# Cloud-Enabled High-Altitude Platform Systems: Challenges and Opportunities

Khaleel Mershad, Hayssam Dahrouj, Hadi Sarieddeen, Basem Shihada, Tareq Al-
Naffouri,
and Mohamed-Slim Alouini Accepted for publication at the Frontiers in
Communications and Networks, Special Issue on Resource Allocation in Cloud-
Radio Access Networks and Fog-Radio Access Networks for B5G Systems.K. Mershad
is with the Department of Computer and Communications Engineering, Arts,
Sciences & Technology University, Beirut, Lebanon.H. Dahrouj is with the
Center of Excellence for NEOM Research at King Abdullah University of Science
and Technology (KAUST). (e-mail: hayssam.dahrouj@gmail.com)H. Sarieddeen, B.
Shihada, T. Al-Naffouri, and M.S. Alouini are with the Division of Computer,
Electrical and Mathematical Sciences and Engineering, King Abdullah University
of Science and Technology, Thuwal 23955-6900, Saudi Arabia. (e-mails:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>slim.alouini@kaust.edu.sa).Manuscript received
May 28, 2021, accepted June 28, 2021. Corresponding author: K. Mershad (email:
khaleel.mershad@aul.edu.lb).

###### Abstract

Augmenting ground-level communications with flying networks, such as the high-
altitude platform system (HAPS), is among the major innovative initiatives of
the next generation of wireless systems (6G). Given HAPS quasi-static
positioning at the stratosphere, HAPS-to-ground and HAPS-to-air connectivity
frameworks are expected to be prolific in terms of data acquisition and
computing, especially given the mild weather and quasi-constant wind speed
characteristics of the stratospheric layer. This paper explores the
opportunities stemming from the realization of cloud-enabled HAPS in the
context of telecommunications applications and services. The paper first
advocates for the potential physical advantages of deploying HAPS as flying
data-centers, also known as super-macro base stations. The paper then
describes various cloud services that can be offered from the HAPS and the
merits that can be achieved by this integration, such as enhancing the
quality, speed, and range of the offered services. The proposed services span
a wide range of fields, including satellites, Internet of Things (IoT), ad hoc
networks (such as sensor; vehicular; and aerial networks), gaming, and social
networks. For each service, the paper illustrates the methods that would be
used by cloud providers to offload the service data to the HAPS and enable the
cloud customers to consume the service. The paper further sheds light on the
challenges that need to be addressed for realizing practical cloud-enabled
HAPS, mainly, those related to high energy, processing power, quality of
service (QoS), and security considerations. Finally, the paper discusses some
open issues on the topic, namely, HAPS mobility and message routing, HAPS
security via blockchain and machine learning, artificial intelligence-based
resource allocation in cloud-enabled HAPS, and integration with vertical
heterogeneous networks.

###### Index Terms:

High-altitude platform system, super-macro base station, next-generation
wireless systems, cloud computing, cloud services, cloud-enabled HAPS.

##  I Introduction

Several types of aerial networks are being considered for complementing the
terrestrial communication infrastructures, which is emerging as one of the
major innovative initiatives of the next generation of wireless systems (6G)
[1]. Such compound networks, also known as vertical heterogeneous networks
(VHetNets) are expected to provide important services to ground systems, such
as enhanced connectivity, backhauling, exploration and surveillance, rescue
operations, traffic monitoring and control, etc. Some of these networks, such
as the unmanned aerial vehicle (UAV) and tethered balloon (TB) networks, have
already been used in several applications111https://share-
ng.sandia.gov/news/resources/news_releases/solar_balloons/ and projects [2]
[3] [4]. Other aerial networks, such as the High-Altitude Platform System
(HAPS), have been extensively researched and are currently being considered by
several projects for future
deployment222https://www.hapsmobile.com/en/333https://www.aircentre.org/projects/haps-
high-altitude-platform-station/. In this introduction section, we first
illustrate the importance of utilizing aerial networks as part of future
communication systems and the expected advantages. Next, we provide an
overview of the basic elements of this paper, which are HAPS and cloud
computing. Finally, we briefly describe the importance of integrating cloud
computing into HAPS, which we detail in the remainder of the paper.

###  I-A Overview

The constant growth of interest in high-speed wireless communications causes
the search for new solutions and new types of radio access networks. Although
more than half of the global population is already connected to the Internet,
there is still a need for higher broadband connectivity and telecommunication
services in suburban, rural, and isolated areas that remain underserved. In
addition, ground networks are susceptible to failures and degraded performance
in many situations, such as natural disasters, severe weather conditions, and
changing environments. For these reasons, several types of aerial networks are
being considered as a means to provide both stable broadband connectivity to
end-users and communication links between the mobile and core networks for
backhauling purposes. These two types of applications could open the way to
wireless broadband deployment in distant locations, such as mountains,
seaside, and desert areas. The HAPS is expected to be among the major networks
that will assist the ground infrastructure in future telecommunication
systems. With its wide footprint (500 km radius per node) and reduced round-
trip delay (0.13 to 0.33 milliseconds (ms)) [5], HAPS is ideal for low-latency
and mobile applications that require continuous connectivity, as it relieves
such applications from the burden of frequent handoffs that occur in ground
networks.

In addition, as next-generation communication systems emerge, new high-data-
rate applications become very prevalent. Consequently, network traffic has
been growing so fast that current backhaul networks will soon fail to handle
all traffic demands. A backhaul network provides connectivity between the
cellular base stations (such as 4G eNBs and 5G gNBs) and the core network; it
significantly impacts the performance of the whole network, and it is one of
the major challenges in beyond 5G (B5G) and 6G. Towards supporting the
backhauling requirements of future 6G networks, the development of a
supplementary backhaul network is considered vital. As one of the solutions, a
wireless backhaul network based on an aerial platform has been proposed by
several research projects [6, 7]. These backhaul networks could utilize one or
more types of wireless signals (such as radio frequency (RF), millimeter waves
(mmWaves), microwaves, lasers, and free space optics (FSO)) in order to
provide self-sufficiency, flexibility, and encompass a wide range of
application domains. In fact, Song et al. [6] outlined several advantages of
aerial-based wireless backhaul networks, namely, reduced cost, network
scalability, easiness to deploy in any area, and guaranteed line-of-sight
(LoS) propagation.

Another major advantage of HAPS is its ability to connect users in
disconnected or weakly connected areas, and to provide to these users the
various applications and services that are offered by public and private
ground networks. One of the domains that will benefit from a strong HAPS
constellation network is cloud computing. This is particularly the case since
the HAPS would allow the cloud to reach a wider range of users, enhance the
quality of service (QoS) of traditional cloud applications, and establish new
cloud services that benefit from the unique characteristics of the HAPS, as we
illustrate in this paper.

###  I-B Background

####  I-B1 Cloud Computing

Cloud computing (CC) is the process of supplying computing resources over the
Internet. Instead of maintaining their own computing infrastructure or data
centers, businesses can rent access to various services offered by cloud
providers, such as digital applications, storage, databases, networks, and
analytics tools. By utilizing these resources, companies can make use of any
computational assets they require, and during the period they need them,
without the need to acquire and sustain a physical, on-site infrastructure and
framework. This new computing paradigm provides several advantages to
customers, such as reduced costs, flexibility and efficiency, scalability, and
information sharing.

Cloud computing is based on the virtualization model. Virtualization gives CC
its unique characteristics, which are 1) on-demand self service, which allows
each consumer to individually setup and manage computing resources such as
server utilization and storage automatically without the need to contact the
cloud provider, 2) broad network access, which regulates the access to
facilities and resources that are available over the cloud network via
standard procedures, 3) resource pooling, in which software and hardware
computing resources are configured to meet the needs of multiple customers by
dynamically allocating and reallocating each resource based on the customers’
needs, 4) rapid elasticity, which allows the provider to dynamically add or
remove physical and virtual resources in such a way that they seem infinite to
the customer, and 5) measured service, in which the service provider manages
and optimizes the customers’ usage of resources by means of a resource
measurement technique that is suitable to the type of the provided service.

In cloud computing, many types of resources and digital applications can be
offered as a service (which led to the proliferation of the term XaaS, where X
is the name of the provided service). Services may be in the form of hardware,
software, storage, platform, infrastructure, database, and many others. The
National Institute of Standards and Technology (NIST) presented three major
categories of services, known as the CC service models, which are:

  1. 1.

Infrastructure as a service (IaaS), which allows the user to rent computing
capabilities, such as storage facilities, networks, processing power, and
virtual private servers, on-demand and over the web. These resources are
priced using a ”pay as you go” method where the price is computed based on
factors such as how much storage was required or the amount of processing
power that was consumed over a specific period.

  2. 2.

Software as a service (SaaS), in which the cloud provider offers and manages a
wide range of applications and software tools that the customers access and
execute over the web. SaaS frees the customer from many responsibilities, such
as the need for software maintenance, infrastructure administration, data
availability, information security, and all the other concerns related to
preserving the application in an active and operating state.

  3. 3.

Platform as a service (PaaS), which combines features from both SaaS and IaaS.
In this service model, the cloud platform provides the customer the
middleware, database management system, operating system, web server, and
development tools. These tools constitute a remote environment where users can
develop, build, and execute their software projects without the need to own
any hardware or software.

####  I-B2 High-altitude Platform System

A HAPS is a network of aerial stations that operate in the stratosphere at an
altitude of around 17-20 km. Triggered by technological advances in the fields
of communication systems, solar power systems, antenna arrays, and autonomous
flight, and encouraged by the thriving industry environment, the HAPS has
arisen as an essential constituent of future wireless networks [5]. Owing to
the distinctive characteristics of the stratosphere, a HAPS node can remain at
a quasi-stationary position. In addition, the HAPS node will be equipped with
a flight control system that enables the ground center to control its mobility
and direct it towards a certain location whenever needed. Note that a HAPS
node does not need a takeoff vehicle, i.e., it can move using its own power,
and it can be fetched back to Earth, upgraded, and redeployed.

Previous research on HAPS focused on utilizing this technology in the areas of
ubiquitous connectivity and disaster relief applications. Very recently, a new
model was proposed in which the HAPS is considered to be a practical potential
for future wireless communication networks [5]. Following the developments in
solar panel systems, HAPS nodes can utilize solar power that can be combined
with storage batteries as the primary methods for energy supply; HAPS stations
possess large surfaces that can house solar panel films. Moreover, because of
its low-delay characteristics compared to evolving satellite networks, the
HAPS can efficiently offer wireless services to the users of terrestrial
networks.

