# Aerial Computing: A New Computing Paradigm, Applications, and Challenges

Quoc-Viet Pham, , Rukhsana Ruby, , Fang Fang, ,
Dinh C. Nguyen, , Zhaohui Yang, , Mai Le,
Zhiguo Ding, and Won-Joo Hwang Quoc-Viet Pham is with the Korean Southeast
Center for the 4th Industrial Revolution Leader Education, Pusan National
University, Busan 46241, Korea.Rukhsana Ruby is with College of Computer
Science and Software Engineering, Shenzhen University, China.Fang Fang is with
the Department of Electrical and Computer Engineering and the Department of
Computer Science, Western University, London, ON N6A 3K7, Canada.Dinh C.
Nguyen is with the School of Electrical and Computer Engineering, Purdue
University, USA.Zhaohui Yang is with the Centre for Telecommunications
Research, Department of Engineering, King’s College London, WC2R 2LS, UK.Mai
Le is with the Department of Information Convergence Engineering, Pusan
National University, Busan 46241, Korea.Zhiguo Ding is with the School of
Electrical and Electronics Engineering, The University of Manchester,
Manchester M13 9PL, UK.Won-Joo Hwang is with the Department of Biomedical
Convergence Engineering, Pusan National University, Yangsan 50612,
Korea.E-mails<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

In existing computing systems, such as edge computing and cloud computing,
several emerging applications and practical scenarios are mostly unavailable
or only partially implemented. To overcome the limitations that restrict such
applications, the development of a comprehensive computing paradigm has
garnered attention in both academia and industry. However, a gap exists in the
literature owing to the scarce research, and a comprehensive computing
paradigm is yet to be systematically designed and reviewed. This study
introduces a novel concept, called aerial computing, via the amalgamation of
aerial radio access networks and edge computing, which attempts to bridge the
gap. Specifically, first, we propose a novel comprehensive computing
architecture that is composed of low-altitude computing, high-altitude
computing, and satellite computing platforms, along with conventional
computing systems. We determine that aerial computing offers several desirable
attributes: global computing service, better mobility, higher scalability and
availability, and simultaneity. Second, we comprehensively discuss key
technologies that facilitate aerial computing, including energy refilling,
edge computing, network softwarization, frequency spectrum, multi-access
techniques, artificial intelligence, and big data. In addition, we discuss
vertical domain applications (e.g., smart cities, smart vehicles, smart
factories, and smart grids) supported by aerial computing. Finally, we
highlight several challenges that need to be addressed and their possible
solutions.

###### Index Terms:

6G, Aerial Access Networks, Aerial Computing, Edge Computing, Enabling
Technologies, Vertical Applications.

##  I Introduction

Research communities have started to define roadmaps for sixth-generation (6G)
networks [1]. As various Internet-of-Things (IoT) applications are set to
emerge in 6G, additional data need to be collected and transmitted. However,
IoT devices are constrained by factors, such as batteries, transmission power,
and processing capacity. To facilitate data transmission from IoT devices
worldwide, several studies have investigated aerial access networks (AANs),
which can offer line-of-sight (LoS) communication links, favorable channels,
and improved coverage. An AAN generally consists of three primary components:
low-altitude platforms (LAPs), high-altitude platforms (HAPs), and low-Earth-
orbit (LEO) satellites [2]. These platforms fully complement the conventional
terrestrial access network to create a future access network in 6G that can
provide wireless services with global coverage and diverse quality of service
(QoS). Moreover, to better support emerging compute-intensive applications,
such as fully autonomous vehicles, flying taxis, holographic communications,
and virtual reality (VR), edge computing (e.g., fog computing and multi-access
edge computing (MEC)) is a promising concept owing to the availability of
powerful computing and storage resources at the edge of the network [3]. The
amalgamation of AANs and edge computing introduces a novel concept referred to
as aerial computing. Aerial computing is expected to provide advanced
services, such as communication, computing, caching, sensing, navigation, and
control, at a global scale.

Aerial computing is considered a promising paradigm that enables local data
analysis and real-time service provisioning in the air. Facilitated by
advantages, such as high mobility, fast deployment, global availability,
scalability, and flexibility, aerial computing complements conventional
computing paradigms (e.g., cloud computing, fog computing, and MEC) and is
thus considered a pillar of the comprehensive computing infrastructure in
future 6G networks [4]. For example, in [5], IoT data over small areas could
be collected and processed cooperatively by a set of unmanned aerial vehicles
(UAVs) at low altitudes, whereas IoT data over large-scale areas could be
executed by HAPs at high altitudes. In [6], UAVs were leveraged as
intermediate points between vehicles and the network edge to enable data
collection and task preprocessing and thus facilitate data transfer in smart
vertical domain environments. Meanwhile, in the race toward global Internet
connectivity among tech giants, such as SpaceX, Google, Amazon, and Facebook,
several LEO satellite systems have been deployed to provide Internet services
to users, particularly in rural and hard-to-reach areas, with a performance
that is comparable to that of the current terrestrial mobile network [7].
However, these systems have not yet been integrated into current computing
systems as key components.

The above observations emphasize the importance of a comprehensive computing
architecture for future 6G systems. As a native constituent, aerial computing
is composed of low-altitude and high-altitude computing platforms as well as
satellite computing platforms. It can carry its full complement of
conventional terrestrial computing paradigms. Conceptually, aerial computing
can facilitate various smart industrial applications, such as smart cities,
smart vehicles, smart factories, and smart grids. Moreover, it is expected to
provide a reference 6G computing architecture for future studies.

###  I-A Visions Toward a Comprehensive Computing Infrastructure

In recent years, several surveys on edge computing and mobile networks have
been conducted. For example, enabling MEC technologies, including virtual
machines (VMs), network function virtualization (NFV), software-defined
networking (SDN), and network slicing were reviewed in [8]. The use of MEC for
IoT applications, such as critical and massive IoT, wearable IoT, smart
energy, and IoT automotive, was discussed in [9]. Computation offloading,
resource management, and optimal placement problems in edge computing systems
were discussed in [10, 11, 12]. The amalgamation of MEC and AI, referred to as
edge intelligence and intelligent edge, has been reviewed in several studies
[13, 14]. Further, certain reviews focused on the integration of edge
computing with emerging technologies, such as blockchain [15] and fifth-
generation (5G) and beyond networks [16]. More recently, privacy/security
issues and solutions of MEC services were reviewed [17].

MEC is a key enabler of many wireless services offering better QoS. Moreover,
MEC has the potential to be closely integrated with emerging technologies and
systems [15, 16]. In addition, these studies elaborate on the various
challenges of MEC that need to be examined in beyond-5G networks, such as
resource optimization, security and privacy, and real-time implementation.
With the expected extraordinary popularization of the next-generation Internet
and the emergence of killer applications, such as VR, space tourism, fully
autonomous vehicles, holographic communications, and deep-sea sight, the
current MEC infrastructure needs to be further expanded both vertically and
horizontally to complement existing edge computing systems. However, the
aforementioned reviews [8, 9, 11, 10, 12, 13, 14, 15, 16, 17] primarily focus
on 5G network scenarios, technologies, and applications, but the new
requirements for a comprehensive 6G computing infrastructure have not been
reviewed yet.

Future 6G systems have been envisioned in recent studies. For example, the
authors of [18] discussed their vision for use cases, enabling technologies,
and overcoming the challenges of 6G networks. In [19], a set of key
performance indicators (KPIs) were defined for 6G, such as peak data rate
$>$100 Gbps, traffic density $>$100 Tb/s/km2, network density $>$10 million
connections per km2, mobility $>$1000 km/h, centimeter-level positioning
accuracy, reliability $>$99.999%, receiver sensitivity $<$-130 dBm, $3$-times
spectral efficiency, and $10$-times energy efficiency. Six technical trends
were discussed in [19], namely, coverage expansion, new resources and
bandwidth spectrum, new modulation techniques, enhancing the system capacity,
adding computing and intelligence capabilities, and three-dimensional (3D)
network architecture. Regarding 3D networks, several studies have reviewed UAV
communications [20], HAP networks [21], airborne communications [22],
satellite communications [23], terrestrial-satellite integrated networks [24],
and future aerial networks [25]. Recently, the primary aspects of aerial radio
access networks, such as architectures, network design, enabling technologies,
and applications, were reviewed in [26]. Owing to excellent features and
numerous applications, many researchers believe that AI techniques are a key
component of many 6G systems. For example, machine learning (ML) is considered
a double-edged sword for privacy in 6G [27]. Moreover, AI is an integral part
of future networks, such as sensing AI at receivers, on-device AI, access AI
(i.e., transmission at PHY, MAC, and network layers), and data-provenance AI
[28].

The above surveys mainly concentrate on the KPIs, enabling technologies,
potential applications, and major challenges of 6G. The existing reviews
emphasize the importance of a comprehensive computing infrastructure for the
foundation of 6G in the next 10 years. However, despite promising studies and
an urgent need for a comprehensive computing infrastructure, a large gap
exists in the existing studies because aerial computing has not been
systematically reviewed. This paper attempts to bridge this gap by introducing
a new computing architecture that can be employed in future network systems.
This paper also presents a comprehensive review of aerial computing. Table I
summarizes the existing surveys on edge computing and aerial communications.
It also highlights the contributions of the present study.

Table I: Summary of existing surveys on edge computing and future wireless networks.  Reference | Main theme |  Key Contributions |  Limitations
---|---|---|---
6G/AI | MEC | AAN
[8] |  | ✓ |  |  Enabling technologies of MEC systems, such as VM, containers, NFV, SDN, and network slicing, are reviewed. |  This paper does not focus on aerial computing and its role.
[9] |  | ✓ |  |  MEC applications for IoT service realization in 5G networks. |  The role and applications of aerial computing have not been presented.
[11, 10, 12] |  | ✓ |  |  Reviews of seminal MEC architectures, computation offloading issues, resource management, and optimal placement problems. |  The role and applications of aerial computing are not discussed.
[13, 14] |  | ✓ |  |  Interactions between AI techniques and MEC systems; that is, intelligent edge and edge intelligence. |  The role of aerial computing in enabling a comprehensive computing infrastructure in 6G is not considered.
[15] |  | ✓ |  |  Integration of blockchain and MEC systems. |  Only the integration of blockchain into edge computing systems is presented.
[16] |  | ✓ |  |  Integration of MEC with 5G technologies, such as cloud radio access networks, massive multi-input multi-output (MIMO), unmanned aerial vehicle (UAV) communications, and AI. |  Only the integration of MEC with 5G technologies is reviewed, while the role and applications of aerial computing are ignored.
[17] |  | ✓ |  |  Comprehensive survey on privacy/security issues and solutions in MEC systems. |  The paper reviews only the security and privacy aspects of MEC systems.
[18] | ✓ |  |  |  A speculative study on promising applications and major technologies in future 6G networks. |  Only the vision of future 6G networks is discussed.
[19] | ✓ |  |  |  A vision of KPIs, technologies, technical trends, and challenges of 6G networks. |  Only a brief discussion of future 6G networks is presented.
[23, 24, 20, 21, 22, 25] |  |  | ✓ |  3D networking, such as UAV communications [20], HAP systems [21], airborne communications [22], satellite communications [23], terrestrial-satellite integrated networks [24], and wireless communications for future AANs [25]. |  These papers primarily focus on the communication aspects of AAN, while the computing aspects and the role of aerial computing are ignored.
[26] | ✓ |  | ✓ |  Technical aspects of AANs in the 6G context and key enablers. |  The computing aspects and applications of aerial computing are not presented.
[27] | ✓ |  |  A comprehensive survey of the alliance between ML and 6G privacy. |  The paper focuses only on privacy aspects and its alliance with ML.
[28] | ✓ |  |  |  Four important domains of AI in 6G are reviewed, namely, sensing AI, on-device AI, access AI, and data-provenance AI. |  The paper focuses on the role and applications of AI in future networks.
This paper | ✓ | ✓ | ✓ |  A comprehensive survey of aerial computing. In particular,  • We introduce a novel architecture of aerial computing, which complements the existing computing infrastructures. Important features of aerial computing, such as mobility, availability, scalability, flexibility, and simultaneity, have also been analyzed in detail. • We provide in-depth discussions of key enablers of aerial computing and the use of aerial computing in important vertical applications, including smart cities, smart vehicles, smart factories, and smart grids. • Key challenges of aerial computing are presented along with potential solutions and future research directives. |

###  I-B Contributions and Research Methodologies

As discussed above, aerial computing is a promising concept for providing
computing resources and wireless services on a global scale. This paper
attempts to bridge the gap in the existing literature by presenting an in-
depth review of aerial computing with a vision toward a comprehensive
computing infrastructure in future 6G networks. The review covers the
following aspects.

