Data-driven event triggering for IoT applications
Open accessAbstract
Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.
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- Authors
- Kolios, P., Panayiotou, C., Ellinas, G., & Polycarpou, M.
- Affiliations
- University of Cyprus (CY)
- Venue
- IEEE Internet of Things Journal
- Published
- 2016-12
- Volume
- 3
- Issue
- 6
- Pages
- 1146-1158
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2345335030
- Cited by
- 60
- References
- 55
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 8 · 2022: 10 · 2021: 6 · 2020: 4 · 2019: 11 · 2018: 6 · 2017: 4 · 2016: 1
- FWCI
- 6.92
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by-sa)
- Funders
- European Research Council (FAULT-ADAPTIVE ERC-2011-AdG-291508)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Keywords
- event triggering, Internet of Things, behavioral modeling, event detection, event handling, public transport monitoring, energy efficiency, spectral efficiency
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