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Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges

Yalin Liu, Hong-Ning Dai, Qubeijian Wang, Mahendra K. Shukla, Muhammad Imran

arXiv:2003.13311v2cs.NIeess.SP

TL;DR

IoE seeks intelligent, ubiquitous connections but remains limited by coverage, energy, computing, and security constraints. This survey reviews IoE expectations and enabling technologies, UAV capabilities, and a UAV-enabled IoE solution. It concludes that UAV integration can enhance IoE scalability, intelligence, and application diversity while leaving resource allocation, security, autonomy, interoperability, and coordination as open issues.

  • Problem

    IoE realization is constrained by restricted coverage and limited network, battery, computing, and security resources despite its goal of intelligent, ubiquitous connections.

  • Method

    The paper surveys IoE expectations and enabling technologies, reviews UAV technologies and opportunities, and integrates UAVs with ICT technologies into a Ue-IoE solution.

  • Results

    The survey concludes that Ue-IoE can enhance IoE scalability, intelligence, and diversity through UAV mobility and flexible deployment.

  • Takeaways & Limitations

    UAV-enabled IoE offers extended coverage, on-demand aerial intelligence, self-maintenance, power supply, sensor recycling, and support for diverse applications.

  • Takeaways & Limitations

    Ue-IoE still requires solutions for restricted-resource allocation, UAV security, lightweight autonomous mobility, heterogeneous applications, and coordination among computing facilities.

Abstract

from arXiv · show

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.

I. INTRODUCTION

IoE extends IoT toward intelligent connections among people, data, processes, and things, but its realization remains constrained by coverage, resource, and security limitations. The paper surveys enabling technologies and UAV-based opportunities for addressing these challenges.

  • IoE depends on sensor and embedded technologies, low-power communications, big-data analytics, and related ICT innovations.
  • IoE extends IoT beyond the connection of things to include people, data, processes, and intelligent services.
  • IoE realization faces restricted network coverage, limited node batteries, computing constraints, and security or privacy vulnerabilities.
  • UAVs offer high mobility and flexible deployment that can extend IoE coverage, capacity, and application reach.
  • The paper addresses limitations in prior single-technology surveys through a comprehensive review of IoE expectations, enabling technologies, UAVs, and UAV-enabled IoE.

B. Three expectations of IoE

IoE is organized around scalability, intelligence, and diversity, requiring coordinated communication technologies and substantial distributed resources. These expectations must be met despite an imbalance between expanding service demands and constrained network, energy, and computing resources.

  • Scalability: Scalability requires a global network providing wide coverage, ubiquitous connection, and massive access across varied geographical scenarios.
  • Intelligence: Intelligence requires distributed computing facilities that enable data analysis, prediction, decisions, and actions for IoE devices.
  • Diversity: Diversity spans geographical, stereoscopic, business, and technology dimensions, with future applications expected to combine multiple forms of diversity.
  • Enabling scalability: Scalable communication combines complementary backbone, limb, and capillary networks for long-distance, local, and near-field connectivity.
  • Enabling scalability: IoE technologies follow an on-demand principle by balancing massive communications and limited resources through low-power, wide-coverage, and low-cost designs.

2) Enabling intelligence:

IoE intelligence is enabled by distributed computing facilities that support big-data processing and intelligent algorithms. Cloud, edge, and local resources divide workloads according to their storage, computing, and latency capabilities.

  • Distributed cloud servers, edge servers, and local IoT nodes run big-data and intelligent algorithms to provide global IoE intelligence.
  • Cloud servers handle computing- or storage-intensive tasks, while edge servers perform less intensive preprocessing, compression, and encryption near users.
  • Local IoT nodes support data collection and lightweight preprocessing despite limited storage and computing resources.
  • Big-data analytics supports descriptive, diagnostic, and predictive analysis, while intelligent algorithms extract information for predictive and prescriptive decisions.
  • IoE intelligence uses both conventional optimization schemes and artificial-intelligence algorithms.

3) Enabling diversity:

IoE diversity covers geographic, spatial, business, and technological settings, with applications increasingly combining these dimensions. UAV maneuverability and communication capabilities support flexible deployment across demanding environments, while implementation still faces major resource and coordination constraints.

  • Diversity dimensions: IoE applications span urban, suburban, rural, forest, ocean, and desert regions, as well as terrestrial, aerial, underwater, and space environments.
  • Diversity dimensions: Business and technology diversity extend IoE across intelligent sectors and heterogeneous ICT systems.
  • Diversity fusion: Future IoE applications are expected to fuse multiple diversity dimensions for flexible, on-demand services.
  • Implementation challenges: IoE deployment remains constrained by coverage, battery, computing, and security issues, including limited infrastructure in harsh or rural areas and insufficient terminal computing.
  • UAV support: UAV applications rely on controllable maneuverability technologies including trajectory optimization, obstacle avoidance, onboard algorithms, and remote control.

