# A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Xiaoyu Xia  0000-0003-3526-3217 School of Computing Technologies, RMIT
UniversityVIC3000Australia<EMAIL_ADDRESS>,  Sheik Mohammad Mostakim
Fattah  Centre for Research on Engineering Software Technologies, University
of AdelaideSA5005Australia<EMAIL_ADDRESS>and  Muhammad Ali
Babar  Centre for Research on Engineering Software Technologies, University of
AdelaideSA5005Australia<EMAIL_ADDRESS>
###### Abstract.

Edge computing facilitates low-latency services at the network’s edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The
recent advancement in Unmanned Aerial Vehicles (UAV) technologies has opened
new opportunities for edge computing in military operations, disaster
response, or remote areas where traditional terrestrial networks are limited
or unavailable. In such environments, UAVs can be deployed as aerial edge
servers or relays to facilitate edge computing services. This form of
computing is also known as UAV-enabled Edge Computing (UEC), which offers
several unique benefits such as mobility, line-of-sight, flexibility,
computational capability, and cost-efficiency. However, the resources on UAVs,
edge servers, and IoT devices are typically very limited in the context of
UEC. Efficient resource management is, therefore, a critical research
challenge in UEC. In this article, we present a survey on the existing
research in UEC from the resource management perspective. We identify a
conceptual architecture, different types of collaborations, wireless
communication models, research directions, key techniques and performance
indicators for resource management in UEC. We also present a taxonomy of
resource management in UEC. Finally, we identify and discuss some open
research challenges that can stimulate future research directions for resource
management in UEC.

UAV-enabled edge computing, resource management, architectures, offloading,
allocation, provisioning, algorithms

††copyright: acmcopyright††journal: CSUR††journalyear: 2024††journalvolume:
1††journalnumber: 1††article: 1††publicationmonth: 1††price: XXX††doi:
XXX/XXXX††ccs: General and reference Surveys and overviews††ccs: Computer
systems organization Cloud computing

##  1\. Introduction

###  1.1. Edge Computing and UAVs

The recent advancements of the Internet of Things (IoT) and wireless
communication technologies have introduced many new applications that require
high computational power and low latency (Mao et al., 2017), including
wearable cognitive assistance, augmented reality (AR), smart healthcare,
facial recognition, and road safety monitoring (Zhao et al., 2021). However,
IoT devices typically have limited computational resources, storage, network
coverage, and energy. Therefore, resource-intensive IoT applications often
face significant challenges in maintaining the expected Quality of Services
(QoS) (Mach and Becvar, 2017; Li et al., 2022c). A conventional approach to
maintain the expected QoS is to leverage cloud computing (Lin et al., 2020) by
utilizing resources from remote cloud servers in different forms such as
virtual machines, virtual storage, and virtual private networks (Armbrust et
al., 2010). However, cloud computing is now considered inadequate to meet the
low-latency requirements of resource-intensive and delay-sensitive IoT
applications (Mao et al., 2017). The reason is two-fold. First, the number of
IoT devices is exponentially increasing and it is expected that it will be
approximately 125 billion by 2030 (Abusafia and Bouguettaya, 2020). These
devices generate a large volume of network traffic that burdens the backhaul
network and significantly affects its performance by network congestion (Xia
et al., 2022a; Lai et al., 2022). Second, cloud servers are typically placed
at a remote distance from IoT devices. As a result, cloud computing introduces
a considerable amount of delay in service provisioning, which degrades the
overall QoS of delay-sensitive IoT applications (Liu et al., 2021a; Xu et al.,
2020b).

Edge computing is a relatively new paradigm that offers an alternative
computing solution for delay-sensitive and resource-intensive IoT
applications. Edge computing extends cloud computing technologies to the edge
of a network, closer to users and IoT devices (Lin et al., 2020). It allows a
resource-limited IoT device (a.k.a., edge devices) to fully or partially
offload its data and computational tasks to nearby powerful edge servers or
other edge devices (Abbas et al., 2017). It substantially improves the latency
and energy efficiency of IoT applications. This also reduces the traffic
congestion at the core network. Edge servers also work as data caches to store
frequently accessed data by IoT devices to improve the QoS of the applications
(Zhao et al., 2021; Zhou et al., 2023).

IoT devices are typically connected to an edge infrastructure using wireless
networks (Mao et al., 2017). However, a good wireless network infrastructure
may not always be available in some of the remotest areas, e.g., rural or
mountain (Ji et al., 2020). Moreover, a wireless network infrastructure can
easily be affected by natural disasters such as earthquakes, floods, or
storms. In military operations or emergency rescue missions, it may often be
difficult to have a trustworthy wireless network infrastructure (Lewis et al.,
2014). Unmanned Aerial Vehicle (UAV) technologies have opened a new
opportunity where edge computing services are provisioned using UAVs in
military operations, disaster response, or rural areas. This is also known as
UAV-enabled edge computing (UEC) (Mei et al., 2019). UAVs offer a wide range
of befits such as mobility, flexibility, and cost-efficiency, which make UEC a
promising solution. A UAV typically works as an aerial edge server or a relay
in a UEC environment (Li et al., 2020a). IoT devices offload their
computational tasks fully or partially to a nearby UAV. A UAV either processes
the tasks locally or sends them for remote execution to nearby edge/cloud
servers.

###  1.2. Resource Management in Edge Computing and UEC

Resource management in edge computing has been investigated intensively by
many researchers in the last decades (Xia et al., 2023; Liu et al., 2019a).
Edge computing offers many unique advantages, such as distributed frameworks
and low-latency services, however, it poses many unique challenges, including
resource provisioning, computation offloading, and resource allocation.

In general, resource management refers to the use of a set of actions and
methodologies to allocate resources, such as bandwidth, energy, CPU, GPU and
many others, to devices or tasks for achieving specific objectives, i.e.,
maximizing utility, minimizing service latency, etc (He et al., 2022; Li et
al., 2022b; Liwang et al., 2022; Liwang and Wang, 2022; Xia et al., 2022d). In
edge computing, the resource has its new characteristics: 1) limited - the
resources on an edge server are usually constrained due to the limited
physical size with smaller processors and a limited power budget (Chen et al.,
2018; Li et al., 2020b; Jin et al., 2022); 2) dynamic - task requirements,
device mobility, available resource amounts on edge servers are always
changing over time (Xia et al., 2021b); and 3) heterogeneous - both resources
on edge servers and resources requirements of devices and tasks are
heterogeneous (Pasteris et al., 2019).

Edge computing provides storage, computing and communication resources at the
network edge to improve serviceability significantly. To ensure the utility of
edge resources, appropriate resource scheduling strategies are required from
two perspectives: infrastructure providers and services providers. Such
resource management problems have been intensively investigated from the
perspective of infrastructure providers with different optimization
objectives, e.g., minimum transmission cost (Tran and Pompili, 2018), maximum
system throughput (Deng et al., 2020), maximum data sharing efficiency (Luo et
al., 2019), etc. From a service provider’s perspective, the objectives of
resource management can be minimizing resource rent cost (Xia et al., 2019),
maximizing users’ QoS/QoE (Lai et al., 2019), maximizing users’ total data
rate (Xia et al., 2022c), maximizing its revenue (Samanta and Chang, 2019),
and many others.

Resource management in UEC is a topical research area, attracting many
researchers from both academia and industry. (Zhou et al., 2020; Liu et al.,
2020a). Table 1 depicts the number of papers related to resource management in
UEC published in the last 5 years. The details of the literature methodology
are explained in Section 1.4. It clearly shows an increasing interest in the
research community’s inefficient resource management in UEC. Compared with
traditional edge computing, UEC offers several advantages such as flexibility
and autonomy with the fast deployment of UAVs for serving devices in rural
areas or for critical missions (Feng et al., 2020; Wu et al., 2018; Wu and
Zhang, 2018). However, UEC also poses many new challenges with its unique
characteristics, such as multiple roles of UAVs, the mobility of servers, and
the UAV trajectory design. Specifically, UAVs can play multiple roles when
formulating resource management strategies in UEC, including servers, relays
and users. The high mobility and limited resources of UAVs also complicate
resource management in UEC. In addition, external factors, such as wind, rain
and storm, can impact the performance of UAVs significantly. Considering those
complicated scenarios in UEC, researchers from academia and industry have
started to investigate to identify and develop suitable strategies for
resource management in UEC. To guide researchers in this area, we have
conducted this survey to provide an overview of resource management in UEC and
point out the challenges that can direct future research.

###  1.3. Related Surveys

Table 1. Number of related papers Year |  2017 |  2018 |  2019 |  2020 |  2021 |  2022
---|---|---|---|---|---|---
Number |  2 |  14 |  28 |  58 |  67 |  96

Existing studies have focused on different aspects of resource management in
UEC including resource provisioning and allocation, task or data offloading,
and trajectory design. However, there is no existing survey or review that
provides a comprehensive overview of resource management in UEC. A few survey
studies of UEC are mainly limited to the computational offloading aspect of
resource management in UEC. A survey on offloading in UEC is presented in
(Huda and Moh, 2022). This study reports a comparative assessment of various
offloading algorithms based on their performance and features. The survey
provides different application scenarios and a case study of where UAVs could
be leveraged to enable edge computing. Examples of such applications are next-
generation wireless networking, surveillance of property, UAV-enabled target
tracking, UAV-MEC in a pandemic, and reconnaissance in military operations. It
provides a classification of offloading algorithms based on offloading
policies such as binary offloading, partial offloading, and relay.

Table 2. A list of recent surveys in UAV-enabled Edge Computing Paper |  Title |  Year |  Key Contributions
---|---|---|---
(Zhou et al., 2020) |  Mobile Edge Computing in Unmanned Aerial Vehicle Networks |  2019 |  The paper presents three UEC architectures based on the role of UAVs in UEC. It provides a brief survey on computational offloading and resource allocation in UEC.
(Abrar et al., 2021) |  Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review |  2021 |  The paper presents a brief survey on UEC networks with a focus on energy efficiency. It introduces basic terminologies and architectures used in UEC. It also presents various techniques and challenges related to computational offloading and a brief overview of resource management in UEC.
(Yazid et al., 2021) |  UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |  2021 |  This paper presents a review of deep learning and machine learning techniques used in various applications of UEC.
(Huynh and Huh, 2022) |  UAV-Enhanced Edge Intelligence: A Survey |  2022 |  The paper introduces and presents various applications of UAV-enhanced edge intelligence systems. It also discusses some challenges and future research directions for UAV-enhanced Edge Intelligent systems.
(Huda and Moh, 2022) |  Survey on computation offloading in UAV-Enabled mobile edge computing |  2022 |  The paper presents a comprehensive survey of computational offloading research in various UEC systems. It compares existing algorithms qualitatively to assess their features and performances.
(Song et al., 2022) |  A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions |  2022 |  The paper provides an extensive survey of UAV optimization problems of UAV-enabled mobile edge computing with applications of Machine Learning techniques.
(Singh et al., 2023) |  A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions |  2023 |  This paper comprehensively reviews the service migration, task offloading, resource allocation and content caching problems in EC. The paper briefly explores the role of UAVs as user equipment.

A short survey is reported in (Abrar et al., 2021) which provides an overview
of UEC networks and focuses on energy efficiency-related studies. It
summarizes key computational offloading techniques concerning the ground
mobile users who offload computational tasks to nearby UAVs. It also provides
a summary of the approaches for the resource management of UAVs from the
energy efficiency aspect. A survey on UAV-enhanced edge intelligence is
presented in (Huynh and Huh, 2022) where edge intelligence is introduced as a
key concept in fifth-generation networks. The survey presents various
applications of UAV-enabled edge intelligence such as smart agriculture,
disaster relief, and intelligent transport systems. The survey also discusses
some key challenges to realising UAV-enabled edge intelligence such as energy
efficiency, practical implementation, security, and privacy. A comprehensive
review is carried out in (Yazid et al., 2021) which focuses on the role of
Machine Learning, and Deep Learning based methods to enable UEC. The review
presents an extensive study of different types of UAVs, their capabilities,
and possible applications. Examples of the application areas are agriculture,
industry 4.0, environment monitoring, health and emergency, smart cities and
smart homes. The review also presents a classification of the relevant studies
in UEC in terms of AI-based approaches. There are also other surveys in UEC
from different perspectives, such as security (Gupta and Gupta, 2022), UAVs as
user equipment (Singh et al., 2023), machine learning applications(Song et
al., 2022), etc. Different from the existing surveys, our prime focus is to
provide a holistic view of resource management in UEC based on the existing
relevant studies. To the best of our knowledge, there is no existing review
that provides a comprehensive overview of resource management for UEC. A list
of the recent surveys is shown in Table 2.

###  1.4. Literature Methodology

The selection of studies for our research was guided by the principles
outlined in the Systematic Literature Review guidelines (Keele et al., 2007).
Here, we first identified and designed the keywords to retrieve papers from
widely-used databases including ACM Digital Library, IEEE Xplore, Wiley,
Scopus, SpringerLink, ScienceDirect and Hindawei: ”’Unmanned Aerial Vehicle’
OR UAV, Edge OR Fog, offloading OR workload OR scheduling OR share OR
allocation OR resource OR task OR schedule OR sharing OR cache OR data OR
storage OR provision OR position OR trajectory”.

