distributed systems. We recognize a fourth dimension of ...

    • Toward distributed, global, deep learning using IoT devices 

      Sudharsan, Bharath; Patel, Pankesh; Breslin, John; Ali, Muhammad Intizar; Mitra, Karan; Dustdar, Schahram; Rana, Omer; Jayaraman, Prem Prakash; Ranjan, Rajiv (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)
      Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...