The HAPS can have encouraging benefits over other wireless system
infrastructures. According to [5], the low-altitude placement of HAPS, as
compared to satellite networks, enables them to make use of better channel
conditions and higher signal-to-noise ratio (SNR). In addition, the almost
stationary position of HAPS nodes allows full utilization of their
capabilities, preventing the existence of a significant Doppler shift and
enabling a long airborne duration with marginal energy consumption. Moreover,
due to its large volume, a HAPS node is appropriate for multiple-input
multiple-output (MIMO) and massive-MIMO antenna arrangements. When assisted
with multi-antenna arrays, the HAPS node can produce highly directional 3D
beams with narrow beamwidths that increase the signal-to-interference-plus-
noise ratio (SINR) for all users. Finally, due to its relatively low altitudes
(as compared to other satellites networks), the HAPS end-to-end delay is
estimated to be between 0.13 to 0.33 ms [5] which is favorable for delay-
sensitive applications. In general, with large antenna array deployments and
low power requirements, the HAPS can support a wide range of fixed and mobile
user terminals to satisfy various service demands [8].

When deployed, HAPS will be the third main layer of communications
infrastructure alongside terrestrial and satellite systems. HAPS networks
promise to offer wide coverage and large capacity to serve highly populated
suburban and rural areas, as well as isolated areas with weak connectivity,
hence supplementing the current wired and wireless infrastructure. Arum et al.
[9] explore the possibility of extending the achievable wireless coverage of
terrestrial systems by utilizing HAPS. The authors show that efficient
intelligent radio resource and topology management can mitigate inter-system
interference and ensure the coexistence of terrestrial systems and HAPS with
improved overall system performance. Hence, the ultimate goal of the HAPS
network is to enhance the performance and accessibility of current
communication systems. This is achieved by supporting a high capacity,
comparable to that provided by terrestrial systems, and a wide coverage area,
comparable to that provided by satellites. Therefore, HAPS networks are not
projected to substitute state-of-the-art terrestrial and satellite
technologies but rather to assist them in a collaborative and consolidated
fashion.

####  I-B3 Cloud-enabled HAPS

The previous subsection illustrates the critical role that the HAPS is
expected to play in future communications systems and applications. With its
position as a mediator between terrestrial and satellite networks and its
large footprint that connects multiple terrestrial and low-altitude networks,
the HAPS will become the communication hub connecting users to many networks
and systems. This provides an opportunity for cloud providers to use the HAPS
strategic position to reach a wider range of customers and improve the quality
of their services and their infrastructure capabilities. As mentioned in [5],
the HAPS station is expected to have a large size that enables it to operate
as an aerial data center. In addition to data and computational offloading,
which was pointed out in [5], the HAPS will enable cloud providers to deploy
and offer their services from within the HAPS data centers. From the one side,
the HAPS connects to users in rural and remote areas either directly or via
low-altitude gateways, which enables users in these areas to get access to the
cloud network via the HAPS. Examples of low-altitude gateways include low-
altitude aerial base stations and tethered balloons (TBs). From the other
side, the HAPS assists the cloud network in urban areas by providing
additional resources and capabilities to improve the QoS of cloud services.

TABLE I:  Comparison with sample recent overview papers on HAPS. Reference |  Focus |  Comparison with the current paper
---|---|---
Kurt et al.[5] |  Comprehensively surveys the literature on state-of-the-art HAPS networks and provides a future vision of unrealized potentials and the corresponding challenges. The paper addresses HAPS design, resource allocation, and topology management criteria. |  This paper describes on a high level the main concepts of a novel future technology, as opposed to surveying an existing topic. To the best of our knowledge, the interplay between HAPS and cloud computing has never been studied before. We promote applications that make use of the HAPS-cloud integration and discuss their corresponding methods and challenges. The exhibition in this paper is one step forward towards establishing innovative initiatives for flying networks towards 6G.
Arum et al. [9] |  Overviews HAPS prospects for service provisioning in remote areas and explores the corresponding techniques for intelligent radio resource allocation, topology management, and coverage extension.
d’Oliveira et al. [10] |  Surveys the HAPS technological trends at the time of writing from an aeronautical engineering perspective (not a communications perspective).
Qiu et al. [11] |  Envisions a software-defined integrated network architecture that is cross-layer, comprising high- and low-altitude platforms alongside terrestrial cellular networks. The paper further motivates the prospects and challenges of the corresponding use cases, and proposes a proof-of-concept case study.

We finalize this section by illustrating in Table I the differences between
this paper and previous survey/review papers on HAPS. Kurt et al. [5] describe
a future vision of a powerful HAPS network, in addition to its applications,
design requirements, and resource allocation methods. Arum et al. [9] explore
the corresponding techniques for intelligent radio resource allocation,
topology management, and coverage extension in HAPS. The authors of [10]
provide a comprehensive review of the HAPS technological trends from an
aeronautical engineering perspective. On the other hand, Qiu et al. [11]
propose a cross-layer integrated network architecture that comprises multiple
high- and low-altitude platforms alongside terrestrial cellular networks. For
an extended list of books and survey papers on HAPS systems, the reader can
check the references within [5]. Unlike these papers, the essential purpose of
our paper is not to provide a full survey or tutorial of HAPS infrastructure
and applications. Rather, to the best of our knowledge, our paper is the first
of its kind which overviews the various aspects related to the integration of
cloud computing services into the HAPS. The HAPS-cloud platform that we
propose in this paper is a one step forward towards establishing a framework
for offering cloud services from HAPS data centers, which promises to be a
vital area for a multitude of future research directions.

The paper next presents the opportunities that can be realized by integrating
various cloud services within the HAPS and the corresponding cloud-type
applications that would utilize the HAPS for enhancing the quality, range, and
type of offered services. The paper then presents some of the challenges that
need to be addressed for realizing practical cloud-enabled HAPS, mainly those
related to the high energy, processing power, QoS, and security
considerations. Finally, the paper sheds light on some open research
directions in the field, namely, HAPS mobility and message routing, HAPS
security via blockchain and machine learning (ML), artificial intelligence
(AI)-based resource allocation in cloud-enabled HAPS, and integration with
vertical heterogeneous networks. To the best of the authors’ knowledge, this
paper is the first of its kind to advocate for the use of cloud-enabled HAPS
from a holistic perspective. The paper presents the challenges and open issues
of such deployment, which promises to be an active area of research in the
context of 6G systems development and cross-layer optimization.

##  II Opportunities

###  II-A Traditional CC Services

####  II-A1 Overview

The number of user applications that utilize the cloud is increasing each day,
especially in the recent two years following the COVID-19 pandemic. The sudden
shutdowns of workplaces, institutions, and industries have increased the
demand for cloud applications and services. The COVID-19 pandemic has
highlighted the cloud opportunities to a large number of businesses that are
expected to strengthen their utilization of the cloud even after the pandemic
ends. A recent study by “Research and
Markets”444https://www.researchandmarkets.com/r/ys29pn shows that the CC
industry is expected to grow from $371.4 Billion in 2020 to $832.1 Billion by
2025. With this increase, a couple of important questions should be asked: 1)
What is the extent of cloud scalability? and 2) Will the cloud be able to
support the demand of users that reside in weakly connected areas?

Cloud scalability is related to the number of customers that a specific cloud
data center can serve. Several factors play an important role in scalability,
such as the computing, storage, and bandwidth demands of the user applications
that consume the services of the cloud data center. With the increase in the
number of heavy applications that demand high processing, storage, bandwidth,
or a combination of the three, the cloud is facing a scalability challenge. In
the near future, it is expected that many organizations and institutions such
as hospitals, universities, research centers, traffic control centers, etc.,
will be exploiting the cloud to run big data and AI applications that require
specific quality of service (QoS) constraints. In addition, the drastic
increase in the utilization of Internet of things (IoT) devices to collect and
make use of various sensors’ readings has increased the demand for high cloud
storage capabilities. Such applications will generate a huge amount of data
each second and require frequent and high-complexity computing power to
process this data continuously. With the increase in the scalability
requirements, cloud providers will look for more and better suitable places to
build cloud data centers that can support high processing complexity. The main
issue here is that the devices of a cloud data center devour vast amounts of
electricity that require a high-cooling process. The high-cooling demand of
cloud data centers has driven cloud providers to apply costly solutions, such
as deploying data centers deeply underwater or at the earth poles in order to
make use of very low temperatures at these locations.

The second issue is related to the ability of the cloud to provide satisfiable
QoS to user applications that are consumed at poor connectivity locations.
Currently, there is still a large percentage of areas worldwide that suffer
from poor Internet connectivity, especially in the developing and the least
developed countries [12]. Many cloud services cannot be consumed from these
locations because the QoS constraints of these services are not met in these
locations (such as minimum bandwidth, maximum latency, etc.). Hence, it
remains a challenge for cloud providers to provide their services with an
acceptable QoS to users that reside in poorly connected areas.

####  II-A2 Opportunities in Cloud-enabled HAPS

The HAPS network can provide solutions and answers to the two raised questions
with optimized design and implementation. First, the HAPS nodes can
extensively enhance the cloud scalability by hosting new flying data centers.
Exploiting the largely available physical space on the HAPS nodes to deploy
powerful data centers will help cloud providers upscale their services and
support more user applications. Since the atmospheric temperature at the HAPS
elevation is naturally low, in the range of [-15oC;-50oC] [5], there is no
need for too much energy for cooling. In addition, due to its location at a
high altitude, the HAPS node can provide coverage to a wide ground area
(around 500 km radius [5]). With LoS links between the user devices and the
HAPS node, the latter can offer cloud services with satisfiable QoS to users
in areas that suffer from poor ground connectivity. Moreover, the HAPS network
enables users to avoid several problems that typically occur when consuming
cloud services via ground connections, such as the possibility of
disconnection while offloading data due to poor channel conditions.