Study selection. We obtained 277 papers using the keywords above and defined
inclusion/exclusion criteria based on (Keele et al., 2007) to filter out
irrelevant or unnecessary studies. Based on these criteria, we removed 162
papers from the initial set of 277. After reading the full text and applying
the criteria, we obtained 50 papers directly related to resource management in
UEC. To further increase study coverage, we performed backward and forward
snowballing on these 50 papers, identifying 7 more papers. We included a total
of 57 studies for our survey. Acknowledging that our selection may not
encompass all relevant literature in the field, we are confident that our
selection encompassed the majority of essential research studies that reveal
the techniques for managing resources in UAV-enabled edge computing.

Data extraction and synthesis. To ensure a thorough analysis, we conducted a
pilot study of 10 papers to gain familiarity with the data to be extracted
from the primary studies. Using initial codes, we iteratively merged them in
several rounds to create themes. The analysis was carried out independently by
two authors, with each author analyzing half of the selected papers and then
reviewing the analysis output of the other author. Cases of disagreement were
resolved through discussions among all the authors.

###  1.5. Contributions and Organization

Figure 1. Organization of the Survey

This survey provides a comprehensive review of the state-of-the-art research
on resource management in UEC. The key contributions of this work are as
follows. ❶ We introduce a three-layered UEC architecture in section 2,
representing a conceptual architecture for managing resources in UEC. The
architecture contains a Things layer, an Edge layer, and a Cloud layer. We
then investigate six types of collaborations that take place in the proposed
architecture. The considered collaborations are a) Things-UAV, b) UAV-Edge, c)
Things-Edge, d) Things-UAV-Cloud, e) UAV-Edge-Cloud, and f) Thigns-UAV-Edge-
Cloud. We also discuss the wireless communication models used in UEC. ❷ We
discover the key research problems of resource management in the context of
UEC. In Section 3, we categorize the research problems into the three
following categories: a) computational tasks and data offloading, b) resource
allocation, and c) resource provisioning. ❸ Section 4 identifies and
comprehensively reviews the key techniques and performance indicators used for
resource management in UEC. The key techniques are categorized into two
categories: a) centralized methods and b) decentralized methods. We
investigate how these methods are evaluated in the existing work. In addition,
The key performance indicators such as energy consumption, latency,
throughput, cost, utility, and resource utilization in the existing literature
are discussed. ❹ We identify our key findings from this work in section 5
which points out the key research challenges and the future research
directions in UEC for resource management.

Figure 1 illustrates the organization of this survey to provide the readers
with a brief overview of this paper.

##  2\. Architecture

We present a conceptual architecture based on the existing UEC-related studies
for resource management in this section. A typical UEC architecture consists
of three layers including things, edge, and cloud. Existing studies
investigate different types of scenarios for resource management in a UEC
environment. As a result, different types of collaborations between things,
UAV, edge, and cloud can be found in the existing studies. We highlight
different types of collaboration between things, UAV, edge, and cloud in a
typical UEC environment. In addition, we analyze and discuss the advantages
and limitations of these collaborations under different contexts. Finally, we
discuss the wireless communication models used in UEC systems.

###  2.1. Overview of the Conceptual Architecture

Figure 2. A Conceptual Architecture for Resource Management in UAV-enabled
Edge Computing Environment

Figure 2 illustrates a typical three-layer UAV-enabled edge computing
structure. The bottom layer, called things, consists of end-devices such as
smart devices and autonomous vehicles, and different applications such as
smart agriculture, smart home, smart health and Industry 4.0 (Liu et al.,
2020a). End-devices are often called user devices (UD), mobile devices (MD) or
user equipment (UE). These devices continuously collect data from the
environment, which requires processing to support smart applications. These
devices typically have limited resources and energy. Data collected by these
devices are either processed locally or offloaded to the UAVs based on
available resources and required QoS. The existing studies develop numerous
offloading approaches to efficiently manage the resources in the things layer
based on required QoS, i.e., latency, energy efficiency and throughput (Huda
and Moh, 2022). In a typical UEC environment, end-devices can be fixed in a
particular location or change their position over time such as mobile users or
autonomous vehicles. Based on the mobility of the devices, different types of
offloading strategies are proposed in the existing literature.

The layer above the things layer is referred to as the edge layer, which
comprises base stations, gateways, edge servers, cloudlets, and UAVs. UAVs in
this layer can either offer edge services directly to the things layer or
function as relays for offloaded tasks or data sent to edge servers or the
cloud. The edge layer typically delivers a range of edge services, such as
computing and storage services, to the things layer through wireless
communication technologies like 4G and 5G. Effectively managing resources
within the edge layer of UEC necessitates efficient resource allocation and
provisioning of UAVs (Liu et al., 2020b). Due to the limited resources of
UAVs, task offloading from UAVs to other UAVs, edge servers, or the cloud is
often required to fulfil the QoS requirements of the things layer.

The cloud layer includes various public and private clouds such as Amazon,
Microsoft, IBM, and Google, supporting the edge layer via the core network
(Luo et al., 2021b). The cloud layer consists of large data centres, powerful
computing capabilities, and high-speed communication resources. The edge layer
often relies on the cloud layer to manage resource-intensive tasks and global
information.

###  2.2. Collaborations

Table 3. Collaborations and Roles of UAV in UEC Collaboration Types |  Related Studies |  Role of UAV
---|---|---
Things - UAV |  (Feng et al., 2021), (Yang et al., 2019), (Ye et al., 2020), (Shimaday et al., 2020), (Wang et al., 2021b), (Xu et al., 2020a), (Wang et al., 2021a), (Wang et al., 2019b), (Ning et al., 2021), (Sun et al., 2021), (Hu et al., 2018), (Khochare et al., 2021), (Zhang et al., 2019), (Zhao et al., 2021), (Liu et al., 2021c), (Luo et al., 2021a), (Jeong et al., 2017), (Li et al., 2020a), (Ji et al., 2020), (Ouyang et al., 2021), (Gu et al., 2021), (Bai et al., 2019), (Qian et al., 2022), (Wang et al., 2022c), (Xu et al., 2022b) |  Edge Server
UAV - Edge |  (Liwang et al., 2021), (Xu et al., 2021), (Dai et al., 2020), (Guo et al., 2021), (Zhu et al., 2022a) |  User
Things - UAV - Edge |  (Wang et al., 2021c), (Wu et al., 2020), (Cheng et al., 2018), (He et al., 2021a), (Hu et al., 2020), (Apostolopoulos et al., 2021), (Zhang et al., 2020a), (Chen et al., 2020), (Liu et al., 2020c), (Yu et al., 2020), (Liu et al., 2020b), (Dai et al., 2021), (Hu et al., 2019), (Liu et al., 2019b), (Seid et al., 2021), (Xiao et al., 2022), (Zheng et al., 2022) |  Edge Server and Relay
Things - UAV - Cloud |  (Wu et al., 2021), (Mao et al., 2020), (Wan et al., 2019), (Mei et al., 2019), (Ti and Le, 2018), (Xie et al., 2022) |  Edge Server and Relay
Things - UAV - Edge - Cloud |  (Asheralieva and Niyato, 2019), (Zhang et al., 2020c), (Liu et al., 2020d), (Xu et al., 2022a) |  Edge Server and Relay

Table 3 demonstrates five types of collaborations between things, UAV, edge,
and cloud that are considered in the existing literature. These collaborations
are Things - UAV, UAV - Edge, Things - UAV - Edge, Things - UAV - Cloud, and
Things - UAV - Edge - Cloud.

####  2.2.1. Things - UAV

Resource management in the Things-UAV layer considers the interaction between
the Things layer and the UAVs in the edge layer. Most studies consider that
one or multiple UAVs are deployed to enable UEC in areas where the terrestrial
network is unreachable or insufficient to maintain the required QoS. UAVs are
typically considered flying edge servers in most existing studies where the
Things-UAV collaboration is the key focus. For example, a fairness-aware
approach is proposed for resource management in (Zhao et al., 2021). The
proposed approach considers that a UAV works as a flying edge server and
provides computing and caching services to the ground nodes. Efficient
resource management of UAVs is the key concern in this case to improve various
QoS attributes. The proposed approach minimizes the energy consumption of a
UAV by considering its trajectory and resource allocation. A joint resource
scheduling approach is proposed where a UAV provides offloading services to a
group of vehicles. The vehicles are considered capable of wireless power
transmission. The goal is to increase the computational throughput with
communication and computational resource constraints. Depending on the number
of available UAVs, different types of resource management strategies and
optimizations are required. For instance, a unified framework is proposed in
(Luo et al., 2021a) that improves the energy efficiency for ground users when
multiple UAVs are deployed to enable a UEC system. The proposed framework
jointly optimizes task scheduling, bit allocation, and UAV trajectory using a
two-layer optimization strategy. An energy consumption minimization approach
is proposed in (Ji et al., 2020) where the energy consumption of UAVs and
ground nodes is optimized by considering the UAV trajectory and its resource
allocation.

####  2.2.2. UAV - Edge

The architecture in Fig. 2 shows that UAVs can be either in the Things or Edge
layer. Several studies consider UAVs as moving users with limited resources.
Under the UAV-Edge collaboration, UAVs are normally required to offload tasks
to the nearby edge servers to pursue their optimization goals, e.g.,
maintaining their desired QoS, maximizing the throughput, etc. To name a few,
Liwang et al. propose a resource trading mechanism between a UAV (buyer) and
an edge server (seller) (Liwang et al., 2021). Since the edge server has to
serve users in its coverage, the available resources that can be provided to a
UAV, are always limited and dynamic. The mechanism proposed in this paper
considered both contracts for future resource trades and transmission power
optimization. To tackle the resource trades between multiple edge servers and
multiple UAVs, a Stackelberg game-based algorithm is introduced in (Xu et al.,
2021). In this study, edge servers provide the price of resources, and the
UAVs make decisions to hire resources from edge servers to maximize utilities.
In (Dai et al., 2020), UAVs offload the computation tasks collected from
nearby devices to edge servers, similar to (Guo et al., 2021).

####  2.2.3. Things - UAV - Edge

UAVs are often deployed to extend edge computing services in remote areas for
sparsely distributed ground nodes. However, UAVs are subject to limited
resource capacity. Many existing studies leverage edge servers located at
nearby base stations. This type of arrangement enables the Things - UAV - Edge
collaboration. In this collaboration, UAV may work as an aerial edge server or
a relay that forwards offloading tasks to nearby edge servers. For instance, a
Things - UAV - Edge collaboration is proposed in (Yu et al., 2020) where a UAV
is placed to enable edge services to ground users. The paper assumes that
users cannot communicate directly with the nearby base stations or Wi-Fi
access points. Therefore, the paper proposes a joint optimization approach
that considers task offloading, resource allocation, and UAV trajectory to
improve the service latency for all ground nodes and the energy efficiency of
the UAV. In (Liu et al., 2020c), a cluster of UAVs serves a set of mobile
users. This scenario considers that a UAV can offload tasks to nearby UAVs to
maintain the ground users’ expected QoS. A similar scenario is studied in (Liu
et al., 2020b), where a cooperative scheme is proposed to efficiently carry
out the computational offloading process in a UEC system. The goal of the
proposed scheme is to maximize the long-term utility of UEC using an
interference mitigation technique. A task and bandwidth allocation approach is
proposed in (Hu et al., 2019), where a UAV acts as either a relay or an edge
server. The proposed approach aims at maximizing the energy efficiency of the
UAVs and ground nodes. It proposes a joint optimization technique that
optimizes the task allocation, bandwidth allocation, and the UAV’s trajectory.

####  2.2.4. Things - UAV - Cloud

Several studies have considered a Things - UAV - Cloud collaboration where
devices in the Things layers in the remote and mountainous areas without
nearby cellular or base stations. Therefore, UAVs are deployed to enable edge
services and satellite-based services by providing ubiquitous and seamless
network coverage. For example, a space-air-ground integrated network is
considered to provide computational offloading services for remote IoT
applications to achieve the min-max service latency (Mao et al., 2020). In
this work, a set of UAVs are deployed to provision various IoT and mobile
devices in remote areas. The LEO satellite-based cloud is envisioned that
provide ubiquitous coverage. Computational tasks of the devices are
partitioned to be executed by UAVs and LEO satellites. Similarly, an online
data processing network with this three-layer collaboration is considered in
(Wan et al., 2019), where UAVs are deployed as moving edge servers that
collect data from distributed sensors and conduct the initial processing. UAVs
send these results to a centralized cloud for further processing. A similar
scenario is studied in (Mei et al., 2019), where the concept of virtualized
network function is leveraged to help ground nodes in offloading tasks. A
joint resource allocation and UAV trajectory optimization approach is proposed
to minimize the overall energy consumption of a UAV. A hierarchical edge-cloud
is considered where a set of users, a set of UAVs, and a macro base station
provision UEC (Ti and Le, 2018). In this case, the study assumes that each
ground node can offload its computation task to at most one cloud or cloudlet
server.

####  2.2.5. Thing - UAV - Edge - Cloud

Some studies have considered a Things - UAV - Edge - Cloud collaboration in
which UAVs work as a relay and/or server to serve devices in the Things
layers, collaborating with edge servers and remote cloud servers by borrowing
resources from them. This is the most comprehensive architecture of UEC. In
(Asheralieva and Niyato, 2019), UAVs collect tasks from devices acting as a
relay and then send those tasks to edge servers. Once edge servers receive
those tasks, each edge server checks its capacities for task execution and
offloads partial tasks to the cloud if it cannot handle all the tasks.
Similarly, In (Liu et al., 2020d; Zhang et al., 2020c), UAVs collect data or
tasks from different areas and then send them to edge servers for execution.
If there are not enough resources, part of the data or tasks will be
transferred to a remote cloud. In this scenario, the capacities of UAVs and
edge servers can be fully utilized and the resources in cloud servers could be
borrowed to complete all the tasks from devices with consideration of limited
resources on UAVs and edge servers. The evidence that many existing studies do
not fully collaborate UAVs, edge servers and cloud servers for serving
devices, indicates that more efforts are required when researchers investigate
resource management problems for formulating a comprehensive architecture.