Fundamentals and Designs (Section II): First, to enrich the full understanding
of the concept of aerial computing, we present the system architecture, which
is illustrated in Fig. 1. This comprehensive computing architecture is
expected to be the key enabler of many computationally intensive applications
that will be available in 6G systems. Following the analysis of the proposed
architecture, we elaborate upon the important features of aerial computing,
including ubiquity, mobility, availability, simultaneity, and scalability, and
further differentiate between aerial computing from other computing paradigms.
Further, we present important design problems for an aerial computing system,
including computation, communication, energy consumption, and latency models.

Enabling Technologies (Section III): To realize aerial computing in 6G
systems, we discuss a set of key enabling technologies, which include network
softwarization, energy refilling, frequency spectrum, multi-access techniques,
and AI and big data. In particular, energy refilling is crucial for
maintaining the sustainable operations of aerial components. With regard to
the operational management of aerial computing systems, we review state-of-
the-art studies on softwarization techniques, including VM, network slicing,
NFV, and SDN. These softwarization techniques are important for facilitating
flexible and programmable operations and the deployment acceleration of aerial
computing systems. In addition, we discuss frequency spectrum and multi-access
techniques (e.g., massive MIMO, intelligent reflecting surface (IRS), and non-
orthogonal multiple access (NOMA)), used in aerial computing systems. Finally,
AI and big data analytics are considered important technologies for exploiting
massive data generated from aerial computing, thus improving system
performance.

Vertical Domain Applications (Section IV): The integration of wireless
technologies into industrial automation systems has shown great potential in
creating more efficient and productive vertical domain applications (i.e.,
smart cities, smart vehicles, smart factories, and smart grids). Further, we
present an extensive review of state-of-the-art studies on these vertical
domain applications in the context of 6G aerial computing. In this regard, we
discuss the manner in which aerial computing can support vertical domain
applications.

Research Challenges and Potential Directions (Section V): To further drive
research into aerial computing in the future, we discuss the major challenges,
possible solutions, and potential directives. Aerial computing systems face
critical challenges, such as energy efficiency, efficient resource management,
and network stability. Further, large-scale network optimization is another
critical issue for embedding distributed and learning approaches such that
aerial computing systems can efficiently operate on a large scale. Finally,
security, privacy, and trust are important aspects of aerial computing but
have not been adequately addressed by existing studies.

##  II Fundamentals and Design

In this section, we present the system architecture and fundamental features
of aerial computing.

Figure 1: A novel comprehensive 6G computing architecture.

###  II-A System Architecture

As shown in Fig. 1, we propose a novel aerial computing-based 6G architecture
wherein the aerial components are positioned in a hierarchical manner. In
addition to conventional computing infrastructure, the proposed aerial
computing architecture consists of four main entities: IoT devices, low-
altitude computing (LAC) platforms, high-altitude computing (HAC) platforms,
and satellite computing platforms, along with vertical domain applications and
key enabling technologies.

IoT: This layer refers to any IoT device that generates data and comprises
compute-intensive applications that need to be executed by aerial computing
systems. For example, wearable devices, such as smart watches, eyeglasses,
fitness trackers, and body-mounted sensors are responsible for monitoring and
collecting the health information (e.g., heart rate, physical activity, and
blood pressure). These devices may offload collected data to an edge node for
further processing because they are typically resource-constrained and have a
relatively small size. Aerial components (e.g., UAVs and drones) can be a part
of this IoT layer when they are dispatched to collect data from ground
sensors. To maximize the flight time and data collection efficiency, the UAV
offloads the collected data to a terrestrial MEC server and/or other more
powerful UAVs.

Terrestrial Computing: This layer comprises conventional terrestrial computing
paradigms, such as fog computing, MEC, and cloudlet. Typically, computing
nodes are deployed at preset locations. According to [16], the potential
deployment locations of an MEC server include routers, IoT gateways, macro
base stations (BSs), small cell BSs, optical network units, radio network
controller sites, and wireless fidelity (WiFi) access points, whereas the
common locations of a fog node are gateways, intermediate nodes (between the
cloud and end devices), and network elements (e.g., routers and switches)
[29]. The collaborative and hierarchical MEC framework utilizes the advantage
of many applications and services, as edge devices with different capabilities
can collaboratively perform data processing and task execution [30].

Low-Altitude Computing: The LAC platform is positioned in the lowest tier of
aerial computing systems with an altitude of 0–10 km above the Earth’s
surface. This platform offers several advantages, such as cost-effectiveness,
rapid deployment, and LoS communication links. Hence, LAC systems are highly
effective for emergency scenarios, military surveillance, temporary events,
and IoT data collection and processing [31]. The authors of [32] demonstrated
that the UAV can be leveraged for data collection, aerial relaying, and aerial
computing of the tasks offloaded from ground IoT devices. The primary
components of the LAC platform are drones and UAVs equipped with computing
capabilities, which typically have limited battery capacities, low computing
resources, and low endurance. Consequently, several solutions have been
proposed to address these challenges, such as wireless- and solar-powered UAV-
MEC and task offloading to more powerful terrestrial and HAC-MEC servers [33].
There exist two types of LAC servers: fixed-wing and rotary-wing. The former
does not hold up in the air and needs to move forward continuously, whereas
the latter can take off and land vertically.

High-Altitude Computing: The HAC platform comprises both manned and unmanned
aerial components (e.g., airplanes, airships, and balloons) that operate at an
altitude ranging from 17 to 50 km. The advantages of HAC systems over other
platforms include large-area coverage with the cell size of up to 10 km,
adaptability to traffic demands, rapid deployment compared to terrestrial
computing and satellite computing, high endurance compared to LAC platforms,
and green operation capability with solar power [34]. At the World Radio
Conference (WRC)-19, several frequency bands were allocated to HAC systems by
the ITU Radio communication Sector, including the 31–31.3 and 38–39.5 GHz
bands for worldwide use, 47.2–47.5 and 47.9–48.2 GHz for administration use,
and 21.4–22 and 24.25–27.5 GHz for fixed services in ITU Regions 2 [35]. In
recent years, several industrial projects have been observed in LAC platforms
for wireless and computing services, such as flying cells on wings [36],
Google Skybender [37], and ApusDuo [38]. Owing to the quasi-stationary
feature, HAC platforms can form a collaborative HAC-MEC server with more
powerful capabilities and higher time endurance, aiding in the execution of
the computation tasks that are offloaded from LAC and/or satellite systems.

Satellite Computing: This layer is composed of LEO satellites with computing
capabilities. LEO satellites typically operate at an altitude of approximately
80–2000 km above the Earth’s surface. For example, SpaceX launched 60 LEO
satellites at altitudes not greater than 580 km in May 2019. These satellites
can provide Internet services globally with a data rate ranging from 50 to 150
Mbps and a latency ranging from 20 to 40 ms (www.starlink.com/). Amazon also
initiated a satellite project called Kuiper, which plans to launch a
constellation of 3200 satellites at an altitude of approximately 610 km [39].
These LEO satellites are expected to provide various wireless and computing
services, particularly when other computing platforms are temporarily
unavailable and/or satellite computing is the only option. Unlike in LAC and
HAC platforms, users are not usually connected with satellite computing
systems because of the excessive hardware cost incurred when MEC servers are
embedded in LEO satellites. Instead, LEO satellites can act as relays to
receive and transmit computation tasks/results between users and MEC servers.
However, computing directly at the satellite edge server is beneficial for
applications with sparse users; that is, users are located at different
geographical locations [40, 24].

Industrial Applications: Aerial computing supports and enables (new)
industrial applications owing to its distinctive features, such as high
mobility, always-available computing, and scalability (discussed in Section
II-B). For example, aerial computing systems can support large-scale vehicle
systems and/or advanced traffic signal infrastructures to improve the
experience of in-car infotainment applications and reduce traffic congestion
by deploying massive MEC on roadside units, power systems, buildings, and
highway infrastructure. In Section IV, we comprehensively discuss the use of
aerial computing for four primary industrial applications: smart cities, smart
vehicles, smart factories, and smart grids.

Enabling Technologies: The realization of aerial computing is facilitated by
several key enabling technologies, including network softwarization, energy
refilling, frequency spectrum, multi-access techniques, and AI and big data. A
thorough discussion of these technologies in the context of aerial computing
is presented in Section III.

###  II-B Desirable Features

Aerial computing retains the base features of the concept of edge computing,
including on-premises, proximity, lower latency, location awareness, and
network contextual information. In addition, it has certain fundamental
features that differentiate it from other computing paradigms (e.g., fog
computing and cloudlet), which are discussed as follows.

  * •

Mobility: Aerial platforms can be deployed quickly to support on-demand
computing applications. While LAC platforms can enable the rapid roll-out of
computing services, the deployment of a terrestrial computing system is time-
consuming owing to planning procedures and civil works involved. From the
perspective of the user, the overlaying architecture of LAC, HAC, and
satellite computing helps aerial computing systems flexibly satisfy different
computation tasks from end IoT devices.

  * •

Availability: Aerial computing systems are usually available as they are not
much affected by natural disasters, unlike terrestrial computing, and can be
easily and quickly deployed for particular computation tasks. The availability
of aerial computing also relies on its hierarchical architecture with
different computing platforms at different altitudes and coverage distances.

  * •

Scalability: Aerial computing systems can provide on-demand computing services
as well as applications with geographically distributed users. As the aerial
components can collaboratively form computing clusters, massive IoT devices
can be served, thereby improving user satisfaction and guaranteeing service
continuity.

  * •

Flexibility: Unlike terrestrial computing, wherein MEC and fog nodes are
typically deployed at pre-specified locations, aerial computing entities can
be changed easily to flexibly suit different situations. Further, enhanced
computing services may be provided via additional aerial computing platforms,
such as LAC and HAC.

  * •

Simultaneity: Different aerial computing platforms can provide computing
services to geographical users simultaneously. Thus, users in different
countries can request computing services to LEO satellite computing services
concurrently. Therefore, aerial computing is regarded as a full complement of
terrestrial computing infrastructure, which is usually used for localized
computing services.

