UAV Networking Applications in the Internet-of-Things Era

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2994

Special Issue Editor


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Guest Editor
Defence and Systems Institute (DASI), University of South Australia (UniSA STEM), Adelaide, Australia
Interests: unmanned aerial vehicles; robotics; artificial intelligence; control systems; embedded systems; IoT; cyber-physical systems; machine learning; data driven modelling

Special Issue Information

Dear Colleagues,

Research in unmanned aerial vehicles (UAVs) has grown significantly, and it has paved the way to numerous applications in both civilian (agriculture and forest monitoring, package delivery, building and structural inspections, search-and-rescue, law enforcement) and military domains (surveillance, combat, border protection), resulting in improved productivity, safety, and efficiency. The systems can offer multiple unique benefits due to their ability to carry flexible payloads in addition to allowing flexible deployment in remote and hostile regions. Today, it is also possible to operate networked multi-UAV systems to accomplish a certain mission, e.g., to extend the connectivity of ubiquitous communication networks while facilitating large-scale data sharing.

As such, the advent of UAVs has brought connectivity to a whole new level to support Industry 4.0 and the Internet of Things (IoT) by simultaneously connecting multiple physical objects such as sensors or machines and the Internet, allowing for access to real-time data, and cyber-physical systems. The systems can facilitate better access and collaboration with people from various backgrounds living in different geographical regions. The concepts of IoT have been adopted in various areas of modern life, starting from smart grids, healthcare, intelligent transport systems, and environment monitoring.

There are, nonetheless, myriad research challenges that deserve further attention from researchers, starting from choosing the right UAV platforms, communication networks, protocols, and IoT architecture, as well as security and privacy issues. 

In this Special Issue, however, we focus on the benefits of UAVs for supporting advanced developments of the Internet of Things (IoT) with an emphasis on (but not limited to) their intelligent applications, such as for future smart cities. As such, we would like to invite researchers working in these research areas to share their findings while fostering collaboration among interdisciplinary experts, i.e., robotics and automation, machine learning and data science, communications, and signal processing as well as cyber-security and forensics, to achieve a holistic approach to the research problems.

Dr. Fendy Santoso
Guest Editor

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Keywords

  • Unmanned aerial vehicles (UAVs): design, technology, consideration, application, opportunities, and challenges in relation to the IoT
  • Formation control strategy and optimum flying paths for the IoT
  • Flying IoT networks: fundamental concepts, operating systems, architecture, communication protocols, privacy, and security
  • Smart UAVs for secure and sustainable smart cities
  • The Internet of Things (IoT): fundamental concepts, architecture, communication, privacy and security, opportunities and challenges
  • UAVs and green IoT (G-IoT) networks
  • Space–air–ground integrated G-IoT networks
  • Efficient resource allocation strategies: energy management and Ppower constraints
  • Security and privacy issues in UAV networking for IoT: architecture, reliability
  • Multiple access techniques for flying IoT
  • Large-scale flying IoT deployments
  • Open issues and challenges: smart applications, machine and deep learning (AI)-enabled for flying IoT, big data analytics for flying IoT, etc.

Published Papers (1 paper)

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Research

32 pages, 16849 KiB  
Article
Dynamic Positioning and Energy-Efficient Path Planning for Disaster Scenarios in 5G-Assisted Multi-UAV Environments
by Adil Khan, Jinling Zhang, Shabeer Ahmad, Saifullah Memon, Haroon Akhtar Qureshi and Muhammad Ishfaq
Electronics 2022, 11(14), 2197; https://doi.org/10.3390/electronics11142197 - 13 Jul 2022
Cited by 16 | Viewed by 2257
Abstract
The integration of fifth-generation (5G) and unmanned aerial vehicle (UAV) technologies has become a promising solution for providing seamless communication in applications, such as disaster management, because of its bandwidth availability, cost-efficacy, and mobile nature. The state-of-the-art research in UAV communication concentrates on [...] Read more.
The integration of fifth-generation (5G) and unmanned aerial vehicle (UAV) technologies has become a promising solution for providing seamless communication in applications, such as disaster management, because of its bandwidth availability, cost-efficacy, and mobile nature. The state-of-the-art research in UAV communication concentrates on effective positioning and path planning. Despite this, these systems performed poorly due to a lack of dynamic control and external factors, such as weather. The solution presented in this paper addresses the problems listed above by using dynamic positioning and energy-efficient path planning for disaster scenarios in the 5G-assisted multi-UAV environments (Dynamic-UAV) to maximize the performance metrics. The lightweight gated recurrent unit (LGRU) is used for weather and event prediction to determine the disaster and non-disaster area and the context of the disaster. The density-based optics clustering (DBOC) algorithm is used to achieve reliability during communication with cluster IoT devices in disaster and non-disaster regions. The satellite determines the number of UAVs required and positions the UAVs using the dynamic positioning-based soft actor–critic (DPSAC) algorithm to achieve maximum throughput. Moreover, the UAVs’ path planning is performed using the shuffled shepherd optimization with dynamic-window method (SSO-DWM) to reduce energy consumption. The proposed approach is simulated using the NS 3.26 simulator and validated by comparing the results with existing techniques regarding the quality of service (QoS), reliability, and energy efficiency. Experimental results indicate that the proposed method achieved maximum throughput (1.59 bit/s), packet delivery ratio (0.88), coverage probability (0.82), number of collected packets (7109–5875), energy efficiency (1.544), minimum delay (16.4 ms–18.5 ms), and energy consumption (7.48 KJ). Full article
(This article belongs to the Special Issue UAV Networking Applications in the Internet-of-Things Era)
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