###  2.3. Wireless Communication Models

In the edge computing environment, especially UEC, wireless communication
plays a significant role in connecting devices, UAVs and edge servers.
Multiple devices can access UAVs and edge servers for data and resources via
wireless communication concurrently. The wireless interference incurred may
significantly impact their achievable data rates and lower their quality of
experience (Chen et al., 2016; Xia et al., 2022c). Thus, the interference must
be considered during resource management in UEC. There are two major schemes
of wireless communication between devices and servers, i.e., Orthogonal
Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA).

Under the OMA scheme, the resources, i.e., frequency and time, can be divided
into sub-resources to achieve the minimum interference between servers and
devices. The most popular OMA schemes in existing studies are Time Division
Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), and
Orthogonal Frequency-Division Multiple Access (OFDMA). Connectivity, i.e.,
multi-device access to the radio resources in cellular systems including the
Global System for Mobile Communication, the Long Term Evolution and Long Term
Evolution-Advanced is ensured with OMA-based multiple access. As a new
multiple access scheme implemented in 5G, NOMA allows non-zero cross-
correlation signals. In this way, NOMA provides connectivity for massive
devices and enhances the spectral efficiency significantly, compared to
conventional OMA schemes (Ding et al., 2017). In the edge computing
environment, each edge server deployed with a base station is equipped with
multiple channels, and each channel is available for multiple device
accommodation at the same time while guaranteeing data rates by well-designed
transmit power allocation (Fu et al., 2019; Liu et al., 2017). The successive
interference cancellation is implemented in the NOMA scheme to increase data
rates of devices with poor channel conditions by treating the signals of
devices with better channel conditions in the same channel as noise (Fu et
al., 2019).

Apart from the OMA or NOMA schemes applied in UEC, the line-of-sight (LoS)
also needs to be considered when formulating resource management strategies.
This is because UAVs hover at high altitudes in the air to serve devices on
the ground, and can use the LoS links for data and task communication (Luo et
al., 2021a; Tun et al., 2020; Deng et al., 2021; Zhou et al., 2018b; Chen and
Zheng, 2020). Compared with traditional download and upload links, LoS links
are dominant over other communications due to their small-scale shadowing and
fading (Abrar et al., 2021). According to the communication protocol or scheme
selected in UEC scenarios, we can calculate the data rates of upload and
download links. For computation, energy-consumption and latency models, we
omit them here because those models have been clearly defined in related
surveys in edge computing such as (Luo et al., 2021b; Mao et al., 2017).

##  3\. Research Directions in UEC

Efficient resource management is the key to unlocking the full potential of
UEC. Resource management is therefore extensively studied in the existing
literature. We have identified three main aspects of resource management in
UEC as shown in Fig. 3. Firstly, UEC needs to deploy UAVs in a geographic area
to provide resources for potential services and/or tasks. Considering the
mobility of UAVs, the trajectory design should also be designed. Those are
referred to as resource provisioning. After that, the second aspect is
computational offloading required by various entities in the Things layer
(Figure 2). Efficient offloading strategies require intelligently deciding
where to offload and how much data or tasks to offload. The last aspect is to
allocate resources provisioned for offloaded data or tasks. Resource
allocation requires efficient allocation of communication, storage, and
computational resources in Things, Edge, and Cloud layers.

Figure 3. A taxonomy of key research directions in UEC for resource management

###  3.1. Resource Provisioning in UEC

Compared with servers in cloud computing, the servers, i.e., UAVs and edge
servers, are physically much smaller and easier to deploy and re-deployed in
UEC. In this way, resource provisioning, as the foundation research problem of
resource management in UEC, should be adaptive for dynamic user requests over
time. There can be under-provisioning or over-provisioning issues as a result
of unsuitable resource provisioning strategies. In under-provisioning, i.e.,
less available resources on UAVs and edge servers, it is hard to fulfil users’
Quality-of-Service (QoS) requirements, suffering huge delays and incomplete
services. However, over-provisioning would result in resource waste without
any benefit. Thus, it is vital to formulate suitable resource provisioning
strategies to dynamically adjust the requirements, such as low system cost,
high QoS and/or QoE.

Different from traditional edge computing, UAV plays a significant but
flexible role in UEC. Thus, when investigating resource provisioning problems
in UEC, UAV behaviours must be considered. Since the edge server deployment is
not unique in UEC, we will elaborate on the UAV deployment, UAV trajectory and
joint UAV deployment and trajectory in this section.

####  3.1.1. UAV Deployment

In a number of the existing studies, UAVs are equipped with small-scale edge
servers with low-capacity computing and storage resources, called UAV-mounted
edge servers. Equipped with such a UAV-mounted edge server, a UAV can hover at
a specific location to provide services to devices within its wireless
communication coverage. Except for the coverage, the hover location of a UAV
and the external factors at this location also impact the distance from
devices, the data rate received by those devices and the power consumption of
this UAV. Thus, it is important to deploy UAVs in suitable hover locations for
maximizing the number of served tasks, maximizing the overall data rates
received by devices, minimizing power consumption and other objectives. In
(Wang et al., 2019b), the authors investigate the UAV deployment problem via
dispatching UAVs to a suitable hover position for serving the maximum number
of tasks from devices on the ground. Similarly, Sun et al. (Sun et al., 2020)
study the multiple UAV deployment problem to minimize the overall task
execution time. However, the UAV trajectory, consuming necessary energy and
impacting the waiting time of tasks, is not considered in the above studies.

####  3.1.2. UAV Trajectory

As the UAV deployment decides the hover locations of UAVs, another key to the
success of providing long-term and stable services is how to design the
trajectory of UAVs to properly utilize both resource capacities on UAVs and
the energy of UAVs in a dynamic manner. This is also a unique characteristic
to be considered when managing resources in UEC, fundamentally different from
transitional edge computing. In (Zhang et al., 2020c), Zhang et al. study the
UAV trajectory in UEC for minimizing energy consumption while maximizing
benefits when scheduling the path of UAVs to collect data for processing.
Similarly, the authors of (Ye et al., 2020) design the UAV trajectory from the
view of a multi-hop routing schedule. In this study, UAVs visit multiple
positions for task collection and then visit the base station for transferring
tasks to edge servers. If those tasks cannot be completed by UAVs and edge
servers, the remaining tasks would be delivered from edge servers to the cloud
data centre via the Internet. However, in those studies, only the optimization
of flight is investigated without consideration of the number of dispatched
UAVs and the selection of hover positions for providing better services with
less energy consumption.

####  3.1.3. Joint UAV Deployment and Trajectory

A more comprehensive way to investigate resource provisioning in UEC is to
consider both UAV deployment and trajectory together since UAVs not only
deliver data or tasks but also serve devices. From the perspective of real-
world implementations, it is also important to consider UAV deployment and
trajectory at the same time. In (Wu et al., 2021), the demand for vehicles is
simulated based on the real-time traffic conditions predicted by the cloud
server. According to both the number of UAVs to be dispatched and traffic
status, the authors design a deep reinforcement learning approach to decide
the hover positions and design the flight path of UAVs to those positions.
However, the scheduling of the number of dispatched UAVs is not considered in
this paper. To reasonably utilize the resources of UAVs while minimizing
overall costs such as energy consumption and UAV rent cost, researchers need
to investigate the joint UAV deployment and trajectory problem more
comprehensively, considering the number of involved UAVs, the hover position
and corresponding time for serving devices and the UAV trajectory that visits
required positions with high system throughput and low service latency.

Table 4. A Comparison Resource Provisioning Strategies in UEC Paper |  Provisioning Type |  Objectives |  Solutions |  Benefit
---|---|---|---|---
(Wang et al., 2019a) |  UAV deployment |  Maximize the number of served tasks |  Iterative algorithm |  Providing efficient solutions considering coverage constraint and task execution deadline in the dynamic UEC environment
(Sun et al., 2020) |  UAV deployment |  Minimize task completion time |  Successive convex approximation |  Providing optimal solution considering limited computing power of UAVs
(Zhang et al., 2020c) |  UAV trajectory |  Minimum energy consumption and maximum reputation gain |  Game-theoretical approach |  Ensuring the quality of user experience by guaranteeing data rate while considering the moving speed of the UAV
(Ye et al., 2020) |  UAV trajectory |  Minimize UAV’s energy consumption |  Heuristic algorithm |  Offering efficient solutions considering communication conditions in the dynamic UEC environment
(Cheng et al., 2018) |  UAV trajectory |  Maximize overall data rate |  Iterative algorithm |  Offering efficient solutions considering limited energy of UAVs in the dynamic UEC environment
(Wu et al., 2021) |  UAV deployment and trajectory |  Minimize energy consumption and maximize system reward |  Genetic algorithm and deep reinforcement learning |  Proposing a combination approach to solve both UAV deployment and trajectory problems

###  3.2. Computation Offloading in UEC

Computational offloading is the prime motivation for enabling UEC and one of
the most important research problems. It is therefore the most studied topic
in resource management in UEC. In this subsection, we summarize the key
research issues and the proposed techniques as solutions in the existing
literature. To provide high performance with low latency in UEC, tasks or data
should be offloaded to proper servers, i.e., edge servers and UAVs. In this
case, there are two major types of offloading modes adopted in the existing
studies, such as binary offloading and partial offloading.

####  3.2.1. Binary Offloading

In the UEC environment, binary offloading means that the tasks or data have to
be locally executed or offloaded to UAVs, edge servers or the remote cloud as
a whole. Since the decision can only be true or false, it is binary. This mode
has been adopted in several studies, (Apostolopoulos et al., 2021; Shimaday et
al., 2020; Xu et al., 2020a; Wang et al., 2021b; Ouyang et al., 2021), as it
is easy to implement and suitable for relatively simple tasks. Apostolopoulos
et al. (Apostolopoulos et al., 2021) study a data offload problem in UEC where
UAVs are additional servers to associate edge servers on the ground. Once a
UAV collects data from a user, the UAV needs to determine whether to execute
it locally or send it to edge servers. Aiming to maximize all users’
satisfaction utility, the authors propose a game-theoretical algorithm. A
similar problem has been studied in (Xu et al., 2020a). In this work, UAVs are
relays for creating communication between devices and edge servers when the
nearby base stations have been destroyed in a disaster. However, the situation
studied in this study is ungrounded. In (Shimaday et al., 2020), the authors
propose a workload balance model for UAVs while ensuring low service latency.
In this model, the tasks can be either executed locally or transferred to
another UAV via wireless communication. Another binary offloading study is
proposed in (Zhang et al., 2020a). This study tackles the scenario that an
edge server has been damaged in the wild while some devices need to be served.
The authors propose a game-theoretical approach to offload the tasks from each
un-served user to a newly involved UAV or to a nearby edge server that
directly covers this user. Unfortunately, this study failed to solve the
complicated UEC scenarios with multiple UAVs and edge servers. In (Ouyang et
al., 2021), the authors also deal with a binary offloading problem in UEC. A
trust-based task offloading scheme is proposed to maximize the energy
efficiency and reliability of the offloading process. A cluster-based task
offloading is proposed where tasks only need to route to any IoT devices
within a cluster. Similar to (Zhang et al., 2020a), this work can only deal
with a single UAV. In addition, for all the above studies, the binary
offloading mode may waste idle resources and become the performance bottleneck
of the overall system performance.

####  3.2.2. Partial Offloading

Compared with binary offloading, partial offloading allows a task to be
partitioned into multiple sub-tasks. Part of those sub-tasks can be partially
executed on the device, and others can be offloaded at different UAVs and edge
servers. In this mode, the offloading is more complicated, since it requires
task partition in advance. However, this mode is more effective than binary
offloading, since it can utilize segmented resources on devices, UAVs and edge
servers. In (Guo et al., 2021), the authors use the coded distributed
computing (CDC) scheme to divide the original task received by a UAV into a
set of sub-tasks, and then distribute those sub-tasks to multiple edge servers
for execution. In this way, task processes in UEC can be distributed and
parallel. However, this study ignores the edge server network and only
considers the single edge server scenario. The authors of (He et al., 2021a)
also consider the partial offloading mode in UEC. In this study, the tasks are
treated as a continuous data stream and once a UAV receives such steam, it
directly executes part of the tasks on its computing resources. If there exist
tasks waiting for execution, the UAV will offload them to other UAVs. A
partial offloading approach is investigated in (Bai et al., 2019) where a UAV
is considered as a user and offloads computational tasks to the nearby edge
server. The study focuses on the three following research questions: a) What
is the volume of the computational tasks to be offloaded? 2) what is the
suitable duration of offloading, and c) How much power should be assigned to
the offloaded signal? This study investigates secure offloading techniques
focused on physical-layer security for both active and passive eavesdroppers
and transforms them into convex problems. Unfortunately, this study only
focuses on a single UAV again, limiting its feasibility and implementation. A
similar study has been carried out in (Gu et al., 2021) where secure partial
offloading is studied in a linear energy harvesting-enabled UEC environment.
The study considers secrecy as an offloading constraint along with latency
constraints to minimize energy consumption and maximize the energy storage of
the UAV. The study focuses on data-partitioned-oriented applications where
data are considered to be partitioned arbitrarily for parallel processing. The
improvement of this study could be considering non-linear energy harvesting
scenarios.