Table II: Comparison of aerial computing with other edge computing paradigms. |  Cloudlet |  Fog Computing |  MEC |  Aerial Computing
---|---|---|---|---
Introduced by |  Satyanarayanan, 2009 [41] |  Cisco, 2012 |  ETSI, 2014 |  This work, 2021
Purpose | Moving cloud computing capabilities to the network edge |  Making computing capabilities available at all times, at both the network edge and air
Deployment |  Business premises (e.g., shopping malls, companies, and personal computers) |  Strategic locations both indoors and outdoors (e.g., IoT gateways, routers, and switches) |  Various locations within radio access networks (RANs) (e.g., radio towers and gateways) |  Locations in the air and within RANs (e.g., radio towers, UAVs, and LEO satellites)
Application
Examples |  Locally resource-intensive and interactive applications |  Smart vertical domain applications (e.g., smart cities and smart health) and video surveillance |  Content caching, autonomous vehicle, augmented reality (AR), data analytics |  On-demand applications and applications with sparse user distribution
Internet
Connectivity |  Autonomous operation (e.g., WiFi) |  Autonomous operation or intermittent Internet connectivity |  Autonomous operation or mobile Internet |  Autonomous operation or mobile Internet
Service
Coverage |  Local |  Less global |  Less global |  Global
Latency |  Low |  Low |  Low |  Medium
User
Proximity |  Low |  Relatively low |  Low |  Various, from low to long
Architecture |  Localized |  Distributed/hierarchical |  Localized/hierarchical |  Localized/hierarchical/on-demand
Storage
Capacity | Dependent on deployment scenarios [29, 42] |  High |  High
Computational Power | Dependent on deployment scenarios [29, 42] |  High |  High
Power
Consumption |  Low [43] |  Low [43] |  High [43] |  Low
Availability | High [44] |  Very high
Scalability |  Low | High [44] |  Very high
Mobility |  Low [44] | High [44] |  Very high
Security |  Medium | High
Context awareness |  Medium |  High |  High |  High
Local
Awareness | High [17]
Standard
Organizations |  NIST |  OpenFog Consortium |  ETSI, 3GPP, ITU-T |  -
Virtualization support |  VM | VM, NFV, network slicing, and other virtualization technologies
Operation Mode |  Standalone only |  Cloud-connected support |  Both cloud-connected and standalone operation |  Both cloud-connected and standalone operation

After analyzing these features, we draw a comparison between aerial computing
and other edge computing paradigms, as presented in Table II. Based on the
above explanations, aerial computing can support other computing concepts,
such as in-network computing and cloud-native computing. However, resource and
operation constraints of aerial computing limit its capability to enable high-
performance services in in-network computing and scalable applications in
cloud-native computing. Therefore, the integration of aerial computing with
edge computing, in-network computing, and AI techniques is necessary to
provide advanced services and applications in the future. For example, network
elements in aerial computing, as edge devices, can partially perform
computation tasks before pushing them into the network elements (e.g.,
switches and routers) for further processing. The integration shows great
potential to respond to heterogeneous network scenarios and application
requirements in 6G, but several challenges and limitations should be addressed
[45].

###  II-C Network Design

This part explains the important models for designing and optimizing an aerial
computing system.

####  II-C1 Computation Model

The first component of aerial computing involves computing models with the
following fundamental aspects:

  * •

computation offloading decision,

  * •

deployment locations of edge servers,

  * •

collaborative and hierarchical computation.

Computation offloading decision: A computation task $I$ can be modeled by a
tuple $I=(C,D,T)$, where $C$ is the computation workload (CPU cycles per bit),
$D$ is the task size (bits), and $T$ is the completion time deadline
(seconds). Fundamentally, an offloading decision results in either local
computing at the devices, full execution at the edge server, or partial
offloading at both. In the case of partial offloading, the task size $D$ is
divided into smaller parts for local execution and remote execution at the
edge servers and/or nearby devices.

Deployment location: Computation in aerial computing may have the following
deployment location possibilities: 1) local devices, 2) terrestrial edge
servers, 3) LAC servers, 4) HAC servers, 5) LEO satellites, and 6) centralized
clouds. Each deployment location of the edge servers has both advantages and
disadvantages. For example, the first scheme (i.e., local computing at the end
devices) does not incur any transmission latency, but poor computing resources
render it unsuitable for many emerging applications and new services, such as
extended reality (XR) and smart cities. Further, computation and content
caching at the satellite computing platforms (e.g., LEO satellites) is highly
beneficial to applications with users in remote areas (e.g., mountains and
islands) but might result in a higher latency and hardware cost compared to
those of terrestrial computing and LAC/HAC platforms [40, 24].

Figure 2: Collaborative computation between LAC and HAC platforms.

Collaborative and hierarchical computation: However, reliance on only an
aerial computing node/platform (i.e., LAC or HAC only) to perform emerging
compute-intensive applications is highly impractical. This is due to the
onboard energy limitations of LAC platforms, intermittent harvested energy of
HAC platforms, and hardware cost of satellite computing platforms for big data
applications. The synergy between LAC and HAC platforms in aerial computing
systems is discussed in [33]; herein, it is illustrated in Fig. 2. In such a
scenario, the more powerful HAC platform and fixed-wing LAPs can assist the
long-term operation and computation tasks of rotary-wing LAPs, which are
mainly responsible for data collection from ground IoT users [33, 46].

####  II-C2 Communication Model

The communication model plays an important role in aerial computing systems
for guaranteeing efficient communications among computing platforms (e.g.,
between IoT devices and LAC servers). Several communication models have been
proposed for this purpose. In the literature, orthogonal multiple access
(OMA), NOMA, and MIMO have been typically adopted for the achievable rate
formula. When OMA (e.g., time division multiple access (TDMA), frequency
division multiple access (FDMA), and orthogonal frequency-division multiple
access (OFDMA)) are widely used for ease interference management. In addition,
there are studies on hybrid OMA-NOMA computing systems [47]. They demonstrated
that hybrid NOMA systems are highly advantageous compared to conventional OMA
systems. Thus, the use of such hybrid OMA-NOMA approaches in aerial computing
systems is a promising approach.

####  II-C3 Energy Consumption Model

Energy efficiency is an important design requirement for aerial computing
systems. Energy is consumed in several aspects, including local computation,
communication, remote computing, and operation. The other part is the energy
consumed by the aerial components (i.e., aerial users and servers), which is
important for designing sustainable operations of aerial computing systems,
particularly LAC platforms with limited onboard batteries. However, various
factors, such as flying modes, flying speed, payload, and external conditions
(e.g., weather and wind speed) can affect the energy model of aerial
components. In an effort to incorporate this, the authors of [48] modeled the
power consumption of multirotor helicopters in hover and vertical moving
modes. The power consumed by the UAV to remain afloat in the horizontal moving
mode is modeled in [49]. These models have been studied to improve the energy
and power efficiency of aerial computing [50]. Moreover, the possibility of
aerial computing in terms of energy and power consumption can be realized by
integrating aerial computing with emerging technologies in 6G, as discussed in
Section III.

####  II-C4 Latency Model

A benefit of aerial computing is the incorporation of LEO satellite computing
platforms, which provide a global coverage but have lower latency compared to
geostationary Earth orbit (GEO) and medium Earth orbit (MEO) platforms. As
reported in [51], the OneWeb LEO satellite system can provide Internet
services for 1 billion users with an average latency of 30 ms. The Starlink
system promises a latency between 25 and 35 ms, while the Telesat system aims
to achieve latency from 30 to 50 ms, and the average latency of the SpaceX
system is less than 20 ms. Moreover, positioned in the lowest tier of the
comprehensive aerial computing architecture, LAC platforms are expected to
provide much lower latency and thus are suitable for latency-critical
services. Further, in the middle tier, HAC platforms aim to balance latency
and computing service availability. Therefore, aerial computing can provide
computing services with different latency requirements while offering
desirable features, such as mobility, availability, scalability, flexibility,
and simultaneity.

Other latency sources in aerial computing include computation, queuing, and
fronthaul and backhaul transmission. Latency also occurs when the server
executes the computation task remotely and broadcasts the processed results to
the users in the downlink. Consequently, aiming to minimize the maximum
latency among users, [52] investigated a joint problem of two-dimensional LAC
placement, fronthaul and backhaul bandwidth allocation, computing resource
allocation, and caching decision. Further, in [53], the joint optimization of
task allocation, scheduling, power control, and LAC server trajectory was
studied to minimize the total energy consumption. To manage the latency
requirement, the LAC server must execute a minimum number of bits offloaded
from each user.

###  II-D Summary

This section presents the system architecture of aerial computing, fundamental
features, and key design aspects. Complementing conventional terrestrial
computing systems, the proposed aerial computing framework provides computing
services to massive IoT users worldwide. Compared to other edge computing
paradigms, aerial computing offers several desirable features, such as
mobility, availability, scalability, flexibility, and simultaneity. Moreover,
four key design aspects of aerial computing have been reviewed, namely,
computation, communication, energy consumption, and latency. Because many
emerging applications will be available in the near future, more thorough
investigations are required to explore the potential advantages of aerial
computing.

##  III Enabling Technologies

Enabling technologies of aerial computing are presented in this section. These
technologies include network softwarization, energy refilling, edge computing,
frequency spectrum, multi-access techniques, AI, and big data. To enrich the
understanding of the integration of aerial computing with enabling
technologies, we illustrate the manner in which enabling technologies support
aerial computing in Fig. 3.

###  III-A Network Softwarization

Virtualization has enabled network operators to design, implement, and manage
systems and network services with improved efficiency. NFV and SDN are two
promising technologies that facilitate virtualization. NFV decouples network
functions from proprietary hardware (e.g., firewalls and routers), thereby
providing equivalent network functionality with general-purpose servers. SDN
separates the control plane from the forwarding plane, thereby enabling
network operators to configure and manage network functions in a centralized
manner through the software. Other commonly used virtualization technologies
include network slicing and VM. We briefly introduce the fundamentals of these
technologies and thereafter discuss their application in aerial computing.

Figure 3: Integration of aerial computing with enabling technologies.

####  III-A1 Network Function Virtualization

The concept of NFV was first proposed in 2012, with an aim to reduce the
capital and operating expenses of telecom networks, as well as to facilitate
the deployment of new services. Its primary idea is to decompose the network
functions from physical network equipment via virtualized network functions
(VNFs). A typical NFV architecture consists of three main elements:
infrastructure, management, and orchestration. The NFV infrastructure
represents computing, storage, and network hardware as well as software
(abstractions of physical resources) resources constituting the environment
where VNFs are deployed. In addition, a VNF is an implementation of a network
function, for example, a firewall, deployed on virtual resources such as VMs.
Furthermore, NFV-Management and Orchestration (MANO) is the management and
orchestration framework needed to provide the VNFs.

NFV provides many opportunities for network service provisioning, e.g.,
independent deployment and maintenance of software and hardware, flexible and
agile service deployment, and dynamic resource allocation. Therefore, NFV can
benefit aerial computing. For instance, NFV can allocate additional resources
from another LAC/HAC/satellite computing platform to alleviate computing
congestion in one platform.

####  III-A2 Software-Defined Networking

SDN is an emerging network architecture that intelligently controls the
network by decoupling the control plane and efficiently managing the network
via programming operations [54]. Driven by its key features, including
separated control plane and data plane, centralized controller, open
interfaces, and programmable operations, an SDN can intelligently operate the
network at a low operating cost. Thus, it can support intelligent applications
through simplified hardware, software, and management [8]. Despite these
advantages, SDN has several key challenges in the 6G network: 1) efficient and
intelligent maintenance of the dynamic network topology [55], 2) intelligent
network management and orchestration via AI/ML, and 3) traffic engineering
with heterogeneous QoS requirements [56].

In aerial computing, wireless networks support heterogeneous IoT
devices/users, LAC/HAC platforms, 6G technologies, and configuration
interfaces. SDN can provide logically centralized control by abstracting the
underlying network infrastructure. For instance, SDN controllers can handle
aerial-computing-related VNFs and VMs as another type of resource that can be
dynamically managed. Therefore, SDN can provide an efficient solution for
managing aerial computing systems and services.