A. Unmanned aircraft system (UAS)

A UAS combines UAVs, a ground controller, and communication links to support flight control, information processing, and task scheduling. Dedicated UAS designs coordinate UAV, ground-station, and link configurations for specific missions.

  • UAS components: A typical UAS comprises UAVs, a ground-based controller or control station, and communication links between them.The system provides flight control, information processing, and task-scheduling services.
  • Dedicated UAS design: Dedicated UAS design addresses UAV hardware, ground control station, and communication link requirements for applications such as surveillance and tracking.The design is organized around three aspects: UAV, ground station, and communication link.
  • UAV design: UAV hardware is matched to mission requirements, with fixed-wing or larger platforms supporting heavier loads, stability, and persistence.Small UAVs carry smaller loads and generally have shorter battery life, whereas larger UAVs can support long-duration and long-distance tasks.
  • Communication link design: UAS communication uses A2G/G2A links for UAV–ground connectivity and A2A links for collaborative multi-UAV operations.A2G/G2A design considers channel modelling, communication performance, and UAV positioning; A2A design considers channel modelling and collaboration.
  • Communication link design: A2G/G2A studies optimize UAV locations to improve feedback and control-signal communication between UAVs and ground stations.The optimization targets link quality for both UAV-to-ground feedback and ground-to-UAV control.

3) The ground control station’s design:

The ground control station serves as the UAS decision center, while UAV communication networks provide stable UAV–ground and UAV–UAV connectivity. Research emphasizes network organization, link performance, routing, and trajectory optimization.

  • Ground control station: The ground control station schedules UAV tasks and manages remote communications as the decision center of the UAS.Its functions include communication, computing and storage, centralized mission planning, monitoring, and recall.
  • UAV communication networks: UAV communication networks are classified into multi-UAV Ad Hoc networks and UAV-aided communication networks.The former mainly organize UAV-to-UAV connectivity, while the latter extend communications through UAV-based network roles.
  • Multi-UAV Ad Hoc networks: Multi-UAV Ad Hoc networks are autonomous and support mobile connectivity, coverage, and data acquisition in emergency, surveillance, and sensor-network settings.They can operate independently of incumbent mobile networks and assist communications where infrastructure connectivity is poor.
  • Multi-UAV Ad Hoc networks: Multi-UAV research addresses unstable A2A channels and dynamic topologies through trajectory optimization and dynamic routing protocols.Trajectory studies include routing choices and joint optimization with power control.
  • UAV-aided communication networks: UAV-aided networks deploy UAVs as flying base stations, relays, or terminals to provide flexible communications where incumbent networks lose connectivity.Trajectory optimization targets QoS, coverage, time efficiency, energy efficiency, and outage probability.
  • Summary: UAS control and UAV communication networks share UAV mobility design as a common research problem, especially trajectory optimization.The two areas respectively address controllable maneuverability and connectivity of A2A and A2G/G2A links.

IV. CONVERGENCE OF UAV AND IOE

UAVs can serve multiple IoE roles, including aerial infrastructure, data collection, edge computing, security, energy supply, and sensor recovery. These roles provide extended coverage, flexible intelligence, and diverse applications.

  • UAV–IoE convergence: UAVs can act as aerial base stations, data collectors, jammers, monitors, edge computing servers, power suppliers, and reclaimers for IoE.These roles support extended coverage, flexible intelligence, and diverse IoE applications.

A. Opportunities brought by UAVs

UAVs offer four IoE opportunities: ubiquitous connections, aerial intelligence, self-maintenance of communications, and sensor powering and deployment. The proposed Ue-IoE solution integrates UAVs with existing networks to address IoE scalability, intelligence, and diversity.

  • Opportunities brought by UAVs: UAVs’ high mobility and reconfigurability support four IoE opportunities: ubiquitous connections, aerial intelligence, communication self-maintenance, and sensor powering and deployment.These opportunities correspond to extending coverage, processing data onboard, restoring communications, and sustaining IoE nodes.
  • Ubiquitous connections: UAV communication networks extend IoE coverage into weak-connection areas and regions without network infrastructure.UAV-aided networks address weak links, while multi-UAV Ad Hoc networks can independently cover isolated regions and connect them to existing IoE networks.
  • Aerial intelligence: UAVs enable aerial intelligence by collecting surrounding data and running onboard algorithms for collision avoidance, flight adjustment, and trajectory optimization.Data may come from the UAV or ambient sensor clusters, supporting application-specific intelligent operations.
  • Self-maintenance of communications: UAVs support communication self-maintenance by redeploying damaged IoE nodes and addressing risks such as eavesdropping and forging attacks.The passage identifies node redeployment as a way to restore lost links.
  • Sensor powering and deployment: UAVs can power and recycle sensors, improving IoE sustainability by using wireless power transfer to address battery-constrained nodes.The opportunity targets discarded nodes, waste, and pollution caused by depleted batteries.
  • UAV-enabled IoE: The Ue-IoE solution combines UAV technologies with IoE through separate solutions for scalability, intelligence, and diversity.Its scalability component cooperates with WLAN, MCN, LPWAN, and satellite networks to cover weak-connection and infrastructure-free areas.
  • UAV-enabled scalability to IoE: Ue-IoE applications span construction management, emergency networks, patrolling, transportation, smart farms, disaster monitoring, forest monitoring, and ocean-related remote services.The application regions include urban weak-connection areas and remote farms, deserts, forests, and oceans.

