Multi-Node Allocation Heuristics for IoT Applications

The rapid and increasing technological advancement highlights the importance of the Internet of Things (IoT) in the digital transformation era, establishing an intelligent network that efficiently connects devices. In IoT networks, the nodes devices have heterogeneous configurations, covering variou...

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Bibliographic Details
Published inBrazilian Symposium on Computing System Engineering pp. 1 - 6
Main Authors Taparo, Murilo, Dantas, Bianca, Santos, Ricardo
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.11.2024
Subjects
Bandwidth
Complexity theory
Digital transformation
edge computing
Energy consumption
greedy heuristic
heterogeneous configurations
Heuristic algorithms
ILP
Intelligent networks
Internet of Things
multi-objective optimization
node selection
Resource management
resources allocation
Systems engineering and theory
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ISSN2324-7894
DOI10.1109/SBESC65055.2024.10771906

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Summary:The rapid and increasing technological advancement highlights the importance of the Internet of Things (IoT) in the digital transformation era, establishing an intelligent network that efficiently connects devices. In IoT networks, the nodes devices have heterogeneous configurations, covering various characteristics, such as processing and storage capacity, network bandwidth, and energy consumption. This complexity of configurations makes it a significant challenge for users interested in allocating the devices according to the applications' demands. This paper presents a Multi-Node greedy heuristic (MNA-IoT) for allocating node resources to meet application demands in IoT systems. The proposed heuristic considers that the applications have multiple objectives, and it is applied to scenarios in which application requirements may exceed the capabilities of any individual node in the IoT network. This work presents scenarios and comparisons with other heuristics: a single-node allocation heuristic, gamultobj (a genetic-based algorithm), and paretosearch (direct search-based in the solutions space). Our results show that MNA-IoT can achieve the same or even better solutions than the heuristics and attains a speedup of around 1815 and 21461 compared to gamultobj and paretosearch.
ISSN:2324-7894
DOI:10.1109/SBESC65055.2024.10771906