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Pasi Liljeberg

Researcher at University of Turku

Publications -  316
Citations -  8608

Pasi Liljeberg is an academic researcher from University of Turku. The author has contributed to research in topics: Network on a chip & Cloud computing. The author has an hindex of 40, co-authored 306 publications receiving 6959 citations. Previous affiliations of Pasi Liljeberg include Information Technology University & Turku Centre for Computer Science.

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Journal ArticleDOI

Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.
Proceedings ArticleDOI

Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

TL;DR: Electrocardiogram feature extraction is chosen as the case study as it plays an important role in diagnosis of many cardiac diseases and fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.
Proceedings ArticleDOI

Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems

TL;DR: This paper exploits the strategic position of such gateways to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., proposing thus a Smart e-Health Gateway.
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Using Ant Colony System to Consolidate VMs for Green Cloud Computing

TL;DR: The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function and outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.
Journal ArticleDOI

Internet of things for remote elderly monitoring: a study from user-centered perspective

TL;DR: This paper studies the IoT-enabled systems tackling elderly monitoring to categorize the existing approaches from a new perspective and to introduce a hierarchical model for elderly-centered monitoring.