Table 5. A Comparison of Computational Tasks or Data Offloading in UEC Paper |  Objective |  Method |  Constraints |  Mode
---|---|---|---|---
(Apostolopoulos et al., 2021) |  Maximize satisfaction utility |  Game-theoretic approach |  Users’ latency and energy requirements |  Binary
(Shimaday et al., 2020) |  Minimize service delay |  Integer programming solver |  Not mentioned |  Binary
(Xu et al., 2020a) |  Minimize service latency, and energy consumption of UAVs |  Greedy approach |  Not mentioned |  Binary
(He et al., 2021a) |  Maximize network computing rate |  Iterative algorithm |  Bandwidth and UAV battery constraints |  Binary
(Wang et al., 2021b) |  Minimize overall task processing delay |  K-means and convex toolkit |  computation capacities and UAV battery constraint |  Binary
(Zhang et al., 2020a) |  Minimize overall cost, consisting of latency and energy consumption |  Game-theoretic approach |  Not mentioned |  Binary
(Guo et al., 2021) |  Minimize overall cost, consisting of latency and energy consumption |  Mixed Integer programming solver |  Fly speed |  Partial
(Wang et al., 2021a) |  Minimize maximum processing delay |  Reinforcement Learning |  UAV battery capacity and task deadlines |  Partial
(Dai et al., 2020) |  Maximize UAV offloading efficiency |  Game-theoretic approach |  Computing capacities of UAV and edge servers |  Partial
(Ouyang et al., 2021) |  Minimize energy consumption of UDs |  Cluster-based offloading, ant colony optimization |  Trust, trajectory, energy, and flight duration |  Binary
(Gu et al., 2021) |  Minimize energy consumption of UAV |  Convex optimization |  Energy consumption, latency, and security |  Partial
(Bai et al., 2019) |  Minimize energy consumption of UAV |  Convex optimization |  Energy consumption, latency, and security |  Partial

###  3.3. Resource Allocation in UEC

As a significant research issue of resource management in UEC, it is important
to devise and thoroughly evaluate different resource allocation strategies
effectively and efficiently for executing offloaded tasks. In general,
computing and communication resources on devices, UAVs and edge servers are
the main ones to be managed in UEC. Typical computing resources include CPUs,
GPUs, memory, storage, etc., whilst the main communication resources refer to
bandwidth, power, link and spectrum. Considering the power limit on devices
and UAVs, power for harvesting has become a new research trend in resource
management, different from power allocation in wireless communication. In this
section, we discuss the resource allocation studies in computing,
communication, harvesting and joint resource allocations.

Table 6. A Comparison of Resource Allocation Approaches in UEC Paper |  Objective |  Resource Direction |  Resource Type |  Method |  Benefit
---|---|---|---|---|---
(Liwang et al., 2021) |  Maximize both seller’s and buyer’s expected utilities |  Edge server $\rightarrow$ UAV |  Computing resource |  Bilateral negotiation algorithm |  Considering computation and storage resource constraints on edge server in the dynamic UEC environment
(Xu et al., 2021) |  Maximize edge server’s profit and maximize UAV’s revenue |  Edge server $\rightarrow$ UAV |  Computing resource |  Stackelberg game and blockchain |  Solving problems in an online and distributed manner while using blockchain to enhance security
(Yang et al., 2019) |  Minimize power consumption of devices and UAVs |  UAV $\rightarrow$ Things |  Computing resource |  Iterative algorithm |  Thouroughly considering coverage, computation capacity, communication power and computation power of the UAV and edge server
(Wang et al., 2021c) |  Minimize overall system cost |  UAV $\rightarrow$ Things |  Communication and computing resources |  Multi-agent reinforcement learning |  Considering bandwidth allocation with task failure penalty
(Wu et al., 2020) |  maximize average spectral efficiency and overall network throughput |  Edge server and UAV $\rightarrow$ Things |  Communication and computing resources |  Threshold-based greed scheme |  Monitoring and managing UAV moving speed with coverage limitations in the dynamic UEC environment
(Asheralieva and Niyato, 2019) |  Minimize overall cost with low-latency |  Edge server and UAV $\rightarrow$ Things |  Communication and computing resources |  Blockchain, Stackelberg game, and deep Q-learning |  Using game theory and deep learning together to ensure the performance of the proposed solution in the dynamic UEC environment while implementing blockchain to enhance trust and security
(Zhou et al., 2018a) |  Maximize sum computation rate |  UAV $\rightarrow$ Things |  Computing, communication and harvesting resources |  Iterative algorithm |  Utilizing energy harvesting technique to enhance the sustainable usage of UAV and end-devices with consideration of UAV fly speed limit
(Feng et al., 2021) |  Maximize sum computation rate |  UAV $\rightarrow$ Things |  Computing, communication and harvesting resources |  Heuristic algorithm and convex toolkit |  Ensuring low-latency task completions while utilizing energy harvesting technique

####  3.3.1. Computing Resource Allocation

As mentioned above, resource allocation aims to optimize the computation
offloading process for various goals, including QoS, service latency, energy
consumption system utility, etc. Thus, computing resource allocation is key to
the success of achieving effective computation offloading in UEC. Without
proper computing resource allocation, the number of un-served or delayed tasks
would increase dramatically, similar to under-provisioning discussed in 3.1.
Thus, many researchers have started to investigate how to effectively utilize
computing resources provided by UAVs and edge servers in UEC. Liwang et al.
(Liwang et al., 2021) investigate the resource trading problem between a UAV
and its nearby edge server. They design a beneficial and risk-tolerable
forward contract for negotiation between the UAV and edge server before the
real resource transaction happens. In (Xu et al., 2021), the authors also
study the computing resource allocation from edge servers to UAVs in UEC.
However, this resource allocation scenario is more complicated, since there
are multiple UAVs obtaining computing resources from nearby edge servers in
the coverage of UAVs. Rather than allocating computing resources from edge
servers to UAVs, the authors of (Yang et al., 2019) propose an energy-
efficient resource allocation approach to allocate computing resources of UAVs
to devices on the ground.  The main objective of this work is to improve the
energy efficiency of multiple UAVs in a UEC system. It introduces a low-
complexity algorithm that jointly optimizes power control, user association,
resource allocation, and location planning.

####  3.3.2. Computing and Communication Resource Allocation

As the ”last mile” of delivering servers to devices in UEC, the impacts of
wireless communication must be considered when investigating resource
management problems in UEC. As mentioned in Section 2.3, multiple access
schemes are enabled to power the 5G wireless network in UEC. In this way, the
incurred interference can impact the QoS of users significantly by reducing
received data rates, which widely exist in UEC. Thus, the communication
resources, including bandwidth, power, link and spectrum, also need to be
considered together with computing and storage resources to improve the
service quality and performance. In (Wang et al., 2021c), bandwidth is the
main communication resource that is considered when allocating computing
resources from UAVs to devices in the Things layer. In this work, the latency
requirements of tasks must be fulfilled to ensure the safety of devices, e.g.,
vehicles. To achieve this, the authors consider the computing resources,
channel efficiency and bandwidth usage together as the optimization goal of
this study. The authors of (Wu et al., 2020) consider the changes in
communication statuses, e.g., channel gains, available power and bandwidth,
between the UAV and devices on the ground. To provide high-quality offloading
services to devices covered by the UAV flight, a theoretical framework is
proposed in this paper to maximize the average spectral efficiency and overall
network throughput. Asheralieva et al. (Asheralieva and Niyato, 2019)
investigate the resource management and corresponding pricing problem in UEC.
Because normally there are multiple infrastructure providers in the UEC
environment, this work studies how to enable various providers to work
together with incomplete information to suitably allocate available resources
to minimize the overall cost with low service latency.

####  3.3.3. Computing, Communication and Harvesting Resource Allocation

The energy harvesting techniques, e.g., wireless power transfer, have been
treated as a potential solution for IoT devices to improve the battery
performance (Tang et al., 2017, 2018). However, the heavy propagation loss can
significantly degrade the harvested power level. To improve the efficiency of
energy transfer, the UAV-enabled wireless power transfer architecture is
proposed and widely adopted in recent studies (Zhou et al., 2018a; Feng et
al., 2021; Wang et al., 2017; Xu et al., 2018). Under this architecture, UAVs
perform as a transmitter for powering devices nearby. Due to the short-
distance line-of-sight (LoS) energy transmission links between UAVs and
devices, the harvested power level is significantly improved. With the
advantages of the integration of energy harvesting and UEC, the device battery
life, service time and available energy for computation tasks can be largely
extended. In (Zhou et al., 2018a), a multi-stage approach is proposed to
maximize the computation rate in a UEC scenario by properly allocating
computing resources, with consideration of energy harvesting and UAV flight
speed constraints. Specifically, this study considers both binary and partial
task execution modes with task completion deadlines. Feng et al. (Feng et al.,
2021) study this joint resource allocation problem in UEC under the non-
orthogonal multiple access (NOMA) scheme. Similar to (Zhou et al., 2018a),
energy is also transferred from UAVs to devices by the joint technique of
wireless power transfer and NOMA. To ensure the performance in both binary and
partial task offloading, a deep deterministic policy gradient framework is
proposed for allocating computing, communication and harvesting resources.

###  3.4. Joint Resource Management in UEC

Many existing studies focus on resource management more than one aspect of
resource management. We classify these studies into four categories based on
their research focus as shown in Fig. 4. The four categories are a)
Provisioning and Offloading, b) Offloading and Allocation, c) Allocation and
Provisioning, and d) Offloading, allocation, and provisioning. We provide an
overview of the existing studies in these four categories in this subsection.

####  3.4.1. Joint Provisioning and Offloading

When tackling the resource management in UEC with the special characteristics
of UAVs, a number of the existing studies start to investigate both
computation offloading and resource provisioning at the same time (Wang et
al., 2019b; Ning et al., 2021; Sun et al., 2021; Hu et al., 2020, 2018;
Khochare et al., 2021). In (Wang et al., 2019b), the authors consider the
joint UAV deployment and task scheduling problem for large-scale users in UEC.
The Things-UAV collaboration is adopted in this work to provide services to
users by UAVs. This work aims to minimize the system energy consumption
including computational energy consumption and hover energy consumption whilst
ensuring all tasks can be completed. A similar study is carried out in (Ning
et al., 2021) where UAVs act as servers to process tasks offloaded from
devices on the ground. In this work, the optimization objective is to minimize
the overall system cost, consisting of the energy consumed on UAVs and the
latency of task completion. To solve this problem, a game-theoretical approach
is designed to find the solution effectively and efficiently. However, the
coverage constraint, energy limit and UAV movement are not considered,
weakening the novelty of this study. Different from the above studies which
consider UAV deployment and computation offloading, the authors of (Sun et
al., 2021) study the joint UAV trajectory and computation offloading problem,
with similar aims of (Ning et al., 2021). Rather than the cost, the authors of
(Hu et al., 2020) focus on maximizing task completion and the authors of (Hu
et al., 2018) focus on minimizing the delay over time when solving the joint
UAV trajectory and computation offloading problem. Unfortunately, both (Ning
et al., 2021) and (Hu et al., 2018) only consider the single UAV scenario.
Khochare et al. (Khochare et al., 2021) investigate a co-scheduling problem of
analytic tasks and UAV routing to maximize the overall utilities of UAVs and
achieve the analysis completion before the deadline. However, there is still a
research gap that the UAV deployment, UAV trajectory and computation
offloading at the same time have rarely been studied.

Figure 4. A taxonomy of joint resource management Table 7. A comparison of Joint Resource Management Studies in UEC Papers |  Objectives |  Research Focus
---|---|---
(Wang et al., 2019b) |  Minimize system energy consumption while ensuring all tasks can be completed |  Provisioning and Offloading
(Ning et al., 2021) |  Minimize system computation cost, including latency and energy
(Sun et al., 2021) |  Minimize UAV’s energy consumption
(Hu et al., 2020) |  Maximize weighted sum complete task-input bits
(Hu et al., 2018) |  Minimize the sum of maximum delay over time
(Khochare et al., 2021) |  Maximize utility
(Chen et al., 2020) |  Maximize computation performance of UD |  Allocation and Offloading
(Yu et al., 2020) |  Minimize the service delay of all UDs and energy consumption of UAVs
(Seid et al., 2021) |  Minimize computation cost
(Liu et al., 2020b) |  Maximize computation capacity
(Liu et al., 2020c) |  Maximize Energy Efficiency
(Jeong et al., 2017) |  Minimize energy consumption of UDs |  Allocation and Provisioning
(Li et al., 2020a) |  Minimizing energy consumption of UAVs
(Mao et al., 2020) |  Minimize the maximum computation delay of UDs
(Wan et al., 2019) |  Enhance UAV coverage
(Ji et al., 2020) |  Minimize weighted-sum energy consumption UDs and UAVs
(Mei et al., 2019) |  Maximize the energy efficiency of an UAV
(Zhao et al., 2021) |  Minimize energy consumption of UAVs |  Offloading, Allocation and Provisioning
(Dai et al., 2021) |  Minimize energy consumption of All UDs
(Hu et al., 2019) |  Minimize energy consumption of UDs and UAVs
(Liu et al., 2019b) |  Minimize energy consumption of UAVs
(Ti and Le, 2018) |  Minimize energy consumption of UDs
(Liu et al., 2021c) |  Maximize system-wide computation capacity
(Luo et al., 2021a) |  Minimize user energy consumption

####  3.4.2. Joint Offloading and Allocation

Several studies consider both offloading and allocation of resources to
improve various QoS attributes in different UEC scenarios. For instance, a
joint optimization approach is proposed in (Yu et al., 2020) under Things-UAV-
Edge collaboration settings to optimize resource and task allocation. The
proposed UEC scenario consists of edge servers, a UAV, and a few IoT devices.
The UAV works as an edge server and relay in this study. The goal is to
improve the latency for all IoT devices and the UAV’s energy efficiency,
however, this study is still a single UAV scenario. A similar study is carried
out in (Liu et al., 2020c) to reduce users’ energy consumption by optimizing
task offloading and resource allocation processes using the convex
optimization method and stochastic learning automata algorithm. A set of UAVs
is considered that work as edge servers instead of a single UAV. A cooperative
UEC environment is studied in (Liu et al., 2020b) where UAVs assist one
another in executing computational tasks. The study aims to mitigate the
interference from UAVs to devices while maximizing the long-term utility of
the studied UEC network using deep reinforcement learning algorithms. It
considers both centralized and distributed UEC environments. An age of
information-aware resource management approach is proposed in (Chen et al.,
2020) where the freshness of information is considered during the maximization
of each ground user’s long-term computing performance. The limitation of this
study is its assumption assumes that the UAV and the ground users move at the
same speed following a Markov mobility model, which is unlikely true in real-
world scenarios. The proposed approach formulate the problem as a stochastic
game and proposes a novel online deep reinforcement learning scheme. A multi-
UAV-based UEC environment is studied in (Seid et al., 2021) where a cluster of
UAVs is provisioned to enable offloading services. The goal is to reduce the
network’s computation cost and meet the QoS requirements of IoT devices. It
leverages a multi-agent deep reinforcement learning-based approach to reduce
the cost. However, all the above studies ignore the significant characteristic
of UEC, the movement of UAVs.

