Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems
Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems
Venue & metadata
- Journal/Proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume: 13104 LNCS
- Pages: 345 – 359
- Note: Cited by: 3
- Author keywords: Edge computing; IoT; Performance evaluation; Surveillance
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.
Keywords
Edge computing GS Electric batteries GS Internet of things GS Low power electronics GS Network security GS Security systems GS ’current GS Battery powered GS Computing-task GS Edge computing GS Energy requirements GS Hybrid simulation GS Lower energies GS Monitoring system GS Performances evaluation GS Surveillance systems GS Monitoring GS
Links & artifacts
Suggested citation
Campanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2021). Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13104 LNCS, 345–359. https://doi.org/10.1007/978-3-030-91825-5_21