For these reasons, it is convenient that cloud providers deploy their services
within the HAPS data centers. In addition to the novel HAPS-specific cloud
services discussed in the following sections, we expect various traditional
cloud services to be deployed on the HAPS nodes. Such services include
platform as a service (PaaS), software as a service (SaaS), infrastructure as
a service (IaaS), storage as a service (STaaS), and security as a service
(SECaaS), among others. Offering such services to various users and businesses
further satisfies urgent demands for higher scalability and rapid new cloud
resources; it satisfies QoS requirements that ground cloud services fail to
achieve. Figure 1 provides an overview of the various HAPS cloud services that
we discuss in this paper and the environments in which they are offered and
consumed. These services are detailed in the next subsections.

Figure 1: Overview of the proposed services within the cloud-enabled HAPS.

###  II-B Satellite As a Service (SATaaS)

####  II-B1 Overview

Satellite services comprise a wide area of applications that can be
categorized into three domains: Telecommunications, broadcasting, and data
communications. Currently, there are more than 2,500 active satellites that
orbit around the Earth, and the number is expected to significantly increase
in the upcoming years. Several companies are investigating the use of large
low earth orbit (LEO) satellite constellations to improve their services and
reach more users; the HAPS network can be deployed as a middle layer between
the ground and satellite networks. One of the main advantages of HAPS, as
compared to LEO satellites, is their closer proximity to the ground level, and
so connecting to them becomes much faster. Therefore, a HAPS constellation-
based communication system can overcome the inherent high-latency problem of
satellite networks [5].

Recently, Microsoft announced Azure Orbital555https://azure.microsoft.com/en-
us/services/orbital/, a service that offers to satellite operators the
possibility to connect to and manage their satellite, process data, and
control satellite activities directly from the cloud. Similarly, Amazon ”AWS
Ground Station” provides several satellite-related services, such as weather
forecasting; surface imaging; communications; and video
broadcasts666https://aws.amazon.com/ground-station/. Yao et al. [13] present a
spatial cloud-based solution for analyzing and utilizing Big Earth
Observational Data (BEOD), which are images that reflect specific
characteristics in a time and space interval in the objective world. Kanev and
Mirenkov [14] describe the ”Satellite Cloud Computing” model, in which
virtualized information resources from satellites are integrated within the
cloud. The authors propose a general architecture of a cloud infrastructure
with direct access to satellite data, and discuss the possibilities for
bridging professional and home-based satellite receivers into an integrated
satellite cloud computing framework. Finally, the authors in [15] describe how
the computation, storage, and networking capabilities of cloud computing
enable natural hazard modeling systems that utilize Satellite Remote Sensing
to achieve accurate results in real-time.

####  II-B2 Opportunities in Cloud-enabled HAPS

One of the main applications that the HAPS cloud framework can offer is
satellite as a service (SATaaS) in which HAPS nodes act as the mediator
between ground networks (including unmanned aerial networks or UAVs) and the
satellite network. Current satellite services such as global navigation
satellite system (GNSS), telephone, broadcasting, and monitoring services
(weather, financial, environmental, etc.) can be offloaded from the satellites
to the HAPS data centers and then offered to customers with better QoS, such
as lower delay or better accuracy (for example, in localization services). In
particular, the HAPS data centers can host and execute the server-side code of
applications while continuously obtaining input data from various satellite
networks. Furthermore, cloud customers can consume these services from the
HAPS data centers using suitable CC partitioning and sharing models. In
addition, the HAPS infrastructure and equipment could be used to improve the
services whenever possible. For example, positioning sensors can be deployed
on the HAPS nodes to improve the localization accuracy (much higher resolution
than GNSS). Another important reason for offloading satellite services to the
HAPS is enabling delay-sensitive applications to obtain the required
information from the HAPS cloud service very fast, meeting the strict delay
requirements. Such applications cannot afford the delay of obtaining
information from satellites in traditional services. Therefore, deploying
satellite services on the HAPS cloud, as opposed to consuming the service
directly from the satellite network, allows application users to enjoy a
higher QoS.

###  II-C Sensor as a Service (SENaaS)

####  II-C1 Overview

A large number of IoT applications are currently offered as cloud services.
IoT devices utilize various sensors that continuously read data from the
environment and transmit it to IoT applications. Such IoT devices can be
connected to a local server or to the cloud via gateways. IoT cloud platforms
thus combine the powers of IoT devices and cloud computing in a single model
that delivers an end-to-end service. Currently, there are more than 20 billion
IoT devices connected to the Internet, and the number is expected to reach 38
billion in 2025777https://www.statista.com/statistics/802690/worldwide-
connected-devices-by-access-technology/; there is an increasing need for
gathering the big data obtained from such devices and processing it
effectively via various applications.

Cloud providers offer numerous IoT cloud platforms, hosting a wide range of
IoT applications in automation, home security, agriculture, environmental
monitoring, networking, etc. Examples of IoT cloud platforms include Oracle
IoT, Google Cloud IoT, SAP IoT, Cisco Jasper, Altair SmartWorks, Ubidots, and
SensorCloud. Also, there are special types of IoT cloud services utilized
during specific events or situations such as emergencies and natural
disasters. The latter usually use of advanced ML algorithms for predictive
analysis and fast recovery (IBM DRaaS, Bluelock, etc.).

####  II-C2 Opportunities in Cloud-enabled HAPS

The HAPS network is expected to offer new possibilities to current IoT cloud
platforms and open the way for new types of platforms. A group of IoT sensors
that monitor a particular place or environment could be connected to a HPAS
gateway that frequently aggregates the sensors’ readings and transmits them to
the HAPS node. This setup is similar to the current setup in wireless sensor
networks (WSNs), except that WSN gateways connect to the cloud via a base
station or a dedicated wired connection [16]. In some situations, it is hard
to secure a reliable Internet connection for WSN gateways, especially when
WSNs are deployed in rural locations, deep inside forests or deserts, or on
top of high mountains. In such cases, dedicating a robust LoS connection
between WSN gateways and HAPS nodes guarantees fast offloading of the WSN data
to the HAPS cloud, and hence better QoS to users. In addition, HAPS nodes are
expected to have high storage capacities [5] that help various types of WSNs
overcoming the limited storage problem in their local servers, thus enhancing
these networks’ applications performance. Furthermore, in scenarios where the
WSN location is not covered by any HAPS node, tethered balloons (TBs) can be
used to connect the WSN (and other ground users and networks) to the HAPS
network. TBs were proposed in several works as a means for connecting multiple
ground sources [17, 18]. As shown in Fig. 1, in the absence of HAPS
connectivity, TBs play an essential role in connecting the ground users and
networks to the HAPS cloud. Several TBs can be placed in rural areas to
connect to each other and connect via one or more TBs to the HAPS network.
This solution is economically more feasible than placing HAPS nodes in rural
areas.

HAPS can also open the way to new critical IoT cloud services. For example,
HAPS nodes can be equipped with various types of sensors that monitor the
environment and record the events that occur within the HAPS zone. The
readings of such sensors can be directly transmitted to the HAPS node cloud
server, processed by the ML algorithms, and then offered to the cloud users.
The distinctive advantage of these types of HAPS cloud applications is that
they allow the ground users to detect and/or predict important events deduced
from the HAPS sensors’ readings in real-time. Such features could help
emergency and disaster teams build new models for natural disasters based on
the sensor readings, which helps forecast future events more accurately. In
addition, the readings of the HAPS sensors help scientists understand and
discover new facts about the stratosphere. Fig. 2 illustrates the two SENaaS
scenarios explained in this section.

Figure 2: Two SENaaS scenarios: 1) collecting data from ground sensors and
sending them to the HAPS cloud via HAPS gateways or TBs, 2) monitoring the
stratosphere via environment monitoring sensors installed within the HAPS
node.

###  II-D Transportation as a Service (TaaS)

####  II-D1 Overview

The research on vehicular cloud computing has flourished in the last few
years, with a large number of papers proposing various models for integrating
intelligent transport system (ITS) applications into the cloud [19, 20, 21].
Such integration uses new features from the Internet of vehicles (IoV) to
provide different cloud solutions for transportation companies. The authors in
[19] provide a good overview of such ITS-based cloud services. In general,
transportation cloud services can be offered to several types of cloud
customers. For example, real-time vehicle tracking and vehicle health
monitoring services are offered to transportation companies to monitor and
manage their fleets of vehicles [20]. Furthermore, accident alert, road
navigation, e-ticketing, and passenger entertainment services are offered to
road passengers; smart parking services are offered to companies that require
automatic parking solutions for their parking lots; road traffic management
and traffic resolution services are offered to traffic control centers;
disaster/emergency management services are offered to various emergency-
related agencies and departments.

The actual implementation of vehicular cloud computing, however, is still
limited. Very few cloud providers currently offer ITS-related services within
their cloud platforms. For example, Amazon web services (AWS) provide several
transportation solutions to related businesses. AWS services include traffic
flow management, traffic congestion resolution, and disaster
recovery888https://aws.amazon.com/stateandlocal/transportation/. Similarly,
Google has recently started the connected vehicle platform as part of its
cloud IoT core with services such as monitoring driving behaviors, predictive
maintenance, and freight
tracking999https://cloud.google.com/solutions/designing-connected-vehicle-
platform. Netrepid101010https://www.netrepid.com/technology-
strategies/transportation/ also offers customized transportation solutions
based on the customer’s exact needs. These solutions include vehicle tracking
and managing routes (for railroad and airline companies). Nevertheless, the
number and range of vehicular cloud services are expected to increase in the
next few years with the appearance of smart cities and the need to manage road
data and operations in a fast and efficient manner. The latter opens the space
for the cloud to provide many new applications to the ITS sector.

####  II-D2 Opportunities in Cloud-enabled HAPS

The most important advantage that the HAPS network can offer to the vehicular
cloud is the ability to provide huge computational and storage capabilities.
Future HAPS architectures are expected to support data acquisition, computing,
caching, and processing in diverse application domains [5]. Several recent
works emphasized the fact that vehicular networks in future smart cities are
expected to generate continuous streams of big data that require continuously
increasing storage and very fast processing [22], [23]. Various ITS
applications could be partially or entirely offloaded to the HAPS network and
offered as cloud services to ITS administrators and customers. Consequently,
various vehicular network nodes could offload their data to the HAPS cloud via
multiple strategies. For example, some roadside units (RSUs) could be
integrated with HAPS communication modules to establish a high-speed LoS
connection to the HAPS network and support a high data rate for vehicular big
data offloading. These RSUs can be deployed in high locations such as the top
of buildings to enhance their connectivity with the HAPS network. Using such
setups, the vehicular network can make use of the ground RSU-to-RSU (also
denoted as infrastructure to infrastructure or I2I) connections to aggregate
and transfer various ITS applications’ data to RSUs, which in turn transmit
the data to the HAPS network. Another possible solution is to equip certain
types of vehicles that act as mobile gateways (such as buses) with HAPS
communication modules. Such strategies create fast connection lines between
the vehicular and the HAPS networks, enabling the former to use the latter’s
storage and processing capabilities. Fig. 3 illustrates four ITS applications
that can be offered to ITS customers via the HAPS cloud.

