Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems
Published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021
Recommended citation: Lelio Campanile, Marco Gribaudo, Mauro Iacono, Michele Mastroianni, "Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121899001&doi=10.1007%2f978-3-030-91825-5_21&partnerID=40&md5=5ce64fd502deef5043d5d6792b9d12ba
Cited by: 3
Abstract: Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads. © 2021, Springer Nature Switzerland AG.
Author Keywords: Edge computing; IoT; Performance evaluation; Surveillance
Bibtex citation:
@ARTICLE{Campanile2021345,
author = "Campanile, Lelio and Gribaudo, Marco and Iacono, Mauro and Mastroianni, Michele",
title = "Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems",
year = "2021",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
volume = "13104 LNCS",
pages = "345 – 359",
doi = "10.1007/978-3-030-91825-5\_21",
type = "Conference paper"
}