####  III-A3 Network Slicing

Network slicing is a virtualization technology that allows multiple logical
networks (slices) to run on a unified physical network infrastructure [57].
Each logical network is independently configured to satisfy the required
network characteristics, such as bandwidth, delay, and capacity, to provide
diverse services of expected scenarios. Each logical network also contains
computing and storage resources that are capable of realizing specific network
functions via NFV or service function chains. In contrast, VM can provide
fine-grained control to instantiate and terminate tasks and processes at any
time without affecting the hardware on aerial platforms. Each VM shares
computing, storage, memory, and network resources from aerial computing
systems, while its operation is entirely isolated from that of the host and
guest VMs. Further, AI/ML can be employed to intelligently allocate virtual
resources of VMs in aerial computing.

In aerial computing, network slicing can be utilized to slice the entire
network into individual networks considering computing services and IoT
services, which can be optimized by specific requirements and services.
However, an AI-enabled intelligent management and orchestration framework
should be designed to efficiently support network slicing in 6G networks.

###  III-B Energy Refilling

####  III-B1 Energy Harvesting

Energy harvesting is a novel approach to utilize renewable energy sources. For
example, solar energy can be converted into electric energy by deploying
photovoltaic (PV) cells on UAVs, and the electric energy can be stored in
rechargeable batteries [58]. However, natural energy sources are highly
dependent on climate variability. For instance, solar power harvesting can be
severely affected by weather conditions.

Some non-electromagnetic field-based charging technologies have been used,
including gust soaring, PV arrays, laser beaming, and battery dumping [59].
Dynamic soaring involves harvesting energy from wind and airflow by adjusting
the trajectory of UAVs to prolong their flying duration [60]. For PV arrays,
solar irradiation energy is harvested by PV cells to power a drone or recharge
its battery, which is used for the operation during the night. This technology
can be applied to fixed-wing drones because a certain amount of space is
needed for PV panels. In laser-beaming technology, a laser fed by an external
source of energy produces a concentrated and streamlined beam of light with a
certain frequency to the specific PV cells on a drone. The PV cell can then
convert the laser beam into energy to recharge the battery of the drone.

####  III-B2 Wireless Power Transfer

Another popular application of energy harvesting is that energy is usually
harvested from ambient radio frequency electromagnetic signals. In
simultaneous wireless and information power transfer (SWIPT), energy can be
harvested, usually partially, from the transmitted signals via power switching
or time switching techniques. A strong user with a high channel gain can
forward data to a weak user with a low channel gain by using the harvested
energy from the transmitted signals. Wireless power communication networks
(WPCNs) are used to harvest energy from radio transmission, wherein sequential
energy is transferred wirelessly to the intended users, which supports
dedicated wireless charging [61]. Aerial components at different computing
platforms (e.g., UAVs at the LAC platform and balloons at the HAC platform)
have been proposed to be integrated with WPCN. This flexible implementation
allows aerial components to be charged or to charge a set of ground users,
thus providing sustainable solutions for aerial computing systems [62, 63].
Besides, aerial components can be equipped with solar-powered cells and large
energy storage to store energy harvested from solar powers in the environment.

Wireless power transfer (WPT) has been proven to provide power for UAVs
wirelessly without landing for energy refueling [64]. Moreover, the
coordination of LAC servers at the LAC platform and edge servers on different
platforms (i.e., LAC, HAC, and satellite) is a promising approach to better
provide computing services over large-scale areas [63]. Since WPT from the air
is quite limited due to the severe path loss, many factors (e.g., antenna
size) need to be considered for practical implementation. To further support
low-power IoT/mobile devices, emerging technologies, such as backscatter
communications and IRS, can be deployed. For example, [65] considered IoT
devices as backscatter-assisted users, which harvest energy from the ground
gateway to perform computation offloading. Similarly, aerial components can
assist low-power users in performing necessary data transmissions and
computation tasks.

###  III-C Frequency Spectrum

####  III-C1 Sub-6 GHz and mmWave

To meet the extremely high data rate requirements of 5G systems, high-
frequency spectrum bands are exploited. In particular, millimeter waves
(mmWave) can provide gigabit-per-second data rates owing to the radio
frequency spectrum in the range of 30–300 GHz, whereas the sub-6 GHz range can
provide large coverage with high cost efficiency with a spectrum band below 6
GHz [66]. Driven by the increase in IoT devices, such as XR services
encompassing AR, mixed reality, and VR, some studies proposed the integration
of mmWave and Sub-6 GHz to balance the high data rate and large coverage in
next-generation wireless communication systems. Specifically, mmWave can be
used in tiny cells for mobile and fixed access, whereas sub-6 GHz can be used
in small cells. For example, the use of the mmWave band in aerial computing
was considered in [67], wherein a hierarchical architecture of a satellite
computing server, two terrestrial MEC servers, and a set of vehicles was
considered.

####  III-C2 THz

Terahertz (THz) band, in the frequency range of 0.1–10 THz, has been explored
in the next-generation wireless communication systems to achieve the terabits
per second data rate with low latency. Potential use cases for THz
communications include close proximity communication, extremely high data
rates for indoor communications, and wireless backhauling and fronthauling
technologies. Thus, THz can be utilized in LoS and high data transmission
among aerial components in aerial computing systems [68].

####  III-C3 Other High-Frequency Bands

At WRC-12, the ITU identified the C-band for the use of control and non-
payload communication. The C-band refers to a frequency range of 4–8 GHz and
is used primarily for satellite communications. Moreover, the K-band in the
frequency range of 14–27 GHz is typically used for short-range communications
owing to the high atmospheric attenuation. The K-band can be used in satellite
communications, astronomical observations, and radar. Radars in this frequency
range provide a short range and high throughput.

###  III-D Artificial Intelligence and Big Data Analytics

AI technology aims to train machines to perform human tasks. It has been
applied to various areas, such as image recognition, robotic vehicles, machine
translation, and game AI. Further, ML, which is a promising subset of the AI
technique to learn from the data and impart intelligence to existing systems,
has been employed to render wireless communication and networks highly
efficient and adaptable. Intelligent wireless network can provide efficient
support for aerial computing. The advantage of applying ML in wireless network
operation is that it enables the network to monitor, learn, and predict
various communication-related parameters, such as wireless channels, traffic
patterns, user context, and device locations. In general, ML algorithms
include supervised learning, unsupervised learning, reinforcement learning
(RL), deep learning (DL), deep reinforcement learning (DRL), and federated
learning (FL) [69].

####  III-D1 Supervised Learning/Unsupervised Learning

In supervised learning algorithms, both the input and desired outputs of the
used datasets are available. However, supervised learning algorithms can only
be employed in scenarios with sufficient labeled data, for example,
classification. In contrast, in unsupervised learning, the dataset used for
training does not include labeled output or target values. The purpose of
unsupervised learning is to extract key features of the data for better
prediction. Further, unsupervised learning algorithms can be applied to
scenarios, such as clustering based on the available data. In aerial
computing, supervised learning/unsupervised learning can be used for user
clustering/grouping to provide better service.

####  III-D2 Deep Learning

DL algorithms have been developed to deal with complex input–output mappings.
DL consists of multiple layers for feature extraction and transformation. DL
can be used for in-depth analysis in a complex scenario with massive data and
to realize different control schemes for different protocol layers. For
example, in [70], DL was used to classify cloud images captured by satellite
clouds in aerial computing systems.

####  III-D3 Reinforcement Learning

The main idea of RL is to train the agent to generate actions according to the
current environment. In RL, the problems are solved by employing a sequence of
actions that use the trial-and-error rule. RL algorithms have been extensively
used in wireless network optimization to obtain the optimal policy, for
example, user grouping decisions or actions. DRL is a DL framework developed
based on RL; it relies on updated samples in practice instead of the ideal
transition probability in theory. DRL involves learning from the feedback that
evaluates the actions taken rather than learning from the correct actions. DRL
algorithms have been applied to wireless networks for multiple aspects,
including mobile networking, resource allocation, schedular design, and
routing [71]. Furthermore, it can promptly make a decision under dynamically
changing network conditions, such as channel state information [72].

Figure 4: Applications of aerial computing for FL-enabled wireless systems
[2].

####  III-D4 Federated Learning

FL is an AI approach that enables users to collaboratively learn a shared
model with data maintained on their own devices [73]. In contrast to a
standard ML algorithm that requires a centralized training dataset in the data
center, FL allows devices to train a learning model locally and transmit the
training parameters instead. Thus, it addresses concerns, such as user privacy
and limited data transmission resources (e.g., bandwidth) [73, 62]. A
potential application of aerial computing for FL-enabled wireless networks is
aerial aggregation [74]; that is, a computing server (e.g., LAC, HAC, and
satellite computing) in aerial computing acts as the aerial server and
performs model aggregation in the air, as shown in Fig. 4(a)]. Moreover, as
shown in Fig. 4(b), aerial computing can extend the learning coverage of
terrestrial FL-enabled networks, particularly when certain obstacles prevent
model updating and broadcasting between FL users and the aggregation server.
Further, aerial components (e.g., LAC, HAC, and satellites) can be FL users,
which train local AI models and share updates with the central cloud [75].

####  III-D5 Big Data

The features of big data have been commonly described by five “V”s: volume,
variety, velocity, veracity, and value. Certain attempts have been undertaken
to apply big data analytics to wireless communications [76]. The increasing
complexity of networks and complicated wireless traffic patterns make big data
analytics appealing. ML algorithms coupled with edge computing can be used to
process big data in 6G [77]. MEC has become the primary computing method for
big data analysis in heterogeneous 6G.

AI and big data, combined with other enabling technologies mentioned in this
section, have been widely introduced to aerial computing in 6G owing to the
advantages of intelligent management and automatic self-improvement. In
particular, AI/ML can be used in intelligent UAV/drone trajectory design,
virtual resource management, data processing, task computation, and channel
estimation. In addition, AI and big data can be used in NFV and SDN-based
networks to achieve intelligent network management and optimization [78]. RL
approaches have also been conducted to improve resource utilization and
service provision [79]. Further, advantages of the other ML techniques, such
as meta learning and transfer learning, can be exploited. For example, the
benefits of transfer learning with knowledge preprocessing in LAC systems have
been demonstrated in [80]. However, major challenges should be answered to
further improve the effectiveness of transfer learning in heterogeneous aerial
computing systems. More specifically, we need to determine the source agent
and transferred knowledge, and optimize scheduling and resources.

Certain challenges need to be addressed to realize aerial computing in 6G,
including data security/privacy, learning efficiency, communication cost, and
the tradeoff between learning accuracy and convergence. AI/ML is recognized as
the most promising solution for providing intelligent wireless communications
and signal processing computation task control for increasingly complex and
heterogeneous networks.

Table III: Summary of enabling technologies of aerial computing. Category |  Enabler |  Advantages |  Key Challenges
---|---|---|---
Network Softwarization [54, 81, 55, 56, 82, 83] |  NFV |  High flexibility and low operational cost |  Service heterogeneity
VM |  Sufficient computing resources |  Intelligent management of virtual resources
Network slicing |  Flexibility, resource efficiency and Security |  Intelligent management and orchestration framework design
SDN |  High flexibility and low operational cost |  SDN controller placement
Energy Refilling [58, 59, 84, 61, 85, 63] |  Energy harvesting |  Ambient natural energy source |  Highly dependent on weather conditions
WPT (SWIPT and WPCNs) |  Harvest energy from radio transmission |  Trade-off between harvested energy and consumed power and joint resource allocation
Frequency Spectrum [66, 68] |  Sub-6 GHz |  High bandwidth and high data rate |  Severe attenuation and blockage and low range
THz |  High bandwidth and high data rate |  Severe attenuation and blockage and low range
C-Band K-Band |  Satellite communications and high throughput |  Atmospheric attenuation
Multiple Access Techniques [86, 87, 88, 89, 90] |  NOMA |  High spectral efficiency and massive connectivity |  Decoding complexity for multiple users
Massive MIMO |  High statistical multiplexing gain and high spectral efficiency |  High implementation cost, intelligent environment adaption and channel prediction
IRS |  High channel gain and low implementation costs |  Intelligent optimization of IRS
AI/Big Data Analytics [69, 71, 72, 73, 76, 77, 78, 79] |  ML |  Automatic features, predictions |  Computational complexity
DL |  Automatic features, predictions |  Computational complexity
RL |  Generate optimal decisions in a dynamic environment and require the Markovian model and high computation |  High dimensionality of the states and action spaces
FL |  Protects user data privacy |  Communication cost and tradeoff between learning accuracy and convergence
|  Big data
analytics |  Prediction of user preference distribution |  Feature extraction and data modeling

###  III-E Other Wireless Techniques

####  III-E1 Non-orthogonal Multiple Access

NOMA is considered a promising technology in 5G and beyond owing to its high
spectrum efficiency and massive connectivity. It is broadly classified into
two categories: power-domain NOMA and code-domain NOMA. Power-domain NOMA uses
the power domain for multiple access, whereas code-domain NOMA exploits sparse
code for multiple access. NOMA can support more users than the number of
available subcarriers, thereby improving wireless communication performance
with multiple features, including massive connectivity, low latency, high
spectral efficiency, and high energy efficiency.

  * •

First, as NOMA enables multiple users to be multiplexed on the same channel
simultaneously, it is suitable for a scenario with a large number of
connections (e.g., IoT applications). Therefore, NOMA can be applied in aerial
computing to support multiple devices/users to offload their tasks to aerial
servers, as shown in Fig. 3.

  * •

Second, compared with OMA where users must wait until a resource block is
available to transmit or receive data, NOMA can provide grant-free
transmission with flexible scheduling. Therefore, the offloading/downloading
delay of the devices/users in aerial computing can be significantly reduced.

  * •

Third, NOMA can achieve higher spectral efficiency and user fairness than OMA
[86]. Further, NOMA users can utilize the entire frequency bands for
transmission, whereas OMA users can only use a fraction of the entire spectrum
for communication. In aerial computing, high spectrum efficiency can also be
achieved in the communications between LAC/HAC platforms when using a high-
frequency spectrum [67].

####  III-E2 Massive MIMO

The concept of massive MIMO has been proposed to drastically increase data
rates, spectral/energy efficiency, and coverage of wireless networks by
aggressive spatial multiplexing. In massive MIMO, a BS equipped with a few
hundred antenna arrays serves tens of users simultaneously. It is considered a
promising technology in 6G owing to the following advantages [87].

  * •

Multiplexing gain: Aggressive spatial multiplexing can increase the data rate
dramatically in the spatial domain. Further, the offloading delay of the
computation task is significantly reduced for remote aerial computing.

  * •

Energy/spectral efficiency: The energy efficiency can be improved via massive
MIMO as it is inversely proportional to the number of antennas at the BS.

  * •

Increased robustness: Due to a large number of antennas, the propagation
channel provides additional diversity gains, which also increases the link
reliability. Thus, MIMO can provide reliable links in aerial computing, where
the uncorrelated noise and intra-cell interference can vanish with an
increasing number of antennas.

Various multiple access technologies, such as OMA and NOMA, can be combined
with massive MIMO to further improve the communication performance [91]. In
massive MIMO-OMA systems, the maximum ratio combing and zero-forcing can be
utilized to achieve a high spectral efficiency in underloaded systems.
However, some studies proposed massive MIMO-NOMA to support a large number of
users; that is, the overloaded system, and to meet the massive connectivity
requirement of 6G [89]. However, there exist challenges for the realization of
massive MIMO, which are the 1) deployment of extremely large antenna arrays,
2) adaptation to the intelligent wireless environment, and 3) limitation in
channel prediction. Equipped with full-dimensional large arrays, ground BSs
can apply adaptive fine-grained 3D beamforming to mitigate the strong
interference between high-altitude UAVs and low-altitude terrestrial users.

####  III-E3 Intelligent Reflecting Surface

IRS has been proposed as a promising technology in 6G to alter the wireless
channel by intelligently adjusting the amplitude and/or phase shift of each
element of the IRS [90]. Extensive deployment of IRSs in the wireless network
and smart coordination of the reflections can result in the flexible
reconfiguration of the wireless channels between transmitters and receivers to
expand the communication coverage and improve the wireless communication
capacity and reliability. IRS has various advantages.

  * •

Low cost: The reflecting elements of IRS are low-cost printed dipoles, which
only passively reflect the signal without transmitting the signal.

  * •

Full-duplex mode: Different from the traditional relay system with half-duplex
relay, IRS can work in a full-duplex mode.

  * •

Auxiliary device: IRS has great flexibility and compatibility to be integrated
into the existing wireless networks such as WiFi or cellular.

Thus, IRS can be massively deployed in wireless networks and combined with
other promising technologies, such as NOMA [92], massive MIMO, and UAVs.
However, IRS is still in its nascent stages, and certain potential challenges
need to be addressed: 1) The deployment of IRS, especially for air computing,
the location IRS can significantly affect the channel condition for data
transmission; 2) The reflection of IRS is sensitive to the angle of arrival,
which affects the channel model of the IRS in practice [90].

IRS can be applied in various scenarios to improve the communication
performance [68]. The most important task for aerial computing is to prolong
the battery life of UAVs, LAC servers, and IoT devices. Energy harvesting or
WPT techniques can be implemented using IRS to overcome the high-power loss
over long distances. For example, a highly efficient energy charging zone can
be created by deploying IRSs in the proximity of devices or UAVs. In addition,
if IRSs are deployed on UAVs, the UAVs can flexibly establish strong LoS links
with the ground nodes to improve the communication quality for aerial
computation task offloading and downloading.

###  III-F Summary and Discussion

In this section, we have presented a set of enabling technologies of aerial
computing. Table III presents a summary of the enabling technologies and their
advantages and challenges. These technologies can be flexibly implemented in
future 6G networks to efficiently provide fast computing services, better
mobility, and higher scalability and availability. Further, these technologies
can facilitate the process of addressing challenges, such as intelligent
communication and computation resource allocation design, joint optimization
of UAV trajectory and placement design, user association and grouping design,
and secure computing and communications. However, certain challenges need to
be addressed, as highlighted below.

  * •

Intelligent Control: The cooperation of enabling technologies should allow for
intelligence in aerial computing. The joint intelligent optimization of
computing resources (LAC, HAC, and satellite computing platforms),
communication link design with different multi-access techniques, and network
control/scheduling can be investigated in future studies.

  * •

Secure Control: For aerial computing, the user data need to be transmitted and
shared with the aerial server, which leads to security concerns and data
transmission overhead. Therefore, FL and blockchain can be exploited to
protect data privacy and improve security by transmitting the training
parameter to the server for global aggregation [93, 94].

  * •

Aerial Intelligence: AC, HAC, and satellite computing platforms can enable
aerial intelligence by collecting surrounding data and then executing onboard
intelligent algorithms. The aims of intelligent algorithms include autonomous
collision avoidance, adaptive flight gesture adjustment, and trajectory
optimization for data collection. Therefore, the design of intelligent
algorithms can efficiently support aerial computing in terms of energy
savings, large coverage, delay minimization, etc.

##  IV Vertical Domain Applications

Aerial computing is expected to serve different vertical domain applications
that require service provision from different perspectives. In this section,
we explore the impact of aerial computing on relevant domains: smart cities,
smart vehicles, manufacturing, and smart grids.

###  IV-A Smart City

The IoT involves all devices that are connected to the Internet on Earth. Due
to the potential of these devices in improving the quality of human lives,
they are sensitive to latency, storage, bandwidth, and security. With the
increasing popularity of the IoT, cities are becoming smarter, and the concept
of smart cities is gaining traction and a new dimension. Aerial computing can
aid in better understanding the potential of the IoT in developing new
strategies for smart cities, thus reducing costs and improving safety.
Furthermore, new endeavors, such as 5Gcity [95] and SynchroniCity [96], can
help realize the idea of a future city. According to [95], there are four
possible major themes in smart cities, which are data communication and
processing, MEC support, urban planning and management, and surveillance and
security. In Fig. 5, we provide an example of a smart city through the use of
aerial computing technologies.

####  IV-A1 Data Communication and Processing

With embedded multiple sensors, LACs, HACs, and satellite computing platforms
have great potential for sensing data in IoT environments and providing social
services to smart cities. In [97], the authors proposed a joint scheme of 3D
placement, computation, and communication resource allocation for multiple
HACs in an uplink IoT network, where the task distribution among HACs and
communication resources were considered. The solution scheme was developed
based on the K-means method, a Hungarian-based algorithm, and an iterative
method. The simulation results illustrate that the total transmission power of
the IoT nodes is significantly minimized through the proposed algorithm. In
[98], a technique referred to as AirComp was developed to receive data from
multiple sensors simultaneously by utilizing the superposition property of
multi-access channels, as shown in Fig. 5. A near-optimal equalizer was
derived using differential geometry to facilitate multimodal sensing
technology in the proposed technique. Finally, an efficient channel feedback
mechanism was designed to facilitate the acquisition of the entire channel
information from many sensors simultaneously. In [99], a novel and efficient
control algorithm for LACs was proposed to manage sensing and their movement
using DRL techniques. The proposed method extracts features using the
convolutional neural network (CNN) and infers decisions following the multi-
agent deep deterministic policy gradient (DDPG) method. However, swarms of
LACs and HACs may face difficulties owing to their limited storage and
computation capacities during crowd sensing. To resolve this issue, in [100],
edge/cloud computing technologies were introduced to the swarm for enhancing
their QoS. Based on a case study of latency-critical applications, their
simulation results illustrated that the proposed approach could effectively
improve the QoS of the swarm servicing entity. Furthermore, to enhance the
poor coverage of information-centric IoT networks, the authors of [101]
optimized the coverage of such a network via LACs and HACs at a low cost. The
proposed scheme was designed based on an improved rolling horizon strategy
(IRHS). Through comprehensive experiments compared to previous studies, the
authors demonstrated that the proposed scheme can improve the coverage ratio
(by 21.42%) and reduce the cost ratio (by 13.335% to 34.32%).

Figure 5: Illustration of a smart city using aerial computing.

Aiming to track a moving target using synthetic aperture radar (SAR) based on
compressive sensing technology, the authors of [102] considered applying the
multi-channel processing technique to the SAR computation unit. Because of the
joint consideration of the magnitude and phase, the proposed weight-adjusted
sparse algorithm in the computing unit could improve the performance of
clutter suppression. In another work [103], the same authors determined a
different method to track moving targets with the assistance of compensated
target motion in the SAR computing unit. The motion compensation procedure in
the SAR computing unit consisted of two steps: the SPECAN processing was first
applied to the frequency and azimuth domain, and then the correction of the
residual component was incorporated into the sparse imaging outcome. Further,
the authors also overcame the challenges associated with the motion of the
target and the imaging of multiple moving targets using a novel parametric
sparse imaging approach and an orthogonal matching pursuit method,
respectively.

####  IV-A2 MEC Support

In [104], a novel online HAC-assisted edge server management scheme was
proposed to provide flexible edge computing services. Several hot-spot areas
were extrapolated by geographically merging the tasks, and the corresponding
HACs were dispatched to the appropriate locations for the necessary service. A
simple illustration of this use case is provided in Fig. 5. Through extensive
simulation with real-world data, the authors showed that the proposed mobile
server dispatching scheme could serve more users with a high utilization of
resources. The authors of [105] proposed a fairness-aware task distribution
and trajectory optimization scheme in LAC systems, in which a fixed-wing LAC
played the role of a flying computing server for mobile terminals. Under
fairness consideration, the authors aimed to minimize the maximum energy
consumption of all mobile terminals. The non-convex problem was converted into
a convex one, and an iterative algorithm was proposed to solve the problem.
Further, under the assumption that the communication between the LAC and
mobile terminals followed the NOMA technology, the authors in [106] proposed
an iterative algorithm to minimize the maximum energy consumption of all
mobile terminals by controlling the trajectory, task data, and computing
resources in an adaptive manner. The simulation results illustrated that the
proposed algorithm could effectively reduce the highest energy consumption
among mobile terminals while considering the fairness issue. The work in [107]
introduced a highly diversified urban scenario, which was served by multiple
LAC servers. Two non-convex programming problems were sequentially formulated
to optimize the delay and energy performance of uplink and downlink
communication scenarios for AR applications, which were solved by convex
approximation techniques. Through experiments, the authors verified that the
proposed network model could serve cellular users in a satisfactory manner.