####  3.4.3. Joint Allocation and Provisioning

Joint resource allocation provisioning in UEC mostly involves optimizing UAV
deployment, trajectory, and computing and communication resource allocation.
For instance, an energy-efficient optimization approach is proposed in (Li et
al., 2020a) for resource allocation and trajectory planning. The study
considers a scenario where a UAV acts as an edge server and collects and
processes the computational tasks from users. The goal is to minimize the
energy consumption of UAVs by jointly optimizing their trajectories, users’
transmission, and computing task allocation. The study utilizes the following
two algorithms to perform the optimization: a) the Dinkelbach algorithm, and
b) the SCA algorithm. A three-layer online big data processing framework is
proposed in (Wan et al., 2019) where a moving UAV collects and performs
initial processing of raw data generated by IoT devices and sends it to the
cloud. The aim is to efficiently allocate bandwidth and optimize path planning
to efficiently collect distributed data from sensors. In this study, an online
edge scheduling approach is leveraged to optimize the proposed resource
allocation problem. The online path planning problem is solved using a deep
reinforcement learning algorithm. An energy consumption minimization approach
is proposed in (Jeong et al., 2017) where a UAV offers computational
offloading services to a set of mobile users. The approach aims at minimizing
the energy consumption of mobile users while maintaining the expected QoS of
the mobile application. An SCA-based algorithm is utilized to optimize energy
consumption. The proposed approach considers both OMA and NOMA schemes for
communication. However, this is a single UAV scenario again. In addition, the
authors assume that all the tasks can be purely completed by local resources.
In a similar setting, an energy consumption minimization approach is proposed
in (Ji et al., 2020) where the aim is to improve the energy efficiency of the
UAV and user devices. An ADM-based approach is proposed to optimize computing
resources. Some studies consider a space-air-ground integrated network (SAGIN)
to enable UEC. For instance, the authors in (Mao et al., 2020) utilize a SAGIN
and assume that ubiquitous cloud services will be enabled via satellite. In
this case, UAVs will provide computing services to remote IoT devices with the
help of satellites. The aim is to minimize the service delay by optimizing
task association, resource allocation, and deployment position using block
coordinate descent and SCA-based approaches. The shortage of this study is the
ignorance of UAV trajectory in UEC. A network function virtualization-based
approach is leveraged in (Mei et al., 2019) to assist IoT devices in
computational tasks. The study proposes a BCD-based algorithm to reduce the
energy consumption of a UAV using optimal resource allocation and trajectory
planning. Unfortunately, the collaborations between UAVs or between UAVs and
edge servers are not considered.

####  3.4.4. Joint Provisioning, Offloading and Allocation

Several studies have considered all three aspects of resource management,
including resource provisioning, computation offloading and resource
allocation, to maximize the performance of different UEC systems (Zhao et al.,
2021; Liu et al., 2021c, 2019b; Luo et al., 2021a). A fairness-aware approach
is proposed in (Zhao et al., 2021) to optimally allocate resources and perform
task scheduling. The goal is to minimize the energy consumption of a UAV where
it acts as an edge server for multiple ground nodes. A mixed-integer nonlinear
programming problem is formulated to solve the proposed optimization problem.
The study utilizes an iterative algorithm to find the optimal solution
considering the resource allocation, trajectory, bits scheduling, and task
decision. The proposed approach considers fairness among the ground nodes and
different operations of offloading services. A new type of UEC system is
studied in (Liu et al., 2021c) where edge computing services are provided via
a UAV to a platoon of vehicles that are capable of wireless power
transmission. The UAV may recharge from these vehicles to maximize its
lifetime and computing power. An SCA-based method is leveraged to find the
optimal solution. An energy-efficient scheduling problem is studied in (Dai et
al., 2021) where multiple UAVs and base stations are provisioned to enable
edge services. The main goal is to maximize the lifetime of mobile devices in
the proposed UEC environment. It optimizes trajectories, task associations,
and resource allocations to reduce energy consumption. The optimization
problem has three sub-problems that are solved using a hybrid heuristic and
learning-based scheduling algorithm. Collision avoidance is a critical
research issue when dealing with multiple UAVs. A two-layer optimization
approach is proposed to minimize the energy consumption of the ground users in
(Luo et al., 2021a). It jointly optimizes task scheduling, bit allocation, and
trajectory. The first layer utilizes a bidding optimization method based on
dynamic programming for the task scheduling process. The second layer
leverages the ADMM to optimize the trajectory planning and bit allocation. It
also considers a dynamic service strategy, optimal task scheduling, and a
collision avoidance strategy.

Mobility is a key concern in UEC environments as it may impact QoS
significantly. Although most of the existing studies consider either IoT
devices or UAV’s positions to be fixed, there are a few studies that consider
the mobility of both (Liu et al., 2019b; Ti and Le, 2018). For instance, a UEC
environment is studied in (Ti and Le, 2018) where both mobile users and UAVs
may change their locations over time. The total power consumption is reduced
using a joint optimization technique that considers task offloading,
transmission power allocation, user association, and UAV path planning. The
proposed problem is presented as a MINLP and solved with an iterative two-
phase algorithm. A wireless-powered UEC environment is studied in (Liu et al.,
2019b) which assumes that a UAV is capable of energy transmission and provides
IoT devices with energy and computation capabilities. The aim of this work is
to minimize the UAV’s energy consumption by finding the optimal offloading
resource allocation, transmission power, bit allocation, and trajectory
planning. This work claims that most of the studies do not take into account
the effect of acceleration on energy consumption. Therefore, the effect of
both UAV’s velocity and acceleration are considered during the optimization.
The study leverages SCA-based and alternative, decomposition, and iteration
(DAI)-based algorithms to solve the problem.

##  4\. Key Techniques and Performance Indicators

Various resource management technologies are essential to achieve the optimal
management of resources in UEC for achieving the QoS and QoE requirements of
users. Recently, a number of state-of-the-art technologies have been proposed
for resource management in UEC. According to the control of information
exchange and strategy formulation in UEC systems, those technologies can be
distinguished as centralized and distributed technologies. In general,
centralized technologies mainly consist of convex optimization techniques,
centralized machine learning, heuristic, greedy and iterative algorithms,
while distributed technologies mainly consist of game theory, auction methods,
blockchain and federated learning. In this section, we will discuss both
centralized and distributed technologies and analyze their advantages and
disadvantages. After that, we will summarize the main performance indicators
used in the existing resource management studies in UEC, including energy
consumption, throughput, latency, cost, and utility.

Figure 5. A taxonomy of key techniques

###  4.1. Centralized Technologies

Centralized technologies are widely implemented in the existing studies due to
their advanced performance and intuitive design. In this section, we will
review several widely-used centralized technologies.

####  4.1.1. Integer Programming Solver

In most of the existing studies of resource management in UEC, including
computation and data offloading, resource allocation and resource
provisioning, the investigated problems usually are formulated as a 0-1 or
mixed integer programming model (Zhang and Ansari, 2021; Mukherjee et al.,
2020; Qu et al., 2021; Zhang et al., 2019; Zhou et al., 2022; Deng et al.,
2022). Once the integer programming model is formulated according to the
problem itself, including objective(s) and constraint(s), this mathematical
model can be converted into equations as the input of integer programming
solvers, such as Gurobi111http://www.gurobi.com/ and IBM CPLEX
Optimizer222https://www.ibm.com/analytics/cplex-optimizer. Since those solvers
have been produced maturely for years, mathematical models can be implemented
in different language versions of those solvers, including C, Java, Python and
many others. For example, the integer programming technology is applied in
(Guo et al., 2021) for finding the optimal solution to the mixed integer
programming model formulated by coded distributed computing.

In general, integer programming solvers offer several advantages. Firstly,
they are extensively developed and maintained by industry leaders like IBM.
Secondly, this technology can be readily implemented for mathematical problem-
solving, which is a commonly adopted approach in many existing studies on
resource management in UEC. Thirdly, integer programming solvers are capable
of finding optimal solutions to problems. Lastly, these solvers offer
adaptability with various parameters, such as execution time. However, a
significant drawback of this technology is its inherent high computational
complexity. Solvers aim to find the optimal solution for given integer
programming models, which typically involve NP-hard or non-convex problems.
While certain solvers like CPLEX allow users to limit the execution time
through parameters, setting strict time limits often leads to a substantial
decrease in performance. For instance, in experiments conducted in (Xia et
al., 2021a, 2022b), the performance of integer programming solvers was
inferior to that of iterative approaches even when the time limit was set
between 10 to 100 seconds. Considering the low-latency service requirements in
UEC, integer programming solvers are often impractical in real-world
scenarios. Their limitations make them less suitable for most UEC
applications.

####  4.1.2. Convex Optimization

As mentioned in Section 4.1.1, the problems investigated in resource
management in UEC are typically NP-hard or non-convex, it is impractical to
find the optimal solution by force-bruce algorithms or integer programming
solvers. Since the convex optimization technology is mature in the area of
wireless communication, many researchers attempt to convert a non-convex
problem into a convex or near-convex problem, then apply a feasible convex
optimization solution to solve the converted problem, such as (Su et al.,
2021). Currently, the successive convex approximation (SCA), and Lyapunov
optimization are decomposition techniques, the main techniques used in
resource management for converting a non-convex problem into a convex or near-
convex problem. For example, in (Tang et al., 2022), the Lagrangian duality
method and SCA technology are utilized for formulating computational
offloading strategies to minimize energy consumption in UEC. The Lyapunov
optimization technology is applied in (Lin et al., 2022) to dynamically
allocate resources from UAVs to nearby mobile devices in UEC, aiming to
complete computational tasks with minimum system cost and maximum system
utility. Specifically, the authors formulate a queuing model for tasks on each
edge server to keep the system stable.

When comparing the use of integer programming solvers with convex optimization
techniques for resource management problems, it is evident that the
computation complexity of the converted problems through convex optimization
is significantly reduced. Additionally, convex optimization provides a
theoretical bound that guarantees performance. However, it is important to
note that the services offered by UEC require millisecond-level response
times. Consequently, implementing convex optimization technology to solve
resource management problems in UEC becomes challenging due to its relatively
high computation complexity. Meeting the stringent time constraints while
utilizing convex optimization techniques remains a significant obstacle in UEC
resource management. In general, both integer programming and convex
optimization are suitable for UAV and edge server deployment problems, since
these problems typically do not have strict real-time constraints, allowing
for the use of computationally intensive methods.

####  4.1.3. Centralized Machine Learning

Recently, researchers are starting to solve resource management problems in
UEC by machine learning, such as reinforcement learning, deep learning and
others (Xue et al., 2022; Zhao et al., 2022; Liu et al., 2022; Zhang et al.,
2022a; Cheng et al., 2022a, b, 2021a). For most techniques of integer
programming solvers and convex optimization, they hardly handle the dynamics
and uncertainty of UEC because they usually require complete information about
everything, including edge servers, UAVs, devices, environment and mobility.
Leveraging the benefits of machine learning, the dynamic resource management
problems can be effectively solved by being formulated as Markov Decision
process models, which can be solved by machine learning techniques, e.g.,
reinforcement learning. To name a few, Zhao et al. (Zhao et al., 2022) propose
a deep reinforcement learning approach to solve the computation offloading
problem in UEC. The proposed approach aims to achieve the minimum service
delay and energy consumption, based on the Markov decision process. Similarly,
Liu et al. (Liu et al., 2022) also implement a deep reinforcement learning
approach based on the Markov decision process to solve the computation
offloading problem in UEC with the only aim to minimize the total service
latency.