2) UAV-enabled intelligence to IoE:

UAVs enable IoE intelligence through intelligent network functions and aerial services, using layered networking, optimized deployment, routing, and distributed computing resources.

  • UAVs enable IoE intelligence by running lightweight AI algorithms that support smart decisions and controls for UAVs and IoE nodes.
  • UAV-enabled intelligent networks: The UAV-enabled intelligent network comprises communication, network, and application layers for optimizing positions and trajectories, routing, congestion control, access, and high-level control.
  • UAV-enabled intelligent networks: Stable wireless connections depend on optimal aerial positions, while routing adapts to dynamically changing topologies in multi-UAV ad hoc networks.
  • UAV-enabled intelligent networks: Application-layer APIs can issue commands to control UAVs and guide data collection operations such as compression and aggregation.
  • UAV-enabled intelligent aerial services: UAV-enabled aerial services apply intelligent algorithms to tasks including real-time monitoring, object tracking, and remote sensing, with requirements varying by task.
  • UAV-enabled intelligent aerial services: UAV intelligence can use local, edge, and cloud computing facilities, trading immediate low-delay responses against UAVs’ limited computing resources.

3) UAV-enabled diversity to IoE:

UAV technologies broaden IoE diversity through flexible communication coverage, intelligent algorithms, and integrated services across transportation, logistics, surveillance, and military missions.

  • UAV technologies enable diverse IoE applications by covering different geographic regions and executing varied intelligent algorithms across scenarios.
  • Intelligent transportation system (ITS): In intelligent transportation systems, UAVs can collect and transmit transportation information and execute traffic schedules, including rapid incident reporting and emergency commands.
  • Rescue and Logistics: Ue-IoE can support online UAV control for product delivery and use UAVs to carry medicines, food, and clothes to areas affected by disrupted roads.
  • Aerial surveillance intelligence: UAV surveillance provides flexible views beyond fixed cameras and supports region detection, object positioning, path planning, and AI-based image and video analysis.
  • Unmanned military missions: UAVs support military missions including battlefield surveillance, enemy troop detection, global battle monitoring, border surveillance, and relay communications.
  • UAV–IoE integration supports diverse applications through wide network coverage, big sharing databases, and ubiquitous intelligence.

V. OPEN RESEARCH ISSUES

Fully realizing UAV-enabled IoE requires addressing resource, security, autonomy, interoperability, and computing-coordination challenges.

  • Five open issues are identified: resource allocation, UAV security, lightweight autonomous-mobility AI, heterogeneous application support, and coordination among UAV, cloud, and edge computing.

A. Resource allocation

Resource allocation in Ue-IoE concerns energy, storage, and computation across nodes, with future work divided between global deployment decisions and local hardware configuration.

  • Rationally allocating energy supply, data storage, and computation capacity across terminal nodes, UAVs, and ground stations can improve serving efficiency and reduce cost.
  • Resource allocation spans global decisions about node numbers and deployment and local decisions about each IoE node’s hardware configuration.
  • Global allocation targets efficiency in time, energy, and equipment costs by optimizing the deployment of edge devices, cloud servers, and UAVs.

B. Security mechanism

UAV-enabled IoE must address security threats, device-level computing constraints, heterogeneous technologies, and coordination across edge and cloud facilities. The paper surveys these issues and presents UAVs as part of solutions for improving IoE capabilities.

  • Security threats: UAV-enabled IoE remains vulnerable to wireless eavesdropping and forged node identities as its scale grows.The paper identifies physical- or MAC-layer countermeasures, stronger authentication, and anti-forgery mechanisms as directions for mitigation.
  • Computing constraints: Lightweight AI algorithms are needed for immediate and precise responses on computation-constrained devices such as UAVs and sensors.Existing studies often execute AI algorithms on resource-rich cloud servers, leaving device-side resource constraints insufficiently addressed.
  • Heterogeneous orchestration: A universal IoE standard is needed to orchestrate heterogeneous ICT technologies and diverse applications across physical, network, and application layers.The proposed standard is intended to reduce usage cost and increase serving efficiency.
  • Coordination between computing facilities: Edge-cloud coordination supports computation-intensive IoE applications by scheduling which tasks remain local and which are uploaded to remote cloud servers.Edge facilities analyze computing requirements before deciding whether remote upload is necessary; this coordination can be continuously optimized as applications evolve.
  • UAV-enabled IoE solution: The survey integrates UAV mobility with current ICT technologies to address coverage, battery, computing, and security constraints while enhancing IoE scalability, intelligence, and diversity.It reviews UAV opportunities and presents a UAV-enabled IoE solution with corresponding sub-solutions and future directions.
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