####  IV-A3 Urban Planning and Management

Extracting building anatomy through remote sensing technologies is crucial for
urban planning and management. Considering diverse scales and appearances,
assisted by HACs, the authors of [108] proposed an automatic method to extract
building instances. The proposed HAC unit consisted of an improved hybrid task
cascade method, which had three components for three different consecutive
tasks: high-resolution representation, definition of guided anchor, and
formation of focal loss. Comprehensive experiments on a real dataset revealed
that the proposed method was shown to perform better compared to the
conventional R-CNN method in terms of extracting building instances. As of the
LAC server, in [109], a target tracking algorithm to locate LAC-captured
targets, such as pedestrians and vehicles, was proposed using the sparse
representation theory. Upon implementation with real data, the results prove
that the proposed tracker can achieve better performance compared with
existing tracking algorithms. Further, to monitor urban environments, the
authors of [110] proposed the use of heterogeneous aerial computing (e.g.,
LACs, HACs, and satellite computing) and investigated a collaborative
methodology to share the workload. A simple illustration picture of this
collaboration feature is provided in Fig. 5. For a particular set of degrees
of freedom (e.g., a number of tasks and vehicles), an efficient algorithm was
proposed to maximize the overall weight of completed tasks. Through
simulations, the authors determined that the proposed algorithm significantly
outperformed the other heuristic algorithms.

With the growing trend of industrialization, air pollution has reached a
significant level in urban areas. Consequently, monitoring air pollution has
become a matter of great concern in recent years. To this end, the authors of
[111] presented an efficient and cost-effective air quality monitoring system
using LACs and HACs. The three major aspects of the system proposed in [111]
were data processing, deployment, and power control. The system was deployed
at Peking University and Xidian University, and about 100,000 samples have
been collected since February 2018. A partial illustration of this system is
provided in Fig. 5. Using a 360-degree panoramic camera in HACs, the authors
of [112] designed a novel air quality indicator (AQI) monitoring system,
AQ360, to detect the air quality level (shown in Fig. 5). Upon solving the
recognition problem of images, they optimized the placement of the
corresponding LAC to achieve the optimal outcome. Through the implementation
of the system under realistic scenarios, they showed that the system can
perform better compared to existing studies in terms of AQI recognition error
and energy consumption. Further, using microwave downlink sensor networks, the
authors of [113] studied a rainfall monitoring system via a satellite
computing platform. The proposed method was implemented in the satellite
computing unit, and it was compared with the Ligurian regional tipping-bucket
rain gauge (TBRG) network at a city in Italy. The performance of these two
types of monitoring systems was found to be similar, based on the rain events
that occurred over the time period between January 2017 and December 2018.

Fine-grained driving lines are crucial components of high-definition maps for
the smooth operation of autonomous vehicles. To this end, the authors of [114]
presented a semi-automated driving line generation method utilizing a mobile
laser scanning computing system, as shown in Fig. 5. Through simulations on
real datasets, the authors demonstrated that the proposed method achieved an
average recall, precision, and F1-score of 90.79%, 92.94%, and 91.85%,
respectively. Further, using the same mobile laser point clouds, the authors
of [115] proposed an image-translation-based method to obtain the 3D vectors
of typical road markings, as shown in Fig. 5). Another method for extracting
road networks using a satellite computing platform was proposed in [116],
wherein a novel aperiodic directional structure measurement (ADSM) technique
was adopted. In contrast, to facilitate city planning via swarms of fully
automated LACs, the authors of [117] proposed a holistic distributed framework
equipped with various effective and efficient skills, which could be adjusted
based on the requirements of the LACs and environments. Through experiments,
the authors demonstrated that a swarm of LACs equipped with the proposed
framework could effectively satisfy the requirements of diverse applications.
The authors of [118] presented an efficient framework for extrapolating urban
tree inventories by using 3D point clouds acquired by a HAC-borne laser
scanning system (shown in Fig. 5). The high-level processing steps of the
proposed HAC framework are individual tree cluster extraction, geometric
parameter estimation, and tree species classification. Through experiments,
the authors demonstrated that the detection accuracy of the roadside tree was
over 93% with an average error of approximately 5%, and the overall
classification accuracy was approximately 78%. Another application of the
point clouds collected by the computing system is the modeling of an
underground parking lot [119]. The method utilized in the HAC consists of two
parts: a joint localization and mapping algorithm based on sparse point clouds
and a semantic modeling algorithm. The results from comprehensive experiments
indicated that the proposed algorithm achieves centimeter-level accuracy with
a precision of 84.8%.

####  IV-A4 Surveillance and Security

The concept of a smart city is vulnerable to security and privacy issues since
the collection, dissemination, management, and processing of data in aerial
computing platforms can be interrupted by malicious users. Thus, evidenced by
certain existing reports, we discuss the way in which aerial computing
platforms (e.g., LACs, HACs, and satellites) could be the means of resolving
security and privacy issues in smart cities.

With the assistance of 3D simulation and a set of robot operating system-
equipped LACs, the authors of [120] demonstrated an autonomous navigation
system to control the physical security of smart buildings. The results from
the experiments indicated that the proposed approach achieves an acceptable
level of accuracy in terms of mapping the indoor environment of smart
buildings. In [121], a novel framework was proposed to monitor suspicious
links by utilizing the characteristics of LACs. Particularly, the system
consists of a suspicious transmission link and an LAC unit for monitoring.
Further, in the LAC system, the wireless resource allocation problems,
including trajectory planning and energy minimization, can be solved using the
popular successive convex approximation method.

Video surveillance applications are associated with several challenges in
smart cities, such as scalability, integrity, and latency. In [122], a series
of optimal scheduling and control algorithms were designed to address these
challenges. A network with full coverage was established via an LAC cluster,
and the scheduling problem was solved using the bi-objective fragile bin
packing technique. Thereafter, extensive simulations with realistic parameter
settings were conducted, and the effectiveness of the proposed scheme was
verified in terms of many systems and video-specific performance metrics. The
authors of [123] introduced a surveillance model for a multi-domain IoT
environment using heterogeneous smart HACs and LACs (shown in Fig. 5). A
heuristic method was used to minimize the maximum movement of LACs while
avoiding collisions among them. Extensive simulations were conducted
considering heterogeneous scenarios with multiple HACs and LACs to verify the
merits of the proposed method.

In addition to limited storage and computation power, security and privacy are
two of the alarming concerns for data dissemination schemes in aerial
environments. To this end, the authors of [124] presented a low-cost
computationally efficient short proxy signature (CB-PS) scheme for the LAC
server, which was oblivious to secret key information in conventional ID-based
schemes. To further enhance the security of such tasks, the authors of [125],
presented a novel blockchain-based strategy to facilitate multi-party
authentication among multiple LACs and HACs. Specifically, this strategy is
useful for P2P and group communications among LACs and HACs while ensuring the
efficiency of data dissemination. Through comprehensive experiments and
simulations, the authors verified the merit of the proposed technique in terms
of authentication among multiple LACs and HACs. Subsequently, to satisfy the
connectivity, data, and service demands of an exponentially growing number of
IoT devices in smart cities, the authors of [126] envisioned a 5G network
environment supported by blockchain-enabled LACs and HACs. The objective of
this network was to provide Internet connectivity to IoT devices via
blockchain-enabled multiple LACs and HACs and to facilitate decentralized
service delivery and routing facilities in a reliable and secure manner.

###  IV-B Smart Vehicle

Over the last 30 years, intelligent transportation systems (ITS) have been
investigated to improve the safety and efficiency of the transportation
industry. Along this line, IEEE developed a protocol called IEEE 802.11p
[127]. However, this standard is not sufficiently competent to meet the real-
time requirements of the emerging vehicular industry. Consequently, existing
studies have emphasized the merits of aerial computing for efficient ITS [128,
129] to achieve reliable and low-latency, smart, and seamless vehicle-to-
everything communication. Along with benefits, several research challenges
still exist in aerial vehicular computing, which can be overcome by leveraging
emerging technologies, such as IRS and AI. For example, [128] showed that AI-
enabled LAPs help improve the service performance of vehicular networks
compared with ones without AI and LAP deployment.

Furthermore, in recent years, the 5G Automotive Association (5GAA) defined a
new concept, namely cooperative intelligent transportation systems (C-ITS),
which considers aerial computing as an enabling technology for V2X
communications [130]. According to this standard, we highlight a few main use
cases in the context of C-ITS, such as traffic management, safety and
security, MEC provision, and infotainment. Fig. 6 illustrates a few
applications served by aerial computing platforms in smart vehicles.

Figure 6: Illustration of smart vehicles using aerial computing.

####  IV-B1 Traffic Management

With the growing number of vehicles in recent years, vehicle detection has
become a vital part of the ITS, which assists in resolving various information
and management issues, such as traffic and vehicle flow statistics and road
network management. By acquiring high-resolution images using HACs, the
authors of [131] designed a vehicle detection algorithm (shown in Fig. 6).
Implemented at the HAC server, the detection method considers sparsity in data
as well as multiple features. Further, through experiments, the authors
verified the utility of the proposed algorithm in terms of detecting a large
number of vehicles in metropolitan cities.

####  IV-B2 Safety and Security

Based on the data collected by the LAC server, the authors of [132] proposed
the Siamese Interaction Long Short-Term Memory (SILSTM) network to identify
the behavior of collision-prone vehicles. Upon learning the interaction
scenarios of a vehicle with its neighbors, the SILSTM network can predict
accident-prone interactions in advance. Subsequently, through extensive
experiments with a real dataset, the authors verified the merit of the
proposed approach in terms of detecting collision-prone trajectories at four
different intersections. A collision-prone scenario and its resolution are
illustrated in the intersection of Fig. 6. Traffic surveillance is one of the
core components of next-generation ITS. LACs or HACs can act as relays to
facilitate quick data delivery between vehicles and edge nodes, which is
associated with a high risk of information leakage. To this end, for the LAC
server, the authors of [133] proposed an optimization model based on a
probabilistic data structure (PDS)-based strategy with a triple Bloom filter
that can detect cyber threats of smart vehicles in advance.

####  IV-B3 MEC Support

During unexpected events, e.g., adverse weather conditions and extreme traffic
congestion, LAC/HACs can act as flying RSUs. Such a scenario was described in
[134], providing the required infrastructure for diverse traffic applications
for the improvement of the QoS (shown in Fig. 6). Through simulation, the
authors demonstrated that the proposed method could achieve satisfactory
performance in terms of network coverage and latency. In the context of social
Internet of Vehicles (SIoV) to strengthen social relationship among vehicles,
an LAC platform for SIoV, consisting of a three-layer integrated architecture,
was adopted in [135]. The aim of the architecture was to jointly optimize the
transmission power of vehicles and the LAC trajectory under realistic
constraints. Through simulations compared with existing schemes, the authors
demonstrated that the proposed architecture in the LAC platform could
effectively maintain social relationships among vehicles.