Figure 3: Illustration of four ITS applications that utilize the HAPS cloud to
offload data and offer services.

###  II-E Aerial Network as a Service (ANaaS)

####  II-E1 Overview

Like vehicular cloud computing, cloud computing services for UAV networks are
expected to become a hot research field in the near future. Some recent papers
proposed various frameworks for integrating UAVs with cloud platforms. For
example, reference [24] discusses a model for offloading the UAVs sensors’
data to cloud servers. Their framework mainly focuses on disaster and
emergency scenarios. In particular, a UAV node captures videos of its
environment and passes them to a context scheduler that analyzes the video
frames based on several context data such as location, spatial and temporal
data, and control commands in order to decide on the parts of the video data
that should be offloaded to a cloud server. The authors in [25] further
propose a combined cloud-fog architecture for UAVs in which several UAV nodes
are chosen as fog coordinators. Such coordinators collect data from other UAV
nodes, analyze and partition the data based on the application requirements,
process the data locally or cooperate with cloud servers to produce the
application results, and coordinate the operations of other UAV nodes to
satisfy the application demands.

The work in [26] outlines several cloud services that UAV networks can offer.
These services include UAV IaaS, which utilizes the UAV hardware components
such as payloads, sensors, actuators, internal memory, and processor. They
also include UAV PaaS, in which the client can make the UAV nodes read and
send specific data using their sensors or perform an action using certain
actuators. Furthermore, the UAV SaaS enables cloud users to request certain
UAV missions, such as commanding UAVs to spray crops for a specific
agricultural area. The authors in [27] outline various challenges that face
UAV cloud applications and scenarios. Such services involve managing the big
data generated by UAV nodes, optimizing the UAV operations for multiple cloud
services, maintaining the situation awareness of UAV nodes, ensuring the
reliability of UAV collected data, enabling scheduling to partition the cloud
service among UAV nodes, and avoiding various security attacks.

####  II-E2 Opportunities in Cloud-enabled HAPS

UAV nodes (drones and aerial BSs) usually connect to the Internet via ground
gateways in urban areas. However, in rural areas, seas, oceans, or deserts,
the drones might not have the means to connect via a ground gateway; the HAPS
nodes can act as the gateway. Similar to the discussion in the previous
section, we note that UAV nodes can be equipped with HAPS communication
modules that enable them to establish a LoS connection with a HAPS node.
Consequently, the UAV nodes with a HAPS connection can collect, filter, and
aggregate various data from other UAV nodes and offload this data to the HAPS
cloud data center. Cloud users then consume the UAV services by connecting to
the HAPS data center and executing the corresponding service. This setup
enables cloud users to connect to UAVs positioned in weakly connected or
disconnected locations. UAV cloud services enable the cloud users to analyze
the UAV sensors’ readings to monitor or study the UAV area and command the UAV
nodes to move to certain locations or perform specific tasks. In addition,
UAVs deployed in weakly connected or disconnected areas enable users to
connect to the cloud via the UAV-HAPS connection. Even in scenarios where the
UAV has ground connectivity, UAV applications that require large storage and
processing overheads can still offload their data and code to the HAPS cloud
instead of the ground cloud due to the advantages of the former, such as
better connection and channel conditions, lower latency, etc. These advantages
mostly appear when the UAVs encounter obstacles (emergencies that affect the
ground infrastructure such as fire or floods, for example) or are deployed in
obstructed areas. Fig. 4 illustrates two examples in which the HAPS cloud can
assist the UAV nodes in offering cloud services.

Figure 4: Two scenarios in which the UAV network can make use of the HAPS
cloud: 1) rural locations where ground connectivity is not available, and 2)
emergency situations where ground connectivity is weak or off due to unusual
conditions such as natural disasters.

###  II-F Routing as a Service (RaaS)

####  II-F1 Overview

Routing in wireless networks is the process of calculating the path that a
packet should follow when sent from a source to a destination node. Different
types of wireless networks require different routing protocols and strategies
that best suit the characteristics of a network. For example, WSNs and IoT
networks utilize multi-hop routing paths to forward a packet from one sensor
node to another until the packet reaches one of the network gateways.
Vehicular networks may depend on fixed infrastructures such as roadside units
connected to the Internet to help in the routing process. Towards this end,
two main types of communications, namely, vehicle-to-vehicle (V2V) and
vehicle-to-infrastructure (V2I), could be used in vehicular routing protocols.
Similarly, UAVs rely on UAV nodes equipped with ground communication modules
to connect to ground gateways. Since only some UAV nodes could have ground
connectivity, the remaining nodes need to find multi-hop paths, connecting to
one or more ground-connected UAV nodes to route packets to ground networks.

Although different routing protocols exist for different wireless networks,
the next generation of routing protocols will arguably utilize network
virtualization techniques such as software-defined networking (SDN) and
network functions virtualization (NFV) for better routing efficiency. Several
SDN-based and NFV-based routing protocols have already been proposed for
various wireless networks. For example, the authors in [28] and [29] propose
SDN-based routing protocols for WSNs and large-scale IoT networks,
respectively. The SDN-based routing protocols for WSNs outperform other
protocols in terms of energy efficiency and reducing the power consumption of
WSN nodes [28]. Furthermore, the SDN routing protocol in [29] outperforms
other routing protocols for IoT networks in terms of supporting higher network
scalability and larger-size networks. The works in [30, 31, 32] prove that
utilizing the SDN combined with RSUs is more efficient than traditional
routing methods for vehicular networks. Finally, the authors in [33] and [34]
propose two similar SDN-based routing protocols for UAVs in which multiple
paths are created between two UAV nodes. These protocols focus on selecting
the routing strategy according to the required QoS parameter, such as
selecting the shortest path to decrease the end-to-end delay or sending the
packet on multiple paths to increase the packet delivery ratio.

####  II-F2 Opportunities in Cloud-enabled HAPS

Future wireless network routing protocols can utilize HAPS-deployed SDN
controllers to process routing data from network nodes, generate routing
decisions (such as OpenFlow rules), and send routing updates to wireless
network nodes (to update the routing tables). Such protocols have two main
advantages compared to traditional ground-based SDNs. The first advantage is
that the HAPS nodes can cover a wide ground area (footprint). Consequently,
wireless networks that do not have a fast connection to a ground cloud server
with high processing capabilities can switch to using the HAPS cloud for its
high processing capacity and fast connection. This is especially true for very
large wireless networks that contain tens or hundreds of thousands of nodes,
such as huge WSNs or IoVs that generate continuous streams of routing data
that should be sent to the SDN controller. Note that there should be an
efficient filtering and aggregation procedure in such networks to enable the
wireless network nodes to filter redundant data and group important data
before sending them to the HAPS SDN controller.

The second advantage of such protocols is related to the fact that SDN
switches require stable and reliable connections to the SDN controller, which
is something that cannot always be guaranteed in current SDN architectures for
wireless networks. Many reasons cause connection loss in these networks, such
as high interference and/or noise, the existence of blocking objects (WSNs
that are deployed in rural and environment-changing areas), and high mobility
of highway vehicles which does not leave vehicles enough time to send packets
to the RSU. Suppose these nodes are equipped with a HAPS communication module
or connected to other nodes with HAPS communication modules (such as TBs). In
that case, we can avoid the network isolation problem that occurs when one or
more SDN switches cannot reach the SDN controller.

###  II-G Other Cloud Services

In addition to the cloud services mentioned in the previous sections, other
services are expected to be implemented and offered within the HAPS cloud as
the HAPS network matures and more HAPS constellations are built. We provide
some examples of cloud services that could make use of a powerful HAPS
architecture to provide a high QoS in this section.

####  II-G1 Mobility Management as a Service (MMaaS)

A vital cloud service related to mobile wireless networks that the HAPS cloud
system can offer is mobility management as a Service (MMaaS). Mobility is an
important characteristic that provides great advantages to networks, but at
the same time creates several problems, such as dynamic topology and
intermittent connectivity, complex modulation and coding, handoff, road
traffic congestion (in vehicular networks), etc. Several research works
proposed mobility management systems for mobile networks. For example, the
authors of [35] propose a mobility model that is implemented within a
vehicular cloud platform. The proposed model makes use of free flow and
queuing-up movements of vehicles to suggest an alternate route for the driver
which is free from traffic. Another model for calculating and managing the
trajectories of drones in UAVs is proposed in [36]. Here, the drone routes are
estimated via the global GPS reference system with supporting GIS mapping,
coupled with autonomous conflict detection, resolution, safe drone following,
and formation flight options.

With HAPS-enabled MMaaS, a mobility management cloud server can execute ML
algorithms that analyze various data to be sent from the mobile nodes to the
HAPS cloud, such as geographic location, movement direction, speed, and
acceleration. The server can then build a movement profile for each mobile
node by analyzing the mentioned parameters during a certain time period; such
data can be used to predict future movements and network changes. The server
can dynamically re-execute the ML algorithms when the inputs change (upon
nodes entering and leaving, speed and acceleration changes, weather changes,
etc.). MMaaS can overcome many of the problems mentioned above by producing a
future movement trajectory for each mobile node to guarantee that the network
continues to operate normally. For example, if the nodes’ movement profiles
indicate that part of the WSN will be isolated from the network if the nodes
retain their movement pattern, the MMaaS can adjust the future movement paths
of one or more nodes to guarantee that no sub-network isolation occurs.
Another example is vehicular traffic control. Although many efforts have been
made in road traffic control and traffic congestion avoidance, road traffic is
still causing huge problems, especially in high-urban areas. HAPS nodes can
combine the data that we mention in this paragraph with high-resolution images
that they can take and that show the exact concentration of vehicles on the
roads, the exact locations of accidents, and the existence of temporary
obstacles on certain roads. When combining and analyzing all this data, the
HAPS nodes can predict the future traffic patterns of the vehicles and advise
each driver to follow a specific path to resolve expected future traffic
congestion and balance the traffic on all the paths to a particular
destination.