Centralized machine learning technologies have their own advantages and
disadvantages when tackling resource management problems. With a set of
suitable parameters, machine learning technologies can provide near-optimal
solutions to resource management problems in UEC, without complicated approach
designs and high execution overhead due to the short time of prediction.
However, such technologies require significant computational resources during
training with large datasets. The learning process itself can be time-
consuming in order to achieve optimal performance. Furthermore, machine
learning models often involve a vast number of parameters that require
adjustment. Thus, centralized machine learning is more suitable for the
algorithms running in the cloud server in a centralized manner to provide
global control.

####  4.1.4. Heuristic, Greedy and Iterative Algorithms

Apart from the above technologies applied when solving resource management
problems in UEC, the heuristic, greedy and iterative (HGI) algorithms are the
most popular method to tackle those NP-hard or non-convex problems with short
computational overheads. There is a special category of those algorithms,
named approximation algorithms. The difference between approximation
algorithms from other algorithms is that the approximation ratio, i.e., the
ratio of performance of the optimal solution over the performance of the
solution provided by the approximation algorithm, can be theoretically
analyzed. In this way, the theoretical performance can be guaranteed. For most
of HGI algorithms, they are normally designed from the view of the
optimization objective, associating with the existing techniques such as
greedy algorithms, dynamic programming and local search and relaxation (Lee et
al., 2022; Zhang et al., 2022b; Yu et al., 2022; Chiu et al., 2022; Tao et
al., 2021). In (Hou et al., 2019), the authors propose a heuristic algorithm
for formulating computation offloading strategies, aiming to minimize energy
consumption. Chen et al. (Chen et al., 2022) implement a greedy algorithm with
genetic algorithm operators to solve the joint computation offloading and
deployment optimization problem in UEC, aiming to minimize the task response
time.

Compared with the methods in Sections 4.1.1, 4.1.2 and 4.1.3, HGI algorithms
can solve the NP-hard or non-convex problems with low computation complexity.
Those approaches are relatively simple, direct and easy to be implemented.
Especially for approximation algorithms, they can also provide a theoretical
bound to ensure performance in the worst cases. However, as the price of a
relatively simple design, HGI algorithms may fall into the local optimal trap,
weakening the performance on achieving optimization goals of resource
management in UEC. Benefiting from high efficiency, HCI algorithms are more
suitable for real-time decision-making scenarios in UEC.

###  4.2. Distributed Methods

In UEC, edge servers and UAVs are geographically distributed to cover
different areas wirelessly. Considering the nature of distributed computing
paradigm, a central control applied in centralized methods can become a
potential bottleneck from perspectives of resource, location and security. In
this section, we will review several widely-used distributed technologies.

####  4.2.1. Game Theory

As a powerful tool, game theory has been widely applied for solving edge
computing problems in a parallel and decentralized manner (He et al., 2019,
2021b; Cui et al., 2020, 2021; Ning et al., 2020; Shen et al., 2023). Under
the game theory framework, the iterations among multiple players, such as UAVs
and devices, can be analyzed to devise incentive and compatible mechanisms for
finding collectively satisfactory solutions. The action of each player can be
changed based on other’s actions for maximizing their interests. Once a Nash
Equilibrium achieves, no further actions will be changed and the solution is
formulated. For example, the authors of (Han et al., 2022) investigate the age
of information minimization problem and propose a stochastic game theoretical
approach. In addition, they propose a learning-based algorithm to achieve the
Nash Equilibrium. Tackling a similar research problem, Wang et al. (Wang et
al., 2022a) formulate it as an ordinary potential game to minimize the age of
information.

Game theoretical approaches, as distributed technologies, offer the advantage
of parallel execution, which significantly reduces the overhead associated
with formulating solutions. This reduction in execution overheads leads to a
decrease in service latency, making game theoretical approaches well-suited
for real-time decision-making scenarios. In game theory, reaching a Nash
Equilibrium is crucial for formulating solutions. However, it’s important to
note that there can be multiple Nash Equilibria in a game. The performance of
a game theoretical approach heavily depends on the selection of the Nash
Equilibrium for solution formulation. Obtaining the Nash Equilibrium with
optimal performance poses a challenge in game theoretical approaches.
Furthermore, game theory involves continuous iterations of information
exchanges and decision updates to achieve the Nash Equilibrium. These
information exchanges may introduce high communication overhead, which may
impact the overall efficiency of the approach. Managing and minimizing this
communication overhead is essential for ensuring the effectiveness and
scalability of game theoretical solutions.

####  4.2.2. Blockchain

As a decentralized system, blockchain has attracted significant attention from
academia and industry because of its distributed, irreversible and traceable
functions, etc. Blockchain has been applied in many applications in edge
computing, including data sharing (Yuan et al., 2021; Abdellatif et al.,
2021), resource allocation (Zhang et al., 2021), intrusion detection (Liu et
al., 2021b), identity authentication (Wang et al., 2021d), etc. Recently, some
blockchain systems have been implemented for supporting resource management in
UEC, including (Nilsen et al., 2022; Li et al., 2022a; Latif et al., 2022;
Singh et al., 2022). To name a few, the authors of (Masuduzzaman et al., 2022)
propose a blockchain-based approach to manage automated traffic in UEC, with a
vehicle detection algorithm. With blockchain, the automated traffic management
scheme can be ensured securely. In (Wang et al., 2022b), the authors design a
lightweight reputation-based blockchain scheme to improve both security and
efficiency when managing resources in UEC, with an improved Practical
Byzantine Fault Tolerance consensus mechanism.

Compared with other distributed technologies, blockchain is more advanced in
data integrity and security, due to the irreversible and traceable
characteristics of blockchain. Given the distributed nature of UEC and the
resource limitations of edge servers and UAVs, ensuring security and privacy
becomes a more complex task. These challenges can be addressed through the use
of blockchain technology. Therefore, blockchain is a viable solution when
there is a need to safeguard the security and privacy of servers, users, and
data. However, blockchain also has its disadvantages. Firstly, the latency
caused by generating transaction blocks is usually high, impacting the overall
performance of blockchain-based approaches. Secondly, the incentive mechanism
among edge servers, UAVs and devices should be carefully considered for
invoking them actively during resource management in UEC. In addition, the
resources on edge servers, UAVs and devices are limited, however, the
resources required for hosting blockchain are significant. Thus, lightweight
blockchain solutions are required for ensuring the performance and security of
resource management in UEC.

####  4.2.3. Federated Learning

As a prospective machine learning scheme, federated learning has emerged to
address the privacy and performance issues of traditional centralized machine
learning approaches (Li et al., 2020c). By federated learning, data collected
by devices can be hidden from edge servers and UAVs since only the training
metrics are uploaded from devices. In the past years, federated learning has
been applied to resource management in UEC (Cheng et al., 2022c, 2021b; Zhang
et al., 2020b). For example, the authors of (Rahbari et al., 2021) introduce a
federated learning approach to minimize the latency and energy consumption
during computational tasks and data offloading among UAVs. In this study, a
swarm of UAVs share resources and collaborate to improve the efficiency of
communication and offloading. Nie et al. (Nie et al., 2021) aim to minimize
the consumed power during the communication and computation process in UEC. To
deal with the user association, resource allocation and power control
problems, the authors propose a multi-agent federated learning approach in a
semi-distributed manner.

As mentioned above, the privacy of users’ data can be protected by federated
learning, since the raw data is only stored on the device, UAV or edge server
where such data are collected. This also releases the transmission burden in
wireless networks. In addition, federated learning helps collaborate resources
from devices, UAVs and edge servers to complete the highly complex training
process, which cannot be completed by a single device. However, federated
learning also faces shortages. The existing federated learning-based
approaches for resource management in UEC face malicious attacks, e.g., data
poisoning attacks (Tolpegin et al., 2020). Under the attacks, the performance
of those approaches cannot be guaranteed. In conclusion, federated learning is
a good option for processing learning tasks in UEC with limited computation
and storage resources..

###  4.3. Key Performance Indicators

In this section, we discuss the key performance indicators, i.e., energy
consumption, throughput, latency, resource utilization, utility and cost in
UEC for resource management. These indicators are identified based on the
objectives presented in table 4 \- 7.

####  4.3.1. Energy Consumption

A key challenge of resource management in UEC is to deal with limited energy.
Both IoT devices and UAVs have limited power. Many studies in UEC, therefore,
focus on minimizing the energy consumption of either IoT devices, UAVs, or
both (Hu et al., 2018; Liu et al., 2020c; Dai et al., 2021; Hu et al., 2019;
Ti and Le, 2018; Luo et al., 2021a). The existing studies mainly consider two
aspects of energy consumption in UEC: a) computation and transmission energy
consumption of IoT devices, and b) computation, transmission, flying, and
hovering energy consumption of UAVs. The energy consumption for computation is
typically measured by the required number of CPU cycles for computational
tasks. The transmission energy consumption considers both offloading and
receiving the related energy consumption. The transmission energy consumption
depends on the used communication protocols (i.e., OMA or NOMA). Therefore,
several studies proposed energy consumption minimization approaches for OMA
and NOMA separately (Ji et al., 2020; Jeong et al., 2017; Li et al., 2020a).
Many studies often ignore the energy consumption caused by hovering or
acceleration or assume that the UAVs have fixed speed and height (Liu et al.,
2020c; Yu et al., 2020; Ti and Le, 2018; Wan et al., 2019). However, the
speed, acceleration, hovering, and height of UAVs considerably impact energy
consumption. The energy consumption for hovering or acceleration is considered
in several studies (Zhao et al., 2021; Jeong et al., 2017; Ji et al., 2020).
Energy consumption is often optimized jointly with other QoS such as latency,
throughput, and resource utilization (Ning et al., 2021; Yu et al., 2020; Xu
et al., 2020a).

####  4.3.2. Throughput

Throughput is one of the most important performance indicators in UEC to
evaluate and compare various resource management approaches. Therefore, many
existing studies have intensively studied throughput in various UEC scenarios
(Wang et al., 2019a; Cheng et al., 2018; He et al., 2021a; Wu et al., 2020).
Most of the existing studies mainly focus on either computational throughput
or network throughput or both. Several studies focus on maximizing each IoT
device’s computational capacity (Dai et al., 2020; Chen et al., 2020). For
instance, an age of information-aware resource management approach is
introduced in (Chen et al., 2020), where the aim is to maximize each mobile
user’s long-term computation performance using a novel online deep
reinforcement learning scheme. Several studies focus on maximizing the
computational throughput of all IoT devices (Hu et al., 2020; Wu et al., 2020;
Cheng et al., 2018). For instance, a cooperative offloading and resource
management approach is proposed in (Liu et al., 2021c) where the aim is to
maximize the system-wide computation capacity. Several studies also focus on
improving the throughput of the UAV in a UEC system (Zhou et al., 2018a).
Throughput is also jointly considered with other performance indicators such
as resource utilization, cost, utility, and energy efficiency (Feng et al.,
2021).

####  4.3.3. Latency

Latency is another key performance indicator directly impacting the user
experience in UEC. For delay-sensitive applications, latency is a critical QoS
attribute. Many studies focus on improving the latency in UEC (Sun et al.,
2020; Mao et al., 2020; Wang et al., 2021a). Latency primarily depends on
three key factors which are local computation time, transmission time, and
remote computation time. Local computation time refers to the required
computation time by IoT devices or UAVs (as users). The transmission time
refers to the data sending time and the result receiving time. The remote
computation time refers to the required time for processing offloaded data.
Improving the latency in UEC systems is considered a complex optimization
problem. Most of the existing studies focus on specific scenarios to develop
various approaches to improve latency along with other QoS (Yu et al., 2020;
Mao et al., 2020).

####  4.3.4. Cost

Several studies aim at minimizing the cost of resource management in UEC
systems (Zhang et al., 2020a; Guo et al., 2021; Seid et al., 2021). The cost
in UEC is typically defined in terms of multiple cost factors in resource
management such as offloading or transmission cost, energy cost, and
processing cost. Hence, the cost is considered as a compound performance
indicator in UEC. Most of the studies typically develop different types of
cost models targeting specific application scenarios (Wang et al., 2017;
Asheralieva and Niyato, 2019; Seid et al., 2021). For instance, A cost model
for mobile devices is proposed in (Zhang et al., 2020a) where cost is
considered as a linear combination of the energy consumption of the mobile
devices and the corresponding time latency. The proposed cost model is applied
to a scenario where each mobile device can execute its tasks locally or
offload tasks to UAVs or Base stations. The total system cost is minimized in
(Guo et al., 2021) where the total system cost is defined as the sum of the
UAVs costs in whole task completion.

####  4.3.5. Utility

The utility in a UEC system typically indicates the user satisfaction achieved
under a particular resource management strategy. The existing work usually
measures the utility using pre-defined utility functions. A utility function
is represented by different QoS attributes such as latency, throughput, energy
consumption, data rate and cost. Utility functions often require mathematical
transformation as each QoS attribute may have a unique unit of measurement.
The transformation is typically performed using reciprocal, logarithm, and
weighted summation methods. The utility function is then maximized using
various resource management algorithms to achieve expected user satisfaction
(Apostolopoulos et al., 2021; Liwang et al., 2021; Khochare et al., 2021; Liu
et al., 2020b).

####  4.3.6. Others

Some of the existing studies consider resource utilization, profit, and UAV
coverage as performance indicators of their proposed resource management
strategies. For instance, a resource pricing and trading scheme are proposed
for the edge server’s profit and UAV’s revenue. A novel path planning and
resource management strategy is proposed for big data processing in UEC (Wan
et al., 2019) where the aim is to enhance service area coverage. Resource
utilization is another key concern in UEC due to the limited available
resources. Therefore, several studies focus on maximizing network,
computation, and storage resource utilization. Resource utilization is
typically measured by the ratio of the utilized resources and the total
available resources. Resource utilization is typically studied with other QoS
attributes such as energy consumption, latency, and throughput.