####  IV-B4 Infotainment

To disseminate information (e.g., news and entertainment), [136] proposed a
new LAC-based wireless access infrastructure using the RaptorQ-protected
content diffusion technique. A simple illustration of this function is shown
at the right-hand side of Fig. 6. Through experiments using real vehicles and
LAC platforms, the authors demonstrated that the RaptorQ-based content
dissemination mechanism in LAC platforms was highly efficient and cost-
effective in terms of transmitting information to multiple moving receivers
simultaneously. For the same purpose, in [137], a 3D LAC-based simulation
model was constructed using the OMNET++ simulator with the objective of
passing information from the LACs to cars. Various path loss and elevation
models were considered to render the simulation more realistic.

Disseminating data efficiently is one of the core tasks in V2X networks. To
this end, the authors of [138] proposed a novel LAC-based scheduling protocol
that consists of a proactive caching policy and a file-sharing strategy.
Subsequently, through simulations, the authors demonstrated that the proposed
scheduling strategy could enhance the performance of the system in terms of
throughput and latency. Further, to enhance the security of V2X
communications, in [139], a few physical layer security (PLS) strategies were
proposed for LACs and HACs. Comprehensive simulations were conducted, the
outcome of which revealed that the proposed strategies in LACs and HACs could
effectively handle many emerging security threats in V2X communications.

###  IV-C Smart Factory

Among the many enabling technologies, 6G, industrial IoT (IIoT) and aerial
computing have been integrated with cognitive skills and innovation to aid
industries in increasing production and delivering customized products more
quickly [140]. Such enabling technologies constitute Industry 5.0, an advanced
production model focusing on the interaction between machines and humans. This
collaborative work facilitates human capabilities, realizing higher
productivity and exceptionally easy automation for individuals and small
businesses. Such a feat was not possible a few years back. Here, we provide a
few examples of manufacturing models with the assistance of LACs and HACs, the
corresponding schematic of which is illustrated in Fig. 7.

Figure 7: Illustration of smart factories using aerial computing.

With the increasing trend of automation, IIoT has become prevalent in smart
factories for data communication and production management. Wireless sensor
networks (WSNs), the most important components of the IIoT, are used for
numerous tasks in indoor industrial environments, such as irrigation and the
inspection of machines. However, these networks have a low computational
capability. Therefore, in [141], a WSN-based safe navigation algorithm was
proposed for micro aerial robots (i.e., LACs) in the IIoT. A WSN with a 3D
ranger was used as a computational tool for micro flying robots to detect
obstacles in an indoor industrial environment, as shown in Fig. 7. Through
extensive simulations with multiple micro flying robots, the authors verified
the merit of the proposed algorithm in terms of obstacle-aware monitoring in
dynamic factory environments. Meanwhile, agriculture is becoming increasingly
automated with the development of robots and smart tractors. However, to guide
these machines in an automated manner, the accurate detection of crop rows is
crucial. In [142], based on images acquired by LACs, the authors proposed a
learning CRowNet model to detect crop rows (shown in Fig. 7). The
corresponding LAC server was equipped with a model formed using SegNet
(S-SegNet) and a CNN-based Hough transform (HoughCNet) technique. Through
extensive numerical simulations compared with traditional methods, the authors
demonstrated that the CRowNet model achieved 93.58% accuracy.

###  IV-D Smart Grid

Smart grids have been widely used in recent years. A large number of new power
lines have been under real operation, resulting in a significant growth of
transmission line mileage and power equipment. However, the monitoring,
detection, and maintenance of power transmission systems are essential to
ensure uninterrupted operations. Having noticed the risk of failure in
conventional manual monitoring methods, many researchers have been working on
the automation of power line inspection, monitoring, and management using the
latest technologies [143, 144], such as aerial computing, image processing,
AI, and DL. Most of the existing studies emphasize two crucial aspects of
smart grid automation: 1) monitoring and inspection and 2) control and
management. We focus on the computational aspect of aerial technologies in
performing these tasks. In Fig. 8, we briefly illustrate a few use cases of
the smart grid domain using aerial computing.

####  IV-D1 Monitoring and Inspection

Insulators are prevalent in high-voltage transmission lines and play a key
role in electrical insulation and conductor conjunction. Insulator faults
(e.g., glass insulators self-blast) pose a grave threat to power systems as
they can cause cascade failure. Considering the difficulty of manual
inspection, [145] proposed a new DL framework to detect the location of broken
insulators by capturing images using HACs (shown in Fig. 8). Specifically, the
proposed DL model in the HAC server works in the low-SNR regime with the
assistance of two modules: R-CNN to detect objects and U-net to classify
pixels. Using a real dataset, via experiments compared with the other methods,
the authors showed that the proposed approach achieves real-time accuracy.
After capturing images of transmission lines using HACs, [146] also proposed
an improved insulator defect detection method based on an improved ResNeSt and
region proposal network. Through suitable experiments on a real dataset, the
authors showed that the proposed method deployed in the HAC server can achieve
98.38% accuracy in detecting insulator defects. Further, aerial computing is a
feasible solution to inspect power transmission lines. Consequently, while
solving the battery limitation problem of aerial computing platforms (e.g.,
HACs and LACs), a new idea was proposed in [147], suggesting the use of a
smart hangar as an assistant to achieve full power automation, a path planning
model for the LAC server was developed. Through simulations, the authors
demonstrated that the proposed scheme in the LAC server is effective in
solving existing inspection problems.

With the growing popularity of wind power, more turbines are being deployed in
remote areas. [148] considered the deployment of HACs and LACs for inspecting
turbines (shown in Fig. 8). Using the forecasted wind conditions obtained by
the HAC-borne edge intelligence, two algorithms were developed to minimize the
flight time of the HAC and to optimize power generation by the turbine.
Through simulation using a real dataset, it was shown that the proposed method
can generate 44% more power and reduce the flight time by 25% compared to an
existing scheme. With the growth of rooftop solar PV arrays in power
generation, [149] designed a new system called SolarFinder, which can be
implemented in a satellite computing platform to detect distributed solar PV
arrays over an area in a cost-effective and autonomous manner, as shown in
Fig. 8. The intelligence of SolarFinder is built on the support vector machine
(SVM) model and a deep CNN approach jointly. Over 269,632 images captured by
satellite, the performance of SolarFinder was evaluated, and the results
showed that the pre-trained SolarFinder achieved a Matthews correlation
coefficient (MCC) of 0.17.

Figure 8: Illustration of smart grid using aerial computing.

####  IV-D2 Control and Management

According to the requirements of a smart grid network, controlling non-
technical losses (e.g., electrical theft) is one of the most alarming concerns
in developing countries. In [150], the authors discussed a possible
communication network and a framework using LAC platforms to control non-
technical losses (i.e., tampering of electrical devices) in energy
distribution systems. An example of this use case is shown in Fig. 8. For the
effective management of power distribution networks, which consist of numerous
sparsely distributed grids, LEO satellites were utilized as the key components
of the communication infrastructure to manage active networks [151]. Such a
scenario is shown at the left corner of Fig. 8. The effectiveness of the
proposed LEO satellite-equipped framework in terms of managing future power
supply networks was established through extensive experiments. In [152], the
feasibility of satellite communication for connecting a smart grid with
remotely located actuators was also studied. In addition to the routing and
channel access mechanism, the possible optimization of computation resources
was studied. In [153], images were captured using a satellite, and a hybrid
solar forecasting method was proposed to run the smart grid in a cost-
effective manner. For the core computing unit, desirable features and cloud
cover factors were extracted from the images using certain intermediate
mathematical formulae and a modified CNN, respectively. Coupled with
meteorological information and cloud cover factors, a final forecasting model
was developed.

Table IV: Summary of vertical domain applications.

|  References |  Use Cases |  Analytical Tools |  Relevant Lessons
---|---|---|---|---
Smart Cities |  – [97, 98, 99, 100, 101, 102, 103]
– [104, 106, 105, 107]
– [108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]
– [120, 121, 122, 123, 124, 125, 126] |  – Data collection, high-mobility multimodal sensing, control and management of sensing, enhancement of QoS in crowd sensing, improvement of information coverage, and tracking moving targets.
– Serving IoT devices with efficient resource utilization, task data
allocation, and trajectory optimization with/without NOMA, and serving as
cache-enabled edge computing nodes.
– Automatic extraction of building instances, target tracking, monitoring
urban environments, air quality monitoring, rainfall monitoring, semi-
automated driving line automation, building road networks, city planning,
extraction of urban tree inventory, and modeling an urban parking lot.
– Physical security of smart buildings, monitoring suspicious links, mitigating challenges of video surveillance applications, constructing surveillance models for multi-domain IoT, ensuring security in data dissemination, and trustworthy authentication. |  – K-means method, Hungarian algorithm, differential geometry, CNN and DDPG, edge/cloud computing, IRHS, sparse algorithms based on compressive sensing technology, and a novel parametric sparse imaging approach.
– Geographic merging of tasks, optimization algorithms, and convex
approximation.
– Sparse representation theory, collaborative workload sharing algorithm,
system development, placement optimization, satellite microwave downlink
sensor networks, inverse distance weighted method, a novel ADSM technique,
decentralized agent-based control architecture, classification-based method,
and SLAM algorithm.
– Jamming signal, an optimal scheduling algorithm, optimization, low-cost CB-PS scheme, and blockchain. |  – Minimize the total transmission power of IoT nodes, outperforms three existing schemes, improves QoS, improves the coverage ratio of information by 21.42%, and can track moving targets accurately.
– Provide energy-efficient AR services to mobile users under tight delay
constraints.
– Enhance performance compared with the mainstream Mask R-CNN method; achieve
good precision compared with state-of-the-art works; maximize the total weight
of completed tasks; achieve a lower AQI recognition error; has close agreement
with other existing methods; achieve an average recall, precision, and
F1-score of 90.79%, 92.94%, and 91.85%, respectively; achieve an overall
accuracy of 78%; and achieve centimeter-level accuracy with a precision of
84.8%.
– Achieve an acceptable accuracy for indoor environments, effectively monitor
a suspicious link, make video surveillance applications work efficiently,
enhance the security of the LAC server, perform authentication tasks
effectively, and proves that blockchain is effective in collaborative security
among multiple LACs and HACs.
Smart Vehicles |  – [131]
– [132, 133]
– [134, 135]
– [136, 137, 138] |  – Vehicle detection for traffic management and road network planning.
– Traffic collision detection and cyber threat detection.
– Flying roadside units, and social relationships among vehicles.
– Disseminating news and entertainment information among vehicles and sharing files in V2X. |  – ML technique.
– SILSTM network and a PDS-based scheduling technique.
– Algorithm and optimization tools.
– RaptorQ-based content dissemination mechanisms and optimized scheduling policies. |  – Achieve satisfactory performance in vehicle detection.
– Can detect collision-prone interaction trajectories and cyber threats
effectively.
– Can achieve full network coverage under different scenarios and optimize the
transmit power in vehicles and LAC trajectory.
– Can deliver infotainment service effectively.
Factories |  – [141]
– [142] |  – Monitoring industrial environments.
– Detecting crop rows in agriculture. |  – A navigation algorithm.
– A model formed with SegNet (S-SegNet) and HoughCNet. |  – Can navigate accurately while detecting obstacles.
– Can detect crop row with the accuracy of 93.58%.
Smart Grids |  – [145, 146, 147, 148, 149]
– [150, 151, 152, 153] |  – Detecting insulator defects in transmission lines, inspecting power transmission lines, inspecting wind turbines, and detecting distributed solar PV arrays.
– Controlling non-technical losses in energy distribution systems, managing active power distribution networks, connecting two power distribution networks, and forecasting solar energy for the smooth operation of smart grids. |  – DL and customized neural networks, optimization tools, and integration of an SVM model with deep CNNs.
– Design of a suitable communication network; integration of LEO satellites, optimization tools, and CNN-based approaches. |  – Achieve 93.58% accuracy, achieve 44% improvement in power generation and 25% reduction in flight time, achieve an MCC of $0.17$, which is $3$ times better than a pre-trained CNN-based approach, proves that a smart hanger can effectively solve the limited battery capacity problem of LACs/HACs.
– Can detect tampering of electrical devices, show that LEO satellites are
effective in managing remote power generation sites, and can forecast solar
energy for smooth operation.