####  II-G2 Gaming as a Service (GaaS)

Cloud gaming has been drawing increasing attention in the cloud and game
industries. Several research works discussed the benefits of cloud-gaming
platforms in improving the performance of end-user games and creating a
network architecture for a high QoS multiplayer cooperative cloud gaming [37,
38, 39]. With HAPS in the picture, cloud gamers’ devices can be equipped with
high-speed HAPS communication modules that allow the gamers to connect to the
HAPS cloud from their devices. Using this setup, players can run the front-end
of the game on their devices, and the devices could connect to the back-end of
the game application on the nearest HAPS node. In addition, the HAPS nodes can
collaborate to run the games in parallel between large groups of gamers and
balance the load within the HAPS network. This setup enables gamers to play
various single and multiplayer games that are hosted on the HAPS cloud.
Several cloud platforms, such as Vortex111111https://vortex.gg/;
Shadow121212https://shadow.tech/; and Amazon
Luna131313https://www.amazon.com/luna/, are currently offering similar
services on the ground cloud. The main advantage that HAPS offer to cloud
gamers is that each HAPS node has a large footprint, allowing mobile gamers to
guarantee a stable connection to the same HAPS node for a large period of time
and avoid the frequent handoff problem that decreases the QoS of the current
cellular systems.

TABLE II: Summary of the proposed HAPS-cloud services. HAPS Cloud Service |  Existing Services |  Advantages |  Limitations
---|---|---|---
SATaaS |  • Azure Orbital • AWS Ground Station • Spatial Cloud for Big Earth Observation Data [13] • Satellite Cloud Computing [14] • Cloud-based natural hazard modeling systems [15] |  • Lower delay • More accuracy • Faster detection and response (natural hazards) • Better quality (media and TV) |  • Less coverage per node • Frequent maintenance and update requirements • Possibility of disconnection to the HAPS network
SENaaS |  • Oracle IoT • Google Cloud IoT • SAP IoT • Cisco Jasper • Altair SmartWorks • Ubidots • SensorCloud |  • Ability to deploy in rural and isolated areas • Monitoring the environment at the stratosphere layer • Meeting the time requirements of delay-sensitive applications |  • Requiring dedicated HAPS gateways • Requiring TBs in areas not covered by the HAPS network
TaaS |  • AWS Automotive • Google Connected Vehicle Platform • Netrepid Transportation Cloud • AzureITS |  • Computational and storage capabilities for ITS big data applications • LoS connection • Ability to consume services from mobile devices • Few HAPS nodes can cover the whole vehicular network |  • Requiring dedicated HAPS gateways • Disconnections in underground and hidden areas
ANaaS |  • Disaster Sensing [24] • Object recognition [25] • UAV IaaS, UAV PaaS, UAV SaaS [26] • Drone as a Service [27] |  • Ability to deploy in rural and isolated areas • LoS connection • Connecting users in ground-disconnected areas or conditions • Computational and storage capabilities for UAV big data applications |  • Requiring dedicated HAPS gateways • Requiring TBs in areas not covered by the HAPS network • Higher End-to-End delay between the user and the UAV (in some cases)
RaaS |  • Energy-Aware Multihop Routing Protocol (EASDN) [28] • Routing Protocol for Low-Power Lossy Networks (RPL) [29] • Secure Routing Protocol for Internet of Vehicles (SURFER) [30] • SDN-based UAV (SD-UAV) [33] |  • Ability to group all routing data from a huge network at a single HAPS node • Stable and reliable connection between routing switches and controller |  • Requiring dedicated HAPS gateways • Requiring TBs in areas not covered by the HAPS network • Disconnections in underground and hidden areas • Limited physical space within the HAPS node to support routing hardware
MMaaS |  • Free-flow model, Queuing- up model [35] • Trajectory-following model, Drone-following model [36] |  • Computational and storage capabilities for traffic and mobility big data applications • Meeting the strict delay requirements of mobility management applications |  • Requiring dedicated HAPS gateways • Requiring TBs in areas not covered by the HAPS network • Real-time connection is not always guaranteed
GaaS |  • Vortex • Shadow • Amazon Luna |  • Meeting the strict delay requirements of gaming applications • Stable LoS connection • No Handoff |  • Requiring dedicated connection between the user device and the HAPS node • Disconnections in underground and hidden areas
SNaaS |  • Social Cloud [40] • Healthcare cloud services [41] • Social network educational services [42] • Multimedia as a Service [43] |  • Lower delay • Computational and storage capabilities for SN applications • Connection to ground SN via the HAPS |  • Requiring dedicated connection between the user device and the HAPS node • Disconnections in underground and hidden areas
CSaaS |  • Crowdsourcing Reference Model [44] • Crowdsourcing Social Enterprise Service [45] |  • Computational and storage capabilities for crowdsourcing applications • Ability to reach users in disconnected areas |  • Higher delay than traditional crowdsourcing applications (in some cases)

####  II-G3 Social Network as a Service (SNaaS)

Social networks (SN) have seen enormous expansion, with millions of Internet
users actively engaging across various social networking platforms. Many
businesses have started utilizing social networks as a means to promote their
services, reach new customers, and enhance their connections with current
customers. Instead of linking people based on social relations, businesses
create interactive societies that group persons based on common business
desires or practices. The social cloud is proposed in [40], enabling cloud
users to share heterogeneous resources from multiple cloud services within the
context of a social network. The social cloud comprises a social marketplace
that utilizes both social and economic protocols to manage the sharing of
resources and facilitate trading between users. In general, social networking
cloud services have been used in the context of many fields, such as
healthcare ([41]), education ([42]), sharing of cloud resources ([46]), and
media ([43]). In the future, HAPS nodes can host cloud data centers dedicated
to social network cloud applications. The HAPS social network data center
could include servers for implementing and managing the SN security, storage,
applications, communications, backup, policies, etc. Similar to what we stated
in Sec. II-G2, the devices of the SN users would use a dedicated HAPS
communication module to connect to the HAPS network, log in to the SN, and
consume the desired services. Each HAPS node will connect to other HAPS nodes
and to the ground data centers of the SN to enable its users to utilize
various resources within the SN. The HAPS SN can offer several advantages as
compared to current cloud social services, such as extra computational power,
lower user-to-cloud and user-to-user delays (most probably in rural and weak
connectivity areas, where users can connect to the HAPS either directly or via
TBs), and better channel conditions.

####  II-G4 Crowdsourcing as a Service (CSaaS)

Crowdsourcing is the practice of involving a group of people for a common
target, such as voting, sharing information or opinions, or performing
specific tasks via websites, social networks, and smartphone apps. It enables
businesses to farm out work to individuals anywhere around the globe, and
hence get access to a wide collection of skills and abilities without
suffering the expenses of in-house employees. The new concept of crowdsourcing
as a service enables companies to leverage cloud users and resources to
execute crowdsourcing activities. The work in [44] describes a reference model
for cloud-based crowdsourcing. Furthermore, the work in [45] proposes a system
that allows social network users to post ideas, share resources, and engage in
crowdsourcing projects, enabling businesses to seek crowdsourcing
opportunities, collect, connect, and analyze users’ data and resources. A
similar framework in [47] regulates various operations within the
crowdsourcing service, such as framing the crowd challenge, dealing with IP
rights, managing the crowd, defining and handling incentives, and correctly
integrating the vast input into innovation projects.

When the HAPS network matures, and people start to have HAPS communication
modules within their devices, services such as CSaaS will be offered from the
HAPS cloud and will become desirable for many companies. For example, when
deployed in the HAPS cloud, the CSaaS enables businesses to create and host
their crowdsourcing projects and lists within the HAPS data center, thus
assisting individuals in participating in crowdsourcing tasks or subscribing
to crowdsourcing lists. CSaaS could be implemented as both a push and a pull
service. In some projects, the organization can select one or more
crowdsourcing lists to send crowdsourcing tasks, such as voting, answering
questions, or filling forms, with corresponding incentives (push service). In
other cases, any subscriber can select a crowdsourcing task or project to take
part in (pull service). The CSaaS subscriber can receive the crowdsourcing
tasks via email or a social media message, and he/she can then reply to the
crowdsourcing server with his/her credentials to receive the incentive.
Organizations collect and analyze answers by utilizing data analytics tools
within the CSaaS. HAPS nodes provide the middleware between crowdsourcing
organizations and users by hosting lists, tasks, and answers, providing
connectivity and message transfer, and executing applications.

TABLE III: Requirements for the various HAPS cloud services. Requirement |  Services
---|---
High-speed connection between the HAPS node and the service source/consumer |  IaaS, SaaS, PaaS, SATaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS
Reliable connection between the HAPS node and the service source/consumer |  IaaS, SaaS, PaaS, SATaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS, CSaaS
High data-rate connection between the HAPS node and the service source/consumer |  IaaS, PaaS, SATaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS
Secure connection between the HAPS and the service source/consumer and secure processing/storage within the HAPS node |  IaaS, SaaS, PaaS, SATaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS, CSaaS
Dedicated HAPS gateway that connects the service’s source network to the HAPS |  SENaaS, TaaS, ANaaS, RaaS, MMaaS
Dedicated communication module for connecting the HAPS to specific networks such as satellites or TBs |  SATaaS, SENaaS, ANaaS
Dedicated connection between the consumer’s device and the HAPS |  GaaS, SNaaS
Sufficient physical space within the HAPS node |  IaaS, PaaS, GaaS, SNaaS
High processing capability by the HAPS |  SaaS, PaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS
High storage capacity within the HAPS |  SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS, CSaaS
Intra-HAPS collaboration between the HAPS nodes to execute/offer the service |  SATaaS, ANaaS, RaaS, MMaaS, GaaS, SNaaS, CSaaS
Tethered Balloons (TBs) for connecting the service source/consumer with the HAPS |  SENaaS, ANaaS, RaaS, MMaaS
Specialized hardware installed within the HAPS nodes for service-specific requirements (such as sensors, SDN controller, etc.) |  SATaaS, SENaaS, RaaS
Support for real-time data transmission and sharing |  SATaaS, SENaaS, TaaS, ANaaS, RaaS, MMaaS, GaaS

A comparison between the services that we propose in this section for the
HAPS-cloud platform and similar services that are offered in traditional or
mobile cloud platforms is depicted in Table II. For each service described in
this section, we state in Table II the existing similar services, the
advantages that the proposed service will have and its limitations as compared
to the existing services. In general, the HAPS-cloud platform will have unique
features that will distinguish it from existing systems. The line-of-sight
connection that will exist between the ”data source”/“consumer’s device” and
the HAPS node makes the speed of offloading/consuming the application very
high, which is significantly important for many cloud applications. In
addition, the ability to deploy HAPS nodes over isolated and extremely remote
areas enables the cloud providers to reach new cloud customers and opens the
way for new types of cloud services. Moreover, the large footprint of a HAPS
node makes it cover a wide ground area, which enables it to manage networks
that span an enormous geographical zone and allows the nodes in these networks
to make use of the computational and storage capabilities within the HAPS node
to execute big data computationally-extensive applications (such as routing,
ITS, social networks, etc.). Finally, the strategic location of the HAPS and
its ability to communicate with several types of networks makes it ideal for
connecting cloud users in these networks together.

We conclude this section by describing in Table III the various requirements
that are essential for the successful deployment of the proposed cloud
services within the HAPS. For each requirement, we state the services that
will crucially need the requirement. Some requirements, such as the first four
in the table, are very important for most cloud services. Other requirements
are needed by specific services. For example, only GaaS and SNaaS require a
dedicated connection between the user’s device and the HAPS node. This is due
to the fact that these services are mostly consumed from mobile devices and
require a stable high data-rate connection. In addition, some services, such
as SENaaS and ANaaS, may require a middle layer between the data source and
the HAPS, since the data collected by these services could be obtained from
devices in rural or isolated areas that are not covered by the HAPS network.
Hence, TBs will be used to obtain the data from these devices and send it to
the HAPS, as described earlier. Another set of services, such as SATaaS,
SENaaS, RaaS, require dedicated hardware to be installed within the HAPS
nodes. For example, SENaaS needs environment monitoring sensors, SATaaS
requires a navigation system, and RaaS entails dedicated routers (such as SDN
controllers) to route packets between ground nodes.

##  III Challenges

The real assessment of the cloud-enabled HAPS remains a strong function of the
physical challenges of their practical deployment. Such challenges, in
essence, stem from the HAPS nature as flying networks, their high power
requirements, and the conditions of the stratosphere and its surrounding
layer, all of which have to be factored in while designing such networks. This
section entails some of the considerations which would arise while integrating
cloud computing capabilities into HAPS, and discusses their potential
solutions, whenever possible.

###  III-A Energy Requirements

The high energy requirement needed to empower HAPS is considered a fundamental
challenge in HAPS research in general [5]. This is mainly because HAPS
missions as aerostatic flying networks require long endurance, which is in the
order of months or years. Such prolonged operation limits the use of classical
energy sources, such as traditional aviation fuel [48] or ground beams
supplies [49], especially given the lengthy constraints on HAPS propulsion and
electrical supply requirements, as well as the surrounding environmental
considerations. In conventional HAPS deployment, solar power coupled with
energy storage is considered essential to empower HAPS. This is primarily due
to the absence of clouds in the stratosphere, which enables utilizing solar
energy effectively. Arum et al. [50] investigate the feasibility of utilizing
solar power for long endurance, semi-permanent HAPS missions. The results show
that the services of a HAPS node can be provided for a duration of 15–24
hours/day with wingspans ranging between 25–35 m. Other recent energy supply
initiatives propose utilizing hydrogen power as a means to provide a long HAPS
endurance and better environmental impact [51].

In the particular context of cloud-enabled HAPS proposed in our paper, the
energy considerations become even more pronounced, especially given the
computational capability requirements at the centralized processors available
at the HAPS level. More precisely, the HAPS system envisioned in this paper
amalgamates both powerful processing capabilities and strong connectivity
premises, all while ensuring a prolonged, reliable, and effective system
operation. This paradigm shift in HAPS operation turn HAPS into a super-macro
base station (SMBS), with a multitude of stringent energy requirements [52].
Such challenging aspects of future HAPS deployment would require sophisticated
energy optimization and resource allocation schemes, so as to determine the
proper energy resources under both short-term and long-term considerations
(e.g., environmental impacts, deployment costs, weather forecasts, etc.), as
further discussed in the future research directions of the paper.

###  III-B Interactions between HAPS and Surrounding Layers

The cloud-enabled HAPS key infrastructure proposed in this paper is expected
to corroborate the multi-layered integrated space-air-ground networks (also
known as vertical heterogeneous networks (VHetNets)). Such networks are a mere
abstraction of integrated satellites at the GEO-layer (i.e., GEO-satellites),
LEO-layer (i.e., cubesats), stratospheric layer (i.e., HAPS and hot-air
balloons), and aeronautical layer (i.e., low-altitude platform systems (LAPS)
and UAVs), all inter- and intra-connected for better serving the ground-level
communications infrastructures. Not only would the end-to-end utility function
of such VHetNets depend on their highly coupled interactions and mutual
dynamics, but it is also a strong function of the ever-changing environmental
conditions across the different layers. For example, for systems where the
downlink transmission from the upper satellites layers to the HAPS are done
via FSO links, the system performance becomes a strong function of the impacts
of component failure and atmospheric conditions, e.g., turbulence, fog, and
rain [53, 54]. Likewise, the connection from the HAPS layer to the ground
level base stations becomes a function of the nature of the backhaul (RF or
FSO) and its surrounding environmental conditions. For example, Grace et al.
[55] study the effect of the HAPS node displacement on the received signal
power. They show that the antenna type (fixed or steerable) at the ground
station greatly affects the system performance when the HAPS node is displaced
from its location. The authors state that steerable antennas are of most use
when ground stations are immediately below the HAPS node, with no benefit for
stations that are on the edge of coverage.

From a macroscopic perspective, such intra- and inter-layer considerations,
together with the misalignment challenges, would practically affect the true
system performance metrics (e.g., coverage, rate, delay) and need to be
addressed as part of the challenging cloud-enabled HAPS systems. One possible
solution is to combine multiple spectrum enablers such as microwave, mmWave,
terahertz (THz), and FSO communications. Each of these technologies has its
advantages and disadvantages. On the one hand, lower-frequency microwave
communications support longer communication distances and wider coverage, both
of which are critical for the HAPS operation in the access domain. On the
other hand, higher-frequency mmWave and THz communications support much higher
data rates which are critical for the HAPS operation in the backhaul domain.
Since extending the communication distance of mmWave/THz requires, given the
high path and absorption losses, high antenna and beamforming gains, the
corresponding beamwidths are usually narrow, reducing coverage and
intensifying the misalignment effect. Nevertheless, in quasi-static HAPS
deployments, such links can still be established. Note that the particular use
of THz communications motivates the merging of communications, localization,
and sensing applications for HAPS operations [56]. Furthermore, THz links can
be used inside HAPS-deployed data centers [57], where the environment can be
perfectly controlled to remove molecular components that result in significant
THz absorption losses. Such wireless links in data centers can significantly
reduce the complications of wired connectivity within the HAPS, which is hard
to maintain and incurs an extra weight factor that can not be neglected in
flying platforms.

###  III-C QoS Requirements

The most important requirement from the user’s perspective is the QoS. In
general, the cloud customer tends to choose the service that better satisfies
his/her specific QoS requirements. Many previous works outlined a variety of
QoS metrics for cloud computing, including availability, reliability, response
time, scalability, elasticity, and fairness [58, 59]. In brief, availability
is related to the overall percentage of time during which a service is
available, reliability is related to several factors such as functioning
adequately; producing correct outputs; and resisting failures, response time
is the period between when a customer makes a request and a response is
returned, scalability (sometimes called capacity) is related to the volume (or
load) that a service can handle while maintaining standards of quality and
performance, elasticity is the ability to increase/decrease the amount of
service resources (such as CPU; storage; memory; and input/output bandwidth)
on demand, and fairness is related to assigning correct priorities to
customers and servicing them fairly based on their priorities in order to
achieve overall customer satisfaction. On the other hand, several papers [60,
61] discussed the QoS from the HAPS perspective and illustrated the various
methods that can enhance the QoS of the HAPS communications and deployment.

Most cloud platforms face challenges in meeting the various QoS requirements;
the same is expected for the cloud services offered within the HAPS. First, a
reliable connection should be maintained between the user’s device and the
HAPS node to achieve the availability metric. This can be realized by
installing HAPS gateways at high locations (such as the top of buildings) and
establishing a high-speed connection between the users’ devices and the HAPS
gateways. However, this cannot always be guaranteed, especially when the user
consumes the service from a mobile device. When the user is mobile, the
connection to the HAPS might be weak or lost in specific locations (such as
underground tunnels), which decreases the availability of the HAPS cloud
service. On the other hand, UAV base stations and TBs could be used as HAPS
gateways in rural areas to increase the availability of HAPS cloud services in
these areas.

Enhancing reliability is also a challenge with the advancement in the various
cloud-related technologies. The work in [62] proposes a comprehensive taxonomy
of reliability issues and a conceptual model for reliable cloud services. In
addition to the mentioned issues, HAPS-based services face additional failure
possibilities due to factors unique to the HAPS environment, such as weather
conditions and unexpected accidents (such as objects colliding with the HAPS
node). These factors should be considered, and appropriate solutions should be
deployed to increase the reliability of the HAPS cloud services. As for the
response time, it is mostly related to the communication techniques that will
be used to connect the users’ devices with the HAPS. As stated before,
intermediate nodes or networks (such as HAPS gateways in urban areas and UAVs
and TBs in rural areas) could be used to enhance the connection between the
user and the HAPS. Future studies should compare the various communication
methods and their corresponding response time ranges to identify the suitable
communication method for each type of cloud service according to its response
time requirements.

The scalability and elasticity of the HAPS cloud are expected to be limited as
compared to ground data centers due to the restricted space within the HAPS
for various cloud-required infrastructures (servers, network devices, storage
resources, etc.). Hence, important factors to consider are the limits that
should be specified for each service according to its computing and storage
requirements. For example, the HAPS network contains, at a certain time, a
specific number of nodes that accommodate a certain maximum amount of data.
Data-intensive cloud applications that produce continuous streams of big data
(such as real-time traffic management) should be configured to manage their
storage requirements in order not to overwhelm the HAPS data center and affect
the performance of other services. In general, cloud applications in the HAPS
should be carefully designed and implemented to consider the limitation of the
HAPS cloud in terms of scalability and elasticity.

Finally, fairness is one of the most difficult QoS metrics to satisfy due to
the complex factors that affect it. Several studies [63, 64] discuss the
elements that affect cloud service fairness and the methods that cloud
providers should use to develop structural and social service fairness and
guarantee equal service delivery and fair treatment. The same factors and
requirements apply to the HAPS cloud. However, the limited amount of resources
within the HAPS provides an additional challenge of allocating them fairly to
the various applications and their users.

###  III-D Physical Maintenance

The cloud-enabled HAPS architecture proposed in this paper requires the
deployment of several types of infrastructure (such as strong processors, data
storage systems, and communication equipment) which would add to the HAPS
platform weight and consumed energy. Therefore, the maintenance of such
systems would be an intricate mission, especially given its aerostatic
standing within the stratospheric layer. While HAPS systems are, generally
speaking, easier to bring back to to the ground level as compared to upper
satellites structures [5], periodic maintenance of aerostatic HAPS nodes
remains a necessity, especially in HAPS missions that require long endurance,
which could be in the order of months or years [65].

Since the HAPS system envisioned in this paper necessitates powerful
processing capabilities and connectivity premises, all while ensuring a
prolonged, reliable, and effective system operation, the durable deployment of
such system becomes a challenging function of the joint regular maintenance of
the HAPS computing and communication infrastructure. This is especially true
due to the QoS requirements that we mentioned in the previous section. Since
many hazards could abrupt the HAPS normal operations (such as hardware
failures, software bugs, and collisions), the continuous maintenance of the
various HAPS infrastructure is vital for ensuring reliable connectivity,
service availability, and acceptable response time of the HAPS cloud platform.
All these factors should be taken into consideration in assessing practical
network designs.

###  III-E Security Requirements

Since cyber threats are continuously growing and becoming more advanced, cloud
customers deem it necessary to select a cloud platform that provides top-of-
the-range security that suits the customer’s needs. In general, cloud security
relates to the technologies, rules, disciplines, and services that safeguard
the cloud software, framework, and data from security attacks. Researchers
from academia, industry, and standardization societies have presented various
perspectives on the cloud security necessities, identifying vulnerabilities,
well-known threats, and potential responses and solutions [66, 67, 68, 69].
The work in [67] emphasizes the fact that security should be enforced across
all seven layers within the cloud network. The authors identify seven main
requirements of cloud security: Confidentiality, integrity, availability,
authentication, authorization, accountability, and privacy. The main threats
that should be considered by cloud providers when designing a strong security
framework are related to data breaches, account hijacking and identity theft,
system and code vulnerabilities, malicious users, abuse utilization of
services, and denial of service (DoS). Furthermore, the authors identify
possible solutions to these threats, including encryption frameworks to manage
identities, keys, and signatures, intrusion detection and prevention systems,
software and virtual environment security, network security, storage security,
physical and hardware security, and trust management systems. The various
types of attacks that could be performed on the cloud framework, services, and
users are identified in [66]; they can be summarized as: DoS, service
injection, user-to-root, virtualization, man-in-the-middle, phishing, metadata
spoofing, and backdoor channel attacks.

With the advancement of technology, new types of security systems are being
researched and applied to the cloud, such as blockchain-based [70, 71, 72] and
ML-based [73, 74, 75] models. According to [70], the cloud provider can
encrypt and store the cloud access control rights in the blockchain, which
highly safeguards the privacy of users. In addition, when applying the
blockchain to the cloud, the cloud transactions’ data are encrypted,
distributed across the blockchain nodes, and interconnected via a hashing
function [71]. Hence, it becomes difficult for attackers to interfere with the
transactions’ data due to the consensus mechanism of the blockchain. The
authors of [73] propose using a convolution neural network (CNN) to analyze
the cloud traffic data, detect and identify sensitive data patterns, and
determine for each new data flow whether it belongs to normal or abnormal
activities. Furthermore, a collaborative privacy-preserving ML architecture
for healthcare systems is presented in [74]. The proposed mechanism provides
important solutions to several challenges that exist in ML-based ubiquitous
healthcare applications. Applying deep learning over cloud data encrypted
under multiple keys is further proposed in [75]. In this approach, the cloud
provider migrates the users’ data that are encrypted with the users’ keys to
the deep learning training model, and the latter trains the model using the
stochastic gradient descent (SGD) algorithm and performs the feed-forward and
back-propagation procedures based on a privacy-preserving calculation toolkit.

Since the cloud-enabled HAPS faces all these security challenges and threats,
it is important to study the best security mechanisms that should be applied
within the HAPS network to secure the various infrastructures, applications,
and data on each HAPS node. In addition, the connections within the HAPS
network and between the HAPS and the various entities (Fig. 1) should be
secured against network-related attacks. In general, it is expected that a
collection of security measures and procedures will be required to secure the
various elements of the HAPS cloud system. The selection of the most
appropriate, efficient, and secure mechanisms for each element of the HAPS
cloud system is expected to become a hot research topic in the near future.

##  IV Future Research Directions

TABLE IV: Summary of the proposed future research directions. Future Research Direction |  Key Points |  Objectives
---|---|---
HAPS Mobility and Message Routing |  • A framework for routing packets within the HAPS network and between the HAPS and the surrounding networks • An optimization problem that takes as inputs the locations and movements of nodes and the environmental conditions and produces the required locations of the HAPS nodes |  • Managing the intra-HAPS and inter-HAPS connections • Establishing routing paths between the HAPS nodes • Controlling the mobility of the HAPS nodes in order to maintain the connections with the surrounding networks
HAPS Security via Blockchain and Machine Learning |  • A blockchain model for securely saving the data and transactions within the HAPS cloud • A consensus mechanism suitable for saving the energy of the HAPS nodes • A ML model for monitoring the blockchain operations |  • Establishing a distributed ledger for saving financial transactions between the HAPS-cloud customers and providers • Establishing a data blockchain for securely saving all data operations that occur within the HAPS cloud platform • Detecting and resolving security attacks that target the blockchain communications and data
Intelligent Cloud-Enabled HAPS |  • A system that combines edge computing and ML capabilities • Network slicing is used to create multiple virtual instances within the HAPS cloud, and optimize each instance based on the requirements of each specific service |  • Making the HAPS cloud dynamically adapt to various network and services changes • Identifying and implementing the ML algorithm that is most suitable to monitor the performance of each HAPS cloud service and adjust its execution parameters
Artificial Intelligence-Based Resource Allocation |  • A framework that utilizes AI techniques to allocate various HAPS resources correctly and efficiently |  • Achieving good trade-offs between the HAPS-cloud different functionalities, such as data rate, power, energy, latency, coverage, and security
Integration with Vertical Heterogeneous Networks |  • A model to manage the real-time cloud-related operations and communications between the integrated space-air-ground networks |  • Creating a multi-cloud system that harvests the advantages of each of the space-air-ground networks and combines their cloud platforms into a distributed super cloud

In light of the challenges described above, many fundamental issues need to be
addressed so as to fully assess the gains harvested from the practical
deployment of cloud-enabled HAPS. This section presents the main open issues
related to the design of such systems, namely, HAPS mobility and message
routing, HAPS security via blockchain and ML, AI-based resource allocation in
cloud-enabled HAPS, and integration with vertical heterogeneous networks.

###  IV-A HAPS Mobility and Message Routing

The deployment of HAPS will not succeed without a solid framework that
identifies and applies the correct rules and procedures for the mobility of
the HAPS nodes. Such framework should optimize the routing of packets within
the HAPS network (intra-HAPS communications) and between the HAPS nodes and
other elements in Fig. 1 (inter-HAPS communications). Hence, we aim to design
and implement a routing protocol that calculates the optimal routing path from
a source to a destination HAPS node based on the routing requirement. The
latter could be the minimum delay (which is achieved by selecting the shortest
path that contains the minimum average distance between adjacent HAPS nodes),
the highest QoS (for instance, delivering the data with high accuracy), the
least energy consumption (for low priority messages), etc. In addition, the
routing algorithm monitors the connections between the HAPS nodes and the
surrounding networks and suggests to the HAPS control center adjusting the
location of one or more HAPS nodes in order to maintain or enhance the various
connections between the HAPS network and the satellite, UAV, TBs, and ground
networks. Since some of these latter networks are mobile, the HAPS connection
to one or more external networks could be lost under mobility. Hence, it is
essential to move some HAPS nodes to new locations to maintain the connections
with these networks.

In order to achieve these objectives, an optimization problem should be solved
to calculate the best position of each HAPS node that satisfies the intra-HAPS
routing requirements and ensures the persistence of the inter-HAPS
connections. The optimization problem is re-executed with updated parameters
whenever the status and environment of one of these networks changes
(satellites’ movements, UAV nodes’ movements, new topographic conditions,
environmental changes, etc.). In all these cases, the optimization problem
will be re-calculated with the new parameters that correspond to the new
locations of various nodes and/or the new connectivity conditions (such as
lost links and new links). The optimization problem results will be the
locations of the HAPS nodes and the new intra- and inter-routing paths. Hence,
these results could require moving one or more HAPS nodes to new locations and
establishing new routing paths. The optimization problem should take into
consideration the limits on the HAPS node movement that the HAPS network
administrators predefine.

###  IV-B HAPS Security via Blockchain and Machine Learning

We propose a future research direction in which we apply a blockchain model
augmented with ML to secure the data and access the HAPS cloud services. The
model utilizes blockchain capabilities to encrypt/decrypt all the data
exchanged between cloud users and providers. In particular, the model can
include two blockchains: The first is a distributed ledger that securely saves
all financial transactions in the cloud. Each financial transaction is
encrypted by the public key of the receiving HAPS node and added to the next
block. The HAPS nodes constitute the blockchain miners that generate the
blockchain blocks. In order to do that, the HAPS nodes will execute a
consensus model in which one of the HAPS nodes is selected as the current
miner that generates the next block. The role of the current miner alternates
between the HAPS nodes. Each HAPS node validates each financial transaction it
receives from the customers and sends it to the current miner. A new block is
generated each period Tblock. When the current miner generates the new block,
it broadcasts it to the HAPS nodes and waits for their acknowledgments. Each
HAPS node checks the validity of the transactions in the new block by
validating the signature of the cloud customer(s) who made the transaction. If
all transactions are valid, the HAPS node sends an acknowledgment to the
current miner. When the current miner receives acknowledgments from THC nodes
(where THC is a threshold determined by the consensus algorithm), it considers
the new block valid and broadcasts its ID to the HAPS network so that all
nodes add it to the blockchain. In this way, all financial transactions within
the HAPS cloud services are saved securely in the cloud ledger. A similar
model for the IoV was presented in [76].