##  5\. Research Challenges and Directions

The research on resource management in UEC has accumulated a number of
results. However, many challenges and research gaps in this area still have
not been well studied. In this section, several existing challenges and future
research directions are discussed.

###  5.1. Architecture

The three-layer architecture of a UEC system typically consists of a things
layer, an edge layer, and a cloud layer. The architecture shown in Fig. 2
captures the deployment aspect of physical resources in UEC. However, most of
the existing studies only consider a partial architecture when performing
efficient resource management in UEC, which ignores one or several of the
collaborations among Things, UAV, edge servers and cloud, as shown in Table 3.
Such a partial architecture weakens the feasibility and functionality of UEC
systems, and results in resource waste. This violates the original pursuit of
resource management in UEC. In this case, the researchers should consider the
architecture more comprehensively to manage the resources of UEC systems in a
more effective way.

Moreover, the deployment of software components in different layers is not
investigated in the existing literature. It may be because of the fact that
most of the existing studies are focusing on theatrical aspects of resource
management in UEC. In the real-world scenario, resource deployment may face
many technical challenges that could be overcome by innovative architectural
patterns for the deployment of software components (Tran and Babar, 2020). In
such cases, future research will need to investigate various aspects of
resource management on virtual levels such as virtual machine migration and
consolidation, serverless computing, and auto-scaling. Another important
aspect is the integration of space-based networks in the architecture. In the
near future, space, air and ground network (SAGIN) will be widely deployed to
support billions of IoT devices. In this case, heterogeneous networks and
resources need to be considered for efficient resource management.

###  5.2. UAV trajectory design

As a significant research problem in UEC, UAV trajectory design is key to the
success of resource management studies in UEC. Among the existing studies, a
small number of researchers consider the UAV trajectory design when they
tackle resource allocation and computation offloading problems in UEC, such as
(Li et al., 2020a; Miao et al., 2022). The mobility of UAVs is one of the
important characteristics of UEC, while the trajectory of UAVs can result in
various changes in user coverage, wireless interference and energy harvesting.
Without proper UAV trajectory designs, the feasibility and usability of
resource management approaches in UEC cannot be guaranteed. For the existing
UAV trajectory studies, the proposed approaches are mainly designed for a
single UAV and ignore the conflict of multiple UAV flight paths. In most real-
world UEC scenarios, a set of UAVs are involved to provide high-performance
and low-latency services for users, e.g., mobile and IoT devices. From the
view of feasibility and usability, the multiple-UAV trajectory design with
environmental changes should be considered for investigating resource
management problems in UEC. Otherwise, the gap from research to industry
cannot be filled.

###  5.3. Energy charging and harvesting

Considering the fact that UAVs and IoT devices have constrained battery life,
saving energy consumption and maintaining the long life of installed batteries
are important when providing high-performance and low-latency services in UEC.
Moreover, from a service provider’s perspective, a long-term service goal is
normally more significant than a short-term service goal (Xia et al., 2021b).
To provide long-term and high-quality services, an effective method for
tackling such a challenge is to implement wireless charging or energy
harvesting techniques in UEC. The existing studies with consideration of
energy harvesting mainly implement wireless charging and energy harvesting
either from edge servers to UAVs or from UAVs from IoT devices (Zhou et al.,
2018a; Feng et al., 2021; Wang et al., 2017; Xu et al., 2018). In addition,
those studies assume that such energy transfer is stable and continuous, which
is not always true in real-world UEC scenarios. Due to many external factors,
such as wind, rain and interference, the energy transfer process could be
easily impacted, even interrupted. In this case, the design of resource
management strategies in UEC becomes very challenging. Thus, the energy
transfer process needs more attention from researchers when they investigate
resource management problems in UEC with consideration of wireless charging
and energy harvesting. Besides the energy transfer, another way for energy
harvesting is utilizing green energy, such as light and wind, to support UAVs
and IoT devices. With the assistance of green energy, the provided services
can be more stable which would help reduce environmental pollution.

###  5.4. Security and Privacy

Security and privacy are two major concerns in any system. Managing security
and privacy is more challenging in UEC due to the distributed architecture,
limited resources, and mobile and volatile environment (Ouyang et al., 2021;
Zhou et al., 2019; Patrikar and Parate, 2022). However, the existing
literature has paid little attention to the security and privacy aspects of
resource management in UEC. A failure of UAVs caused by attacks or system
failures may affect the reliability and robustness of an entire UEC system
(Gao et al., 2020). Therefore, the resource management research in UEC needs
to investigate different types of attacks and fault tolerance strategies for
UAVs. The UEC systems heavily rely on communication channels and protocols
between various components. Information could be compromised during the data
transmission (Yao and Ansari, 2021). The enabling resource-intensive security
protocols may impact the performance of UEC systems. Therefore, various
lightweight security protocols need to be investigated for resource management
in UEC. Most of the existing studies for resource management unconditionally
trust the user devices. However, the trust of user devices needs to be
considered while accepting offloading requests in UEC. If a user’s device is
compromised or acts as a malicious node, UAVs require an efficient and fast
way to identify it. Moreover, privacy-sensitive data may need to be processed
in UEC depending on various application scenarios such as smart health or
military operation. In such cases, privacy-sensitive data processing
strategies need to be considered. Integration of privacy-enabled algorithms
may impact the performance of the UEC systems. Therefore, effective trade-off
mechanisms between performance and privacy are required for resource
management.

###  5.5. External Factors

The environmental factors such as wind, fog, rain, and storm may significantly
affect the performance of UAVs (Alkouz et al., 2020). However, most of the
existing studies for resource management in UEC ignore the environmental
factors for resource management. The trajectory, speed, and acceleration of a
UAV are required to be controlled effectively in various weather conditions to
optimize their energy consumption. Another key concern in UEC resource
management is the interference in multi-UAV systems. When multiple UAVs
communicate in a shared channel with user devices, interference may arise (Lu
et al., 2020). Therefore, effective communication models and trajectories need
to be designed efficiently to avoid interference. Most of the existing studies
assumed that all UAVs are operated by the same provider. However, in the near
future, the same location may get coverage by multiple UAVs operated by
different providers. In such a case, the trajectories of UAVs need to be
designed carefully to avoid collision. Recharging on UAVs is another key
concern that is overlooked in most of the existing studies, which also assumes
that a UAV has a fixed amount of energy that needs to be optimized to deliver
computing services. However, A UAV may have an opportunity to recharge its
battery from charging stations or via wireless energy transfers (Alkouz and
Bouguettaya, 2021). Therefore, the computational performance of the UEC
systems can be improved by considering available charging options for UAVs
while designing resource allocation approaches in UEC. Another key challenge
of UEC is to comply with government regulations. Most of the studies in UEC do
not take into account government regulations. In some countries, governments
may impose specific restrictions on the maximum and minimum height of UAVs.
The height of UAVs affects the communication between IoT devices and UAVs in
the UEC systems. Therefore, future research needs to consider government rules
and regulations during UAV deployment and trajectory design in UEC.

###  5.6. Real-world experiments

Currently, the evaluations of resource management strategies are generally
based on simulation technologies rather than any real-world implementations.
Most of the existing studies evaluate their approaches in simulated scenarios
by Java, Python, Matlab and other programming languages with real or synthetic
data sets, such as (Xu et al., 2022c; Lee et al., 2022; Tan et al., 2022; Zhu
et al., 2022b; Mahmood et al., 2022). Assumptions on weather, power, speed,
etc. are needed to enable the system simulation. However, the connection to
real-world systems and the feasibility of the proposed strategies cannot be
convincingly demonstrated in this case. Thus, without thorough test systems in
real-world scenarios, the evaluations are not comprehensive and less
confident. From this point, more efforts are required to evaluate the
feasibility of proposed approaches in real-world systems. In addition, real-
world or highly simulated test beds are in high demand.

##  6\. Conclusion

Efficient Resource management is a key requirement for deploying UAV-enabled
Edge Computing. We present a comprehensive survey on the state-of-art of
resource management in UEC. The resource management in UEC is considered from
resource provisioning, computational task offloading, resource allocations,
and joint resource management perspectives. We discuss the key techniques and
performance indicators for efficient resource management in UEC. A conceptual
architecture is presented that illustrates the collaborations and
communications between UAVs, IoT devices, edge servers, and cloud servers.
Existing study shows that UAV-enabled edge computing is a promising technology
that may facilitate a wide range of innovative applications in various domain
such as urban traffic control, smart agriculture, disaster response, tactical
edge, and logistics. Although most of the existing studies focus on building a
theoretical foundation for efficient resource management in various scenarios,
the wider adoption of UEC technology is still hindered by several key
challenges. For instance, architectural design decisions in the environment
may considerably impact the quality attributes of UEC-based applications.
However, it remained largely unexplored in the state-of-the-art. External
factors such as environment, interference, recharging availability, and
government regulations must be considered before deploying UEC technologies.
The operating environment of UEC is intrinsically volatile, resource and
energy-constrained. Therefore, maintaining security and privacy in UEC systems
is critical and requires more intensive research. A major challenge in
conducting research in UEC is the lack of real-world testbeds for conducting
experiments. Building real-world testbeds or UEC simulation platforms that re-
assemble the real world will attract more research interests in this domain.
Another future research direction can be found by considering the interests of
different stakeholders in UEC. Most of the existing studies assume that one
stakeholder is responsible for managing all resources in IoT devices, UAVs,
edge servers, and cloud servers. In the real world, these resources may be
provisioned by different stakeholders who will provision their resources based
on their economic models. In such scenarios, the resource management
approaches would require careful consideration of the economic models of
different stakeholders. For example, some stakeholders may deploy charging
stations in remote areas where UAVs may recharge themselves and continue to
provide computing services. Another group of stakeholders may provision UAV-
as-a-Service where UAVs can be rented for a period of time. In such scenarios,
managing resources efficiently could be more challenging, given the
constraints imposed by different stakeholders.

