MURAL - Maynooth University Research Archive Library



    Stochastic-Geometry-Based Analysis of Multipurpose UAVs for Package and Data Delivery


    Qin, Yujie, Kishk, Mustafa A. and Alouini, Mohamed-Slim (2023) Stochastic-Geometry-Based Analysis of Multipurpose UAVs for Package and Data Delivery. IEEE Internet of Things Journal, 10 (5). pp. 4664-4676. ISSN 2372-2541

    [thumbnail of MK_Stochastic.pdf]
    Preview
    Text
    MK_Stochastic.pdf

    Download (2MB) | Preview
    Official URL: https://doi.org/10.1109/JIOT.2022.3218674

    Abstract

    Using drones for communications and transportation is drawing great attention in many practical scenarios, such as package delivery and providing additional wireless coverage. However, the increasing demand for unmanned aerial vehicles (UAVs) from industry and academia will cause aerial traffic conflicts in the future. This, in turn, motivates the idea of this article: multipurpose UAVs, acting as aerial wireless data relays and means of aerial transportation simultaneously, to deliver packages and data at the same time. This article aims to analyze the feasibility of using drones to collect and deliver data from the Internet of Things (IoT) devices to terrestrial base stations (TBSs) while delivering packages from warehouses to residential areas. We propose an algorithm to optimize the trajectory of UAVs to maximize the size of collected/delivered data while minimizing the total round trip time subject to the limited onboard battery of UAVs. Specifically, we use tools from stochastic geometry to model the locations of the IoT clusters and the TBSs and study the system performance with respect to energy efficiency, average size of collected/delivered data, and package delivery time. Our numerical results reveal that multifunctional UAVs have great potential to enhance the efficiency of both communication and transportation networks.
    Item Type: Article
    Keywords: Data collection; Internet of Things; IoT; devices, multipurpose unmanned aerial vehicle; UAV; package delivery; Poisson point process; stochastic geometry;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 19396
    Identification Number: 10.1109/JIOT.2022.3218674
    Depositing User: Mustafa Kishk
    Date Deposited: 21 Jan 2025 16:42
    Journal or Publication Title: IEEE Internet of Things Journal
    Refereed: Yes
    Related URLs:
    URI: https://mural.maynoothuniversity.ie/id/eprint/19396
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only (login required)

    Item control page
    Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads