University of Oulu

T. Anagnostopoulos et al., "Challenges and Solutions of Surveillance Systems in IoT-Enabled Smart Campus: A Survey," in IEEE Access, vol. 9, pp. 131926-131954, 2021, doi: 10.1109/ACCESS.2021.3114447

Challenges and solutions of surveillance systems in IoT-enabled smart campus : a survey

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Author: Anagnostopoulos, Theodoros1; Kostakos, Panos2; Zaslavsky, Arkady3;
Organizations: 1Department of Business Administration, University of West Attica, 12243 Aigaleo, Greece
2Center for Ubiquitous Computing, University of Oulu, 90570 Oulu, Finland
3School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
4Department of Informatics and Computer Engineering, University of West Attica, 12243 Aigaleo, Greece
5Ordnance Survey, Southampton SO15 2AA, U.K.
6Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, U.K.
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-11-05


A Smart Campus is a miniature of a Smart City with a more demanding framework that enables learning, social interaction and creativity. To ensure a Smart Campus uninterruptible secure operation, a key requirement is that daily routines and activities are performed protected in an environment monitored unobtrusively by a robust surveillance system. The various components that compose such an environment, buildings, labs, public spaces, smart lighting, smart parking, or even smart traffic lights, require us to focus on surveillance systems, and recognize which detection activities to establish. In this paper, we perform a comparative assessment in the area of surveillance systems for Smart Campuses. A proposed taxonomy for IoT-enabled Smart Campus unfold five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, we first present the state-of-the-art, and then we make a comparative assessment and classify the systems. We extract valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by surveillance systems for Smart Campus.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 9
Pages: 131926 - 131954
DOI: 10.1109/ACCESS.2021.3114447
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the Course of Advanced Quantitative Statistical Analyses, Master of Business Administration (MBA), Department of Business Administration, University of West Attica, Athens, Greece, in part by the Academy of Finland 6 Genesis Flagship under Grant 318927, and in part by the EU Horizon 2020 Projects CUTLER: Coastal Urban developmenT through the LEnses of Resiliency under Grant 770469 and IDUNN Cognitive Detection System for Cybersecure Operational Technologies under Grant 101021911.
EU Grant Number: (770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency
(101021911) IDUNN - A Cognitive Detection System for Cybersecure Operational Technologies
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © The Authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see