###  IV-E Summary and Discussion

In this section, important vertical domain applications supported by aerial
computing, including smart cities, smart vehicles, smart factories, and smart
grids, have been discussed. A summary of the applications is presented in
Table IV.

Data communication, processing, and management of ground sensors and IoT nodes
are crucial tasks to facilitate applications in smart cities. Aerial computing
platforms (e.g., LACs, HACs, and satellites) are effective in performing these
tasks owing to their flexible deployment and LoS communication features in
air-to-air or air-to-ground links. However, even with the use of aerial
computing, several research challenges require to be solved to obtain optimal
performance, such as 3D deployment, joint computation and communication
resource management, coverage, and trajectory design. Several existing studies
have addressed these challenges by adopting cutting-edge techniques, such as
convex optimization, graph theory, and AI/ML tools. Because of security
breaches, smart city applications are associated with several challenges, such
as designing low-cost and efficient signature and authentication protocols.
Few studies have adopted the CB-PS scheme and blockchain to solve these
problems to a certain extent. Further, aerial computing is a promising
solution for realizing MEC with other communication technologies, which is
associated with several challenges as well, such as task data allocation,
computation and communication resource management, and aerial device
deployment issues. City planning and management are also crucial aspects of
smart cities. Certain useful tasks in the context of this use case are target
tracking to facilitate city design, air pollution monitoring, and city map
designs. In addition to providing certain functional requirements, aerial
computing may aid in achieving surveillance and security in smart cities. The
effective utilization of aerial computing is associated with certain
challenges, such as the HAC/LAC navigation strategy, deployment, and resource
management.

In the domain of smart vehicles, aerial computing platforms can be effectively
utilized for various purposes, such as traffic management, safety and
security, flying RSUs, and infotainment. Despite serving all these use cases,
several research challenges need to be solved, such as computation and
communication resource management, trajectory design of aerial computing
platforms, data analysis, and routing strategy. Several existing studies have
addressed these issues via AI/ML techniques, probabilistic approaches, and
optimization tools.

Finally, monitoring, inspection, and management are the primary applications
of aerial computing platforms in smart manufacturing and smart grid domains.
However, certain technical problems must be solved to obtain optimal outcome
from aerial computing platforms in these domains, such as obstacle-aware
monitoring a factory environment [141] and crop row detection (using ML tools)
in agriculture field [142]. Furthermore, a few more problems in smart grids
using aerial computing, such as the detection of insulator defects, management
of solar PV array, and inspection of power line and wind turbine, need to be
resolved. In terms of control and management, the main problems are electrical
theft detection, active and power distribution network management, and
forecasting solar energy for the smooth and continued operation of smart
grids, which can be addressed by equipping special hardware and software in
aerial computing platforms, AI/ML, and optimization tools.

##  V Challenges and Future Directions

In this section, we identify interesting research challenges and highlight
possible future directions for aerial computing.

###  V-A Energy Efficiency

In aerial computing, achieving sustainable energy management at LAC and HAC
platforms and energy-efficient satellite computing is a major concern. Data
communications and service delivery in satellite environments and space travel
with airplanes, require large energy resources to ensure network operations.
In addition, the power consumption of aerial components may vary depending on
the operation mode. For example, average power consumption of the UAV is
$8.2412$ W in the idle mode and $245.2815$ W in the horizontal movement mode,
and average power consumption is $8.2618$ W and $8.2637$ W when the UAV
performs communication transmissions with WiFi and GPS, respectively [154].
Thus, we need to focus on energy-efficient designs to realize sustainable and
green aerial computing. For example, the authors of [155] suggested a space-
air-ground architecture with a focus on maximizing the system energy
efficiency enabled by the joint optimization of uplink transmission power
control, sub-channel selection, and deployment of aerial relays. This can be
achieved by dividing the original problem into two sub-problems, optimal
subchannel selection and power control policy, which are obtained by available
aerial relay deployment. Moreover, energy refilling techniques to exploit
renewable energy resources can be useful for building sustainable aerial
computing systems. Aerial devices and satellites can harvest power from
ambient environments, such as wind, solar, vibration, and thermal power, at
the LAC platform to support their operations, e.g., data communications over
aerial links.

###  V-B Resource Management

Aerial computing accommodates data collection and computation tasks to serve
end IoT devices via different computing platforms (e.g., LAC and HAC) and
supports edge services in future 6G networks. Compared to terrestrial
computing infrastructures, such as cloud data centers and cloudlet, computing
platforms in aerial computing possess limited storage resources and battery
capabilities, which would hinder the deployment of aerial computing services,
such as airplane-based civil monitoring for terrestrial IoT environments
[156]. Thus, resource management strategies are urgently required to satisfy
seamless service delivery in space. Moreover, in the future 6G era,
intelligent aerial computing is expected to be a dominant research area, where
AI functions can be integrated into aerial devices at the LAC platform to
enable self-controlled and autonomous aerial systems. In this context,
training DL models on these aerial devices with massive datasets may be
infeasible because of the high demands of computation and memory resources,
particularly when training with large-scale audio and image data [157].
Therefore, optimizing on-device AI/DL models is of paramount importance for
solving the computational burden posed on aerial devices in the air.
Consequently, several solutions have been proposed to facilitate resource
management in aerial computing. For example, the authors of [158] considered a
resource management solution for aerial computing systems wherein a joint
optimization of task offloading, resource allocation mechanism, and the
trajectory of aerial devices in the air was derived with respect to
terrestrial users’ latency requirements. Thus, the energy of the aerial
devices was minimized, while a longer flight duration was achieved. Moreover,
it is also necessary to develop on-device learning solutions to support self-
learning aerial computing systems. For example, improved network architecture,
training optimization, and hardware design were used to accelerate on-device
data training [159]. A streamlined slimming framework was developed and
combined with a consecutive tensor layer to improve the training rates.
Simulation results show that the proposed method can enhance the training rate
by up to 30% compared to traditional approaches without compromising learning
accuracy. Thus, this approach provides opportunities for designing intelligent
flying devices in aerial computing.

###  V-C Network Stability

The topology of aerial computing fluctuates with the number of nodes, battery
levels, and varying communication conditions. The trajectories and speeds of
aerial components and flying devices also vary owing to the terrestrial
application requirements and environmental dynamics, e.g., different altitudes
of buildings in smart cities in the LAC platform and different orbital
altitudes of multiple LEO satellites. Further, the operational complexity and
unpredictability of aerial components in space can make the involved aerial
computing system unstable. In fact, aerial nodes can join and leave
unpredictably, or their flight can be stationary, slow, or fast. Thus,
achieving network sustainability in aerial systems is critical. As a promising
method, an optimal tracking policy was constructed in [160] for each aerial
device in an aerial-based network to mitigate the varying network topology
issues in aerial computing operations, e.g., flying trajectories of UAVs. The
key focus was on achieving an adaptive surrounding network configuration for
varying channel quality and communication bandwidth resources. Subsequently, a
particle swarm optimization algorithm was developed to optimally schedule the
energy allocation among a set of aerial nodes, while the prediction error of
the surrounding node locations was minimized. Another study in [161] built an
observer, which could monitor surrounding unmanned aerial nodes in an aerial
environment using Kalman filtering with respect to the maximum number of
parallel targets, measurement time, measurement success rate, and measurement
noise. Thus, the optimal measurement policy was obtained for network topology
monitoring, aiming to achieve an accurate prediction of trajectory topology in
autonomous aerial computing systems. Moreover, blockchain can help build
scalable aerial computing networks thanks to its decentralized feature for
enabling large-scale interconnection among servers and devices, e.g., unmanned
aerial components in the HAC platform [162]. Each aerial device works as a
blockchain node to build decentralized data services, such as data sharing,
data communications, and data storage, without the need for a central server
for better networking scalability.

###  V-D Large-Scale Network Optimization

In aerial computing, aerial devices operate on a large scale, and cooperative
optimization is needed to utilize the advantages from multiple and distributed
network datasets, such as diverse channel features and environment properties,
while improving the network quality. For example, performing an optimal
trajectory control policy for all manned and unmanned aerial components is a
challenge on the HAC platform if only the characteristics of an aerial device
are obtained [163]. More importantly, the datasets of future intelligent
aerial networks are distributed over large-scale networks rather than being
centrally located. Therefore, there is an urgent need for distributed and
large-scale optimization approaches to enable scalable and intelligent aerial
computing applications. A large-scale trajectory optimization solution was
proposed in [164] for the Internet of aerial devices, for example, UAVs and
balloons, through the use of a multi-agent DRL algorithm. This enables aerial
devices to collaboratively develop a distributed sense-and-send protocol in an
aerial computing setting, facilitating large-scale sensing and data task
transfer in cellular networks.

###  V-E Security, Privacy, and Trust

Although 6G-based aerial computing can offer global coverage and diverse QoS
provision to industrial applications, critical issues related to security,
privacy, and trust must be solved. Adversaries may attack aerial communication
channels and deploy data breaches in the flying BSs on the HAC platform, as
the management of lower altitude-based servers is limited owing to the
physical distance [165]. AI techniques are extensively used to enable
intelligent aerial computing, but they often require centralized data
collection for training, which raises potential privacy issues owing to the
exposure of sensitive information in the air. Moreover, the deployment of
satellite-terrestrial communications in space can face critical challenges
caused by untrusted environments, as third parties and attackers can
compromise the data exchange among aerial devices, BSs, and terrestrial IoT
users. Therefore, blockchain is a promising solution [166] for building trust
and establishing secure decentralized satellite-ground communications for
aerial computing systems. Blockchain, with its traceability and
trustworthiness, can provide enhanced security for aerial computing systems
[167]. The data from aerial communications, such as device location and
channel information, can be stored in the data ledger where blocks are built
based on immutable transactions. This makes the information in the blockchains
resistant to data modifications or threats. The elimination of centralized
authority of the blockchain also helps mitigate single-point failures in
involved aerial networks. In addition, smart contracts can be used as self-
executing software running on blockchain for providing automatic
authentication and verification to ensure reliable aerial communications
[166]. This technique attains further relevance in the 6G era because aerial
computing systems tend to be decentralized and deployed on a large scale,
which can be realized using the decentralization feature of blockchain. To
preserve data privacy in aerial computing, perturbation techniques, such as
differential privacy and dummy operations, are particularly helpful in
protecting data leakage against external threats during data exchange. For
example, differential privacy was adopted in [168] to realize privacy-enhanced
intelligent aerial communications by introducing artificial noise into the
trained gradients at aerial devices. This solution helps hide sensitive
information while guaranteeing convergence, such that adversaries cannot
retrieve useful data samples.

##  VI Conclusion

Edge computing has become an indispensable component of the present network
infrastructure, but it also has various limitations owing to the emergence of
new services and applications and the expansion of the network. We highlighted
the fact that the current computing infrastructure does not meet new demands
and requirements. In this regard, the concept of aerial computing was
introduced and comprehensively reviewed in this study. First, we presented an
overview of aerial computing, starting from the system architecture to the
reference model and fundamental features, and compared it with conventional
computing paradigms, such as cloudlet, fog computing, and MEC. Next, we
presented a set of key enabling technologies and discussed the use of aerial
computing in vertical domain applications. Finally, we discussed the research
challenges and promising future directions pertaining to aerial computing. As
the development of aerial computing is still in a preliminary stage and there
are many unexplored issues, we believe that this paper has revealed certain
important lessons and key ideas that will drive further research and unlock
the full potential of a comprehensive 6G computing infrastructure in the
future.

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