Optimizing Virtual Functions Deployment in Multi-UAV IoT Networks
In Internet of Things (IoT) networks, unmanned aerial vehicles (UAVs) play a critical role as mobile nodes that can be deployed to carry out data collection and computation. In this respect, this article considers an operator that deploys UAVs to satisfy requests from IoT applications that require v...
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Veröffentlicht in: | IEEE internet of things journal 2024-06, Vol.11 (11), p.20367-20378 |
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creator | Forghani, Athena Chin, Kwan-Wu Ros, Montserrat |
description | In Internet of Things (IoT) networks, unmanned aerial vehicles (UAVs) play a critical role as mobile nodes that can be deployed to carry out data collection and computation. In this respect, this article considers an operator that deploys UAVs to satisfy requests from IoT applications that require virtual network functions (VNFs) that may communicate with one another to be executed at different geographical locations. To this end, this article formulates a novel mixed-integer linear program (MILP) to determine the optimal assignments of UAVs and VNFs over a planning horizon that maximizes a given performance metric, e.g., revenue. It also outlines a heuristic method named MPopLoc that chooses requests according to popular requested locations and traveling cost of UAVs. The results show that MPopLoc achieved approximately 95.14% of the optimal result. |
doi_str_mv | 10.1109/JIOT.2024.3371468 |
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In this respect, this article considers an operator that deploys UAVs to satisfy requests from IoT applications that require virtual network functions (VNFs) that may communicate with one another to be executed at different geographical locations. To this end, this article formulates a novel mixed-integer linear program (MILP) to determine the optimal assignments of UAVs and VNFs over a planning horizon that maximizes a given performance metric, e.g., revenue. It also outlines a heuristic method named MPopLoc that chooses requests according to popular requested locations and traveling cost of UAVs. 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The results show that MPopLoc achieved approximately 95.14% of the optimal result.</description><subject>Algorithm</subject><subject>Autonomous aerial vehicles</subject><subject>Cloud computing</subject><subject>Containers</subject><subject>drone</subject><subject>Geographical locations</subject><subject>Heuristic methods</subject><subject>Integer programming</subject><subject>Internet of Things</subject><subject>Linear programming</subject><subject>Mixed integer</subject><subject>Monitoring</subject><subject>network design</subject><subject>Optimization</subject><subject>service</subject><subject>Unmanned aerial vehicles</subject><subject>Virtual links</subject><subject>virtual machines</subject><subject>Virtual networks</subject><subject>Virtualization</subject><issn>2327-4662</issn><issn>2327-4662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM9PwjAYhhujiQT5A0w8NPE87I-1W48ERTEol8G16brOFMc62y4G_3pH4MDpew_v837JA8A9RlOMkXh6X66LKUEknVKa4ZTnV2BEKMmSlHNyfZFvwSSEHUJowBgWfARm6y7avf2z7RfcWh971cBF3-poXRvgs-kad9ibNkLbwo--iTbZzLZw6Qr4aeKv89_hDtzUqglmcr5jsFm8FPO3ZLV-Xc5nq0TjjMdE50yxvGalUFQbQSueaYQqiojGzBBalVxXvM5xVuVGaMZLxKgmtBY0VarK6Bg8nnY77356E6Lcud63w0tJEcuJIAyRoYVPLe1dCN7UsvN2r_xBYiSPsuRRljzKkmdZA_NwYqwx5qKfspQPk_9Vt2Ta</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Forghani, Athena</creator><creator>Chin, Kwan-Wu</creator><creator>Ros, Montserrat</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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subjects | Algorithm Autonomous aerial vehicles Cloud computing Containers drone Geographical locations Heuristic methods Integer programming Internet of Things Linear programming Mixed integer Monitoring network design Optimization service Unmanned aerial vehicles Virtual links virtual machines Virtual networks Virtualization |
title | Optimizing Virtual Functions Deployment in Multi-UAV IoT Networks |
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