Abstract

The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole “things” aspect to the omnipotent role of “intelligent connection of things”. Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: (1) scalability requiring a scalable network architecture with ubiquitous coverage, (2) intelligence requiring a global computing plane enabling intelligent things, (3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs’s mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

Keywords

• Unmanned aerial vehicles
• Internet of everything
• Internet of things
• Edge intelligence
• Multi-UAV Ad Hoc networks
• Trajectory optimization

Bibtex

@article{LIU202066,
	title = "Unmanned aerial vehicle for internet of everything: Opportunities and challenges",
	journal = "Computer Communications",
	volume = "155",
	pages = "66 - 83",
	year = "2020",
	issn = "0140-3664",
	doi = "https://doi.org/10.1016/j.comcom.2020.03.017",
	url = "http://www.sciencedirect.com/science/article/pii/S0140366419318754",
	author = "Yalin Liu and Hong-Ning Dai and Qubeijian Wang and Mahendra K. Shukla and Muhammad Imran",
}