The other blockchain in our model is the data blockchain. Here, it is not
feasible to save all data exchanged between cloud providers and users in the
blockchain since some applications generate huge amounts of data and some
users store extensive volumes of data in the cloud. Instead, the data in each
cloud transaction is hashed, and the hash result is saved in the data
blockchain as the transaction ID. In addition, this ID will be linked to the
actual data that is encrypted and saved in the cloud distributed database. The
data could be hashed hierarchically in multiple iterations if it is too big.
Hence, the hash of each data transaction will be saved in the data blockchain
for future reference. Whenever the cloud provider or the user wants to review
the data operations of a previous time period, the hashes can be fetched from
the blockchain and used to index the distributed database and obtain the data
of the corresponding transactions. This operation is required whenever there
is a need to check the details of the data transactions or to resolve any
dispute between the user and the cloud provider.

While the blockchain is used to save the data and financial transactions of
the cloud securely, a ML model can be used to manage and monitor the
blockchain operations to detect any attack attempts on the blockchain data and
messages. For example, suppose an attacker attempts to insert malicious
transactions in the next block by compromising multiple nodes in the
blockchain. In that case, the ML algorithm should be able to detect the attack
from the behavior of the nodes in the blockchain network. The ML algorithm
will continuously monitor the exchanged messages and their encrypted content
and compare them to attacks’ signatures and behaviors in the algorithm’s
training data. In general, the ML model can be used to monitor all the
operations on the blockchain network to detect new attacks, depending on a set
of training data that contains attack samples. When new attacks are detected,
they are added to the training data to detect similar future attacks.

###  IV-C Intelligent Cloud-Enabled HAPS

Edge computing is the process of shifting part of the cloud computations near
the data source or location. In general, edge computing services reduce data
bandwidth needs, improve latency, and ease cloud computing requirements. By
supporting Network Function Virtualization (NFV), network functions can be
divorced from dedicated hardware, allowing Software Defined Networking (SDN)
to rapidly deploy services as needed. NFV/SDN and edge computing share a
symbiotic relation where, mutually, they create a framework for modularized,
re-configurable network adaptability that can easily support network slicing
[77, 78]. Using network slicing, multiple network instances can be created,
each optimized for a specific service, yet sharing the same physical
infrastructure. While these innovations create massive adaptability, they also
create an exponentially expanding design space that is difficult to observe,
control, and predict, which is exactly where innovative Artificial
Intelligence and Machine learning (AI/ML) techniques are expected to make the
most contributions. AI/ML are expected to fuel the next phase of innovation at
the cloud-enabled HAPS systems where both the wireless networks and
infrastructure become more dynamic, virtualized, intelligent, distributed, and
energy-efficient.

In this context, ML is exceptionally good at solving problems where the design
space is extremely large, and complicated rule lists can be replaced by
elegant ML routines that can learn from previous data using automated feature
extraction to alleviate the need for tedious manual extraction. In such high-
altitude cloud systems, not only is the parameter space huge, it is also time-
varying, and more often than not, involves objective functions that are non-
linear and/or polynomial, requiring complicated heuristics based on expensive
measurement campaigns. ML offers a solution for setting and continually
updating network parameters based on actual usage patterns. Generally
speaking, Edge Machine Learning (EML) can be used for training, inference, and
Mobile Multi-Access Edge computing (MEC), all aimed at providing lower
latency, higher reliability, and distributed network resources. However, EML
faces significant challenges, including long training times, high
computational power requirements, difficulty to generate and process
sufficient relevant training samples, scalability, among other issues that
have limited adoption.

There is no ML technique that can be said to be better than any other.
According to the No Free Lunch theorem, any two methodologies may perform
equally well in solving a problem if there is no prior information about the
problem. However, the selection of certain learning techniques for
optimization in cloud-enabled HAPS is tied with the services, the ultimate
objective of that service, and the application information domain. For
example, artificial neural networks (ANNs) fit predicting service while
reinforcement learning is more suitable for optimization with a certain goal
such as service latency. Reinforcement learning matches services in absence of
a priori knowledge and a sufficiently large amount of training data. However,
they suffer from a major drawback: high calculation cost because an optimal
solution requires that all states be visited to choose the optimal one. Thus,
this tradeoff must be carefully considered when implementing reinforcement
learning in certain environments. As shown in [79], support vector machine
(SVM) based learning outperforms deep learning in terms of computation time
for training in the experiment of predicting the next location of a user,
considering the associated delays. However, deep learning wins by far in
predicting the number of users located in a certain location. Deep learning
requires 27 mins to train while SVM consumed 6 days for training [79]. Thus,
we can conclude that each ML technique has advantages and drawbacks that make
it performs better in one application but not in the other.

The selection of ML platforms to be utilized is a challenging task as there is
no single platform that can fit all solutions. Each platform has advantages
and drawbacks. Thus, there are important factors to be considered in the
selection of a ML platform, which are scalability, speed, coverage, usability,
extensibility, and programming languages support. The size and complexity of
data are the main drivers of the scalability factor. The processing platform
that the library is running impacts the speed factor. Coverage comprises the
number of ML algorithms implemented in the tool. The usability factor includes
initial setup, maintenance, and the availability of programming language. The
extendibility factor means that the implementation used in the toolkit can be
useful in the design of a new platform. However, some application requires
real-time data for analysis. Thus, stream processing is designed to analyze
and act on real-time streaming data, using continuous queries to handle high
volume in real-time with a scalable, highly available, and fault-tolerant
architecture. With all these available ML platforms, an important design
decision should be made considering the application requirements and the
associated constraints of the cloud-enabled HAPS architecture.

###  IV-D Artificial Intelligence-Based Resource Allocation

The proposed cloud-enabled HAPS system performance is a strong function of the
practicality of interference management techniques, especially developed to
manage the network radio resources. Such techniques, which are expected to be
coordinated across the HAPS, air, and ground networks, aim to achieve good
trade-offs between the network’s different functionalities, e.g., rate, energy
efficiency, power, latency, coverage, and security. Such utilities are all
functions of how to best design the system parameters, e.g., the spectrum
utilized by the different system links, the transmit power at the HAPS layer,
the traffic routing protocols, the inter-HAPS links, as well as the edge and
core processing capabilities of the system under study, which makes the
resource allocation problem in cloud-enabled HAPS intricate in nature. Given
the additional dynamics of the ground communications infrastructures and the
stochasticity of the HAPS-to-ground and HAPS-to-air channels, we expect AI-
based techniques to play a major role in the design and optimization of future
cloud-enabled HAPS networks, as also mentioned in the previous section. The
promises of ML in solving complex and analytically intractable optimization
problems (e.g., see [80] and references therein) would indeed fuel a plethora
of open issues in the context of dynamic resource optimization in cloud-
enabled HAPS, which promises to be an active area for future research.

###  IV-E Integration with Vertical Heterogeneous Networks

As discussed earlier, cloud-enabled HAPS networks are expected to be a part of
multi-layered integrated space-air-ground networks (VHetNets). Such
integration includes geo-satellites, cubesats, HAPS, hot-air balloons, LAPS,
and UAVs to best serve ground-level communications infrastructures. Such
system performance becomes a strong function of the interactions between its
different entities, their mutual dynamics, and the ever-changing environmental
conditions of all the inter- and intra-connecting links, especially given the
spatio-temporal variations of such distributed entities and their relative
positioning. An important future research direction in this field is,
therefore, to enable the real-time operations of such integrated space-air-
ground networks, by jointly accounting for both the cloud computing
capabilities of HAPS, and the distributed intelligence of other networks
entities through invoking their wireless edge computing functionalities. For
instance, such integration would bring other levels of computing into the
overall integrated paradigm, e.g., fog-computing at the UAV level, and edge-
computing at the network edge [81].

The coordination among such multi-layered flying networks would then require a
tactful joint operational treatment which includes, but is not limited to,
joint routing protocols and synchronization operations on the networking
layers, joint resource allocation on the medium access control (MAC) layers,
and joint modulation and coding schemes (MCS) designs on the physical layers.
Such a compound infrastructure, although intricate, would open the way for a
multi-cloud system that harvests the advantages of each of the aforementioned
networks, and combines their cloud platforms into a distributed super cloud
that reaches a wider range of users, and offers them richer QoS services,
which promises to be an exciting area of future research.

Finally, for the sake of completeness, we refer the readers to Table IV, which
summarizes the future research directions discussed in this section. More
specfically, Table IV illustrates the key points and objectives of each of the
proposed research directions above.

##  V Conclusion

Augmenting ground-level communications with HAPS is expected to be among the
major physical-layer innovations of 6G wireless systems. Given the HAPS quasi-
static positioning at the stratosphere, HAPS-to-ground and HAPS-to-air
connectivity frameworks are expected to be prolific in terms of data
acquisition and computing, which motivates to equip HAPS with cloud computing
capabilities. This paper first advocates for the potential physical advantages
of deploying HAPS as flying data centers, also known as super-macro base
stations. The paper then presents the merits that can be achieved by
integrating various cloud services within the HAPS, and the corresponding
cloud-type applications that would utilize the HAPS for enhancing the quality,
range, and types of offered services. The paper further presents some of the
challenges and resource allocation problems that need to be addressed for
realizing practical cloud-enabled HAPS, mainly those related to the high
energy and processing power, maintenance, QoS, and security considerations.
Finally, the paper sheds light on some open research directions in the field,
namely, HAPS mobility and message routing, HAPS security via blockchain and
ML, AI-based resource allocation in cloud-enabled HAPS, and integration with
vertical heterogeneous networks. To the best of the authors’ knowledge, this
paper is the first of its kind which advocates for the use of cloud-enabled
HAPS from a holistic perspective, and presents the challenges and open issues
of such deployment, which promises to be an active area of research in the
context of 6G systems development and cross-layer optimization.

## Acknowledgment

This work was supported in part by the Center of Excellence for NEOM Research
at the King Abdullah University of Science and Technology (KAUST).

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