Scheduling Services in Multi-UAVs IoT Networks

This article considers an operator that deploys unmanned aerial vehicles (UAVs) to service requests from users/devices in an Internet of Things (IoT) network; each request requires one or more so-called services, such as a database or an Artificial Intelligence (AI) model. A key problem faced by the operator is to determine which services to place on UAVs and which requests to transport to a base location for execution; note, UAVs have resource constraints and hence, they are unable to carry all services or/and serve all users in one trajectory. Further, they do not have connectivity to a base station during flight. To this end, given a set of requests, this article presents novel solutions that minimize the total completion time of these requests. First, it outlines a novel mixed-integer linear program (MILP) that can be used to calculate the optimal assignment of services to trajectories. MILP also decides which requests are transported to a base location for computation, and schedules the return of result(s) to users. The second solution is a heuristic method named MinTime that chooses UAVs and users according to traveling and task execution time. The results show MinTime achieved 91.15% of the optimal result in terms of request completion time.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-04-01
DOI
https://doi.org/10.1109/jiot.2025.3556653
Citations
2
Primary Topic
Cybersecurity and Information Systems
Type
article
Field-Weighted Citation Impact
2.97
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article

Scheduling Services in Multi-UAVs IoT Networks

Athena Forghani, Kwan‐Wu Chin, Montserrat Ros
2 citations
IEEE Internet of Things Journal
Cybersecurity and Information Systems
2.97
article

Scheduling Services in Multi-UAVs IoT Networks

Athena Forghani, Kwan‐Wu Chin, Montserrat Ros
article en
2 citations

Abstract

This article considers an operator that deploys unmanned aerial vehicles (UAVs) to service requests from users/devices in an Internet of Things (IoT) network; each request requires one or more so-called services, such as a database or an Artificial Intelligence (AI) model. A key problem faced by the operator is to determine which services to place on UAVs and which requests to transport to a base location for execution; note, UAVs have resource constraints and hence, they are unable to carry all services or/and serve all users in one trajectory. Further, they do not have connectivity to a base station during flight. To this end, given a set of requests, this article presents novel solutions that minimize the total completion time of these requests. First, it outlines a novel mixed-integer linear program (MILP) that can be used to calculate the optimal assignment of services to trajectories. MILP also decides which requests are transported to a base location for computation, and schedules the return of result(s) to users. The second solution is a heuristic method named MinTime that chooses UAVs and users according to traveling and task execution time. The results show MinTime achieved 91.15% of the optimal result in terms of request completion time.

IEEE Internet of Things JournalVol. 12(14)
University of Wollongong (AU)
Openalex Percentile: Top 10%
Cybersecurity and Information Systems
2.97
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Scheduling Services in Multi-UAVs IoT Networks — Athena Forghani, Kwan‐Wu Chin, et al. · IEEE Internet of Things Journal (2025) | TGRS Research Map | TGRS