## References

  * (1)
  * Abbas et al. (2017) Nasir Abbas, Yan Zhang, Amir Taherkordi, and Tor Skeie. 2017.  Mobile edge computing: A survey.  _IEEE Internet of Things Journal_ 5, 1 (2017), 450–465.
  * Abdellatif et al. (2021) Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Aiman Erbad, Carla Fabiana Chiasserini, Mohsen Guizani, Mark Dennis O’Connor, and James Laughton. 2021.  Medge-chain: Leveraging edge computing and blockchain for efficient medical data exchange.  _IEEE Internet of Things Journal_ 8, 21 (2021), 15762–15775.
  * Abrar et al. (2021) Muhammad Abrar, Ushna Ajmal, Ziyad M Almohaimeed, Xiang Gui, Rizwan Akram, and Roha Masroor. 2021.  Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review.  _IEEE Access_ (2021).
  * Abusafia and Bouguettaya (2020) Amani Abusafia and Athman Bouguettaya. 2020.  Reliability model for incentive-driven iot energy services. In _MobiQuitous 2020-17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services_. 196–205.
  * Alkouz and Bouguettaya (2021) Balsam Alkouz and Athman Bouguettaya. 2021.  A reinforcement learning approach for re-allocating drone swarm services. In _International Conference on Service-Oriented Computing_. Springer, 643–651.
  * Alkouz et al. (2020) Balsam Alkouz, Athman Bouguettaya, and Sajib Mistry. 2020.  Swarm-based drone-as-a-service (sdaas) for delivery. In _2020 IEEE International Conference on Web Services (ICWS)_. IEEE, 441–448.
  * Apostolopoulos et al. (2021) Pavlos Athanasios Apostolopoulos, Georgios Fragkos, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. 2021.  Data offloading in UAV-assisted multi-access edge computing systems under resource uncertainty.  _IEEE Transactions on Mobile Computing_ (2021).
  * Armbrust et al. (2010) Michael Armbrust, Armando Fox, Rean Griffith, Anthony D Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, et al. 2010\.  A view of cloud computing.  _Commun. ACM_ 53, 4 (2010), 50–58.
  * Asheralieva and Niyato (2019) Alia Asheralieva and Dusit Niyato. 2019.  Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing.  _IEEE Internet of Things Journal_ 7, 3 (2019), 1974–1993.
  * Bai et al. (2019) Tong Bai, Jingjing Wang, Yong Ren, and Lajos Hanzo. 2019.  Energy-efficient computation offloading for secure UAV-edge-computing systems.  _IEEE Transactions on Vehicular Technology_ 68, 6 (2019), 6074–6087.
  * Chen et al. (2018) Min Chen, Yixue Hao, Kai Lin, Zhiyong Yuan, and Long Hu. 2018.  Label-less learning for traffic control in an edge network.  _IEEE Network_ 32, 6 (2018), 8–14.
  * Chen et al. (2016) Xu Chen, Lei Jiao, Wenzhong Li, and Xiaoming Fu. 2016.  Efficient multi-user computation offloading for mobile-edge cloud computing.  _IEEE/ACM Transactions on Networking_ 24, 5 (2016), 2795–2808.
  * Chen et al. (2020) Xianfu Chen, Celimuge Wu, Tao Chen, Zhi Liu, Mehdi Bennis, and Yusheng Ji. 2020.  Age of information-aware resource management in UAV-assisted mobile-edge computing systems. In _GLOBECOM 2020-2020 IEEE Global Communications Conference_. IEEE, 1–6.
  * Chen and Zheng (2020) Yuqing Chen and Zhaohui Zheng. 2020.  Joint deployment and task computation of uavs in uav-assisted edge computing network. In _2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS)_. IEEE, 413–416.
  * Chen et al. (2022) Zheyi Chen, Hongqiang Zheng, Jianshan Zhang, Xianghan Zheng, and Chunming Rong. 2022.  Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems.  _Peer-to-Peer Networking and Applications_ 15, 1 (2022), 194–205.
  * Cheng et al. (2018) Fen Cheng, Shun Zhang, Zan Li, Yunfei Chen, Nan Zhao, F Richard Yu, and Victor CM Leung. 2018.  UAV trajectory optimization for data offloading at the edge of multiple cells.  _IEEE Transactions on Vehicular Technology_ 67, 7 (2018), 6732–6736.
  * Cheng et al. (2021a) Zhipeng Cheng, Zhibin Gao, Minghui Liwang, Lianfen Huang, Xiaojiang Du, and Mohsen Guizani. 2021a.  Intelligent Task Offloading and Energy Allocation in the UAV-Aided Mobile Edge-Cloud Continuum.  _Ieee Network_ 35, 5 (2021), 42–49.
  * Cheng et al. (2022a) Zhipeng Cheng, Minghui Liwang, Ning Chen, Lianfen Huang, Xiaojiang Du, and Mohsen Guizani. 2022a.  Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks.  _Computer Communications_ 192 (2022), 234–244.
  * Cheng et al. (2022b) Zhipeng Cheng, Minghui Liwang, Ning Chen, Lianfen Huang, Nadra Guizani, and Xiaojiang Du. 2022b.  Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks.  _Digital Communications and Networks_ (2022).
  * Cheng et al. (2022c) Zhipeng Cheng, Minghui Liwang, Xiaoyu Xia, Minghui Min, Xianbin Wang, and Xiaojiang Du. 2022c.  Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks.  _IEEE Transactions on Vehicular Technology_ (2022).
  * Cheng et al. (2021b) Zhipeng Cheng, Minghui Min, Minghui Liwang, Zhibin Gao, and Lianfen Huang. 2021b.  Joint Client Selection and Task Assignment for Multi-Task Federated Learning in MEC Networks. In _2021 IEEE Global Communications Conference (GLOBECOM)_. IEEE, 1–6.
  * Chiu et al. (2022) Te-Chuan Chiu, Chih-Yu Wang, Ai-Chun Pang, and Wei-Ho Chung. 2022.  Collaborative Energy Beamforming for Wireless Powered Fog Computing Networks.  _IEEE Transactions on Wireless Communications_ (2022).
  * Cui et al. (2021) Guangming Cui, Qiang He, Xiaoyu Xia, Feifei Chen, Tao Gu, Hai Jin, and Yun Yang. 2021.  Demand Response in NOMA-based Mobile Edge Computing: A Two-phase Game-theoretical Approach.  _IEEE Transactions on Mobile Computing_ (2021), 1–1.  https://doi.org/10.1109/TMC.2021.3108581
  * Cui et al. (2020) Guangming Cui, Qiang He, Xiaoyu Xia, Phu Lai, Feifei Chen, Tao Gu, and Yun Yang. 2020.  Interference-aware SaaS User Allocation Game for Edge Computing.  _IEEE Transactions on Cloud Computing_ (2020).  https://doi.org/10.1109/TCC.2020.3008440
  * Dai et al. (2021) Bin Dai, Jianwei Niu, Tao Ren, Zheyuan Hu, and Mohammed Atiquzzaman. 2021.  Towards Energy-Efficient Scheduling of UAV and Base Station Hybrid Enabled Mobile Edge Computing.  _IEEE Transactions on Vehicular Technology_ 71, 1 (2021), 915–930.
  * Dai et al. (2020) Minghui Dai, Zhou Su, Jiliang Li, and Jian Zhou. 2020.  An Energy-Efficient Edge Offloading Scheme for UAV-Assisted Internet of Things. In _2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)_. IEEE, 1293–1297.
  * Deng et al. (2022) Cailian Deng, Xuming Fang, and Xianbin Wang. 2022.  UAV-enabled Mobile Edge Computing for AI Applications: Joint Model Decision, Resource Allocation and Trajectory Optimization.  _IEEE Internet of Things Journal_ (2022).
  * Deng et al. (2021) Xiaoheng Deng, Jun Li, Peiyuan Guan, and Lan Zhang. 2021.  Energy-efficient UAV-aided target tracking systems based on edge computing.  _IEEE Internet of Things Journal_ 9, 3 (2021), 2207–2214.
  * Deng et al. (2020) Xiumei Deng, Jun Li, Long Shi, Zhiqiang Wei, Xiaobo Zhou, and Jinhong Yuan. 2020.  Wireless powered mobile edge computing: Dynamic resource allocation and throughput maximization.  _IEEE Transactions on Mobile Computing_ (2020).
  * Ding et al. (2017) Zhiguo Ding, Xianfu Lei, George K Karagiannidis, Robert Schober, Jinhong Yuan, and Vijay K Bhargava. 2017.  A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends.  _IEEE Journal on Selected Areas in Communications_ 35, 10 (2017), 2181–2195.
  * Feng et al. (2021) Wanmei Feng, Jie Tang, Nan Zhao, Xiuyin Zhang, Xianbin Wang, Kai-Kit Wong, and Jonathon A Chambers. 2021.  Hybrid beamforming design and resource allocation for UAV-aided wireless-powered mobile edge computing networks with NOMA.  _IEEE Journal on Selected Areas in Communications_ 39, 11 (2021), 3271–3286.
  * Feng et al. (2020) Wanmei Feng, Nan Zhao, Shaopeng Ao, Jie Tang, XiuYin Zhang, Yuli Fu, Daniel KC So, and Kai-Kit Wong. 2020.  Joint 3D trajectory design and time allocation for UAV-enabled wireless power transfer networks.  _IEEE Transactions on Vehicular Technology_ 69, 9 (2020), 9265–9278.
  * Fu et al. (2019) Shu Fu, Fang Fang, Lian Zhao, Zhiguo Ding, and Xin Jian. 2019.  Joint transmission scheduling and power allocation in non-orthogonal multiple access.  _IEEE Transactions on Communications_ 67, 11 (2019), 8137–8150.
  * Gao et al. (2020) Ying Gao, Hongying Tang, Baoqing Li, and Xiaobing Yuan. 2020.  Securing energy-constrained UAV communications against both internal and external eavesdropping.  _IEEE Communications Letters_ 25, 3 (2020), 749–753.
  * Gu et al. (2021) Xiaohui Gu, Guoan Zhang, Mingxing Wang, Wei Duan, Miaowen Wen, and Pin-Han Ho. 2021.  UAV-aided Energy Efficient Edge Computing Networks: Security Offloading Optimization.  _IEEE Internet of Things Journal_ (2021).
  * Guo et al. (2021) Yunkai Guo, Shushi Gu, Qinyu Zhang, Ning Zhang, and Wei Xiang. 2021.  A Coded Distributed Computing Framework for Task Offloading from Multi-UAV to Edge Servers. In _2021 IEEE Wireless Communications and Networking Conference (WCNC)_. IEEE, 1–6.
  * Gupta and Gupta (2022) Akshita Gupta and Sachin Kumar Gupta. 2022.  Secured Unmanned Aerial Vehicle-based Fog Computing Network: A Review.  _Holistic Approach to Quantum Cryptography in Cyber Security_ (2022), 67–82.
  * Han et al. (2022) Zhaoyang Han, Yaoqi Yang, Weizheng Wang, Lu Zhou, Tu N Nguyen, and Chunhua Su. 2022.  Age Efficient Optimization in UAV-Aided VEC Network: A Game Theory Viewpoint.  _IEEE Transactions on Intelligent Transportation Systems_ (2022).
  * He et al. (2019) Qiang He, Guangming Cui, Xuyun Zhang, Feifei Chen, Shuiguang Deng, Hai Jin, Yanhui Li, and Yun Yang. 2019.  A game-theoretical approach for user allocation in edge computing environment.  _IEEE Transactions on Parallel and Distributed Systems_ 31, 3 (2019), 515–529.
  * He et al. (2022) Qiang He, Zeqian Dong, Feifei Chen, Shuiguang Deng, Weifa Liang, and Yun Yang. 2022.  Pyramid: enabling hierarchical neural networks with edge computing. In _Proceedings of the ACM Web Conference 2022_. 1860–1870.
  * He et al. (2021b) Qiang He, Cheng Wang, Guangming Cui, Bo Li, Rui Zhou, Qingguo Zhou, Yang Xiang, Hai Jin, and Yun Yang. 2021b.  A Game-Theoretical Approach for Mitigating Edge DDoS Attack.  _IEEE Transactions on Dependable and Secure Computing_ (2021).  https://doi.org/10.1109/TDSC.2021.3055559
  * He et al. (2021a) Xiaofan He, Richeng Jin, and Huaiyu Dai. 2021a.  Multi-Hop Task Offloading with On-The-Fly Computation for Multi-UAV Remote Edge Computing.  _IEEE Transactions on Communications_ (2021).
  * Hou et al. (2019) Xiangwang Hou, Zhiyuan Ren, Wenchi Cheng, Chen Chen, and Hailin Zhang. 2019.  Fog based computation offloading for swarm of drones. In _ICC 2019-2019 IEEE International Conference on Communications (ICC)_. IEEE, 1–7.
  * Hu et al. (2018) Qiyu Hu, Yunlong Cai, Guanding Yu, Zhijin Qin, Minjian Zhao, and Geoffrey Ye Li. 2018.  Joint offloading and trajectory design for UAV-enabled mobile edge computing systems.  _IEEE Internet of Things Journal_ 6, 2 (2018), 1879–1892.
  * Hu et al. (2019) Xiaoyan Hu, Kai-Kit Wong, Kun Yang, and Zhongbin Zheng. 2019.  Task and bandwidth allocation for UAV-assisted mobile edge computing with trajectory design. In _2019 IEEE Global Communications Conference (GLOBECOM)_. IEEE, 1–6.
  * Hu et al. (2020) Xiaoyan Hu, Kai-Kit Wong, and Yangyang Zhang. 2020.  Wireless-powered edge computing with cooperative UAV: Task, time scheduling and trajectory design.  _IEEE Transactions on Wireless Communications_ 19, 12 (2020), 8083–8098.
  * Huda and Moh (2022) SM Asiful Huda and Sangman Moh. 2022.  Survey on computation offloading in UAV-Enabled mobile edge computing.  _Journal of Network and Computer Applications_ (2022), 103341.
  * Huynh and Huh (2022) Luan NT Huynh and Eui-Nam Huh. 2022.  UAV-Enhanced Edge Intelligence: A Survey. In _2022 6th International Conference on Computing Methodologies and Communication (ICCMC)_. IEEE, 42–47.
  * Jeong et al. (2017) Seongah Jeong, Osvaldo Simeone, and Joonhyuk Kang. 2017.  Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning.  _IEEE Transactions on Vehicular Technology_ 67, 3 (2017), 2049–2063.
  * Ji et al. (2020) Jiequ Ji, Kun Zhu, Changyan Yi, and Dusit Niyato. 2020.  Energy consumption minimization in uav-assisted mobile-edge computing Systems: joint resource allocation and trajectory design.  _IEEE Internet of Things Journal_ 8, 10 (2020), 8570–8584.
  * Jin et al. (2022) Hai Jin, Ruikun Luo, Qiang He, Song Wu, Zilai Zeng, and Xiaoyu Xia. 2022.  Cost-effective data placement in edge storage systems with erasure code.  _IEEE Transactions on Services Computing_ (2022).
  * Keele et al. (2007) Staffs Keele et al. 2007\.  Guidelines for performing systematic literature reviews in software engineering.
  * Khochare et al. (2021) Aakash Khochare, Yogesh Simmhan, Francesco Betti Sorbelli, and Sajal K Das. 2021.  Heuristic algorithms for co-scheduling of edge analytics and routes for uav fleet missions. In _IEEE INFOCOM 2021-IEEE Conference on Computer Communications_. IEEE, 1–10.
  * Lai et al. (2019) Phu Lai, Qiang He, Guangming Cui, Xiaoyu Xia, Mohamed Abdelrazek, Feifei Chen, John Hosking, John Grundy, and Yun Yang. 2019.  Edge User Allocation with Dynamic Quality of Service. In _17th International Conference on Service-Oriented Computing_. Springer, 86–101.
  * Lai et al. (2022) Phu Lai, Qiang He, Xiaoyu Xia, Feifei Chen, Mohamed Abdelrazek, John Grundy, John Hosking, and Yun Yang. 2022.  Dynamic User Allocation in Stochastic Mobile Edge Computing Systems.  _IEEE Transactions on Services Computing_ 15, 5 (2022), 2699–2712.  https://doi.org/10.1109/TSC.2021.3063148
  * Latif et al. (2022) Zohaib Latif, Choonhwa Lee, Kashif Sharif, and Sumi Helal. 2022.  SDBlockEdge: SDN-Blockchain Enabled Multihop Task Offloading in Collaborative Edge Computing.  _IEEE Sensors Journal_ (2022).
  * Lee et al. (2022) Meng-Tse Lee, Ming-Lung Chuang, Sih-Tse Kuo, and Yan-Ru Chen. 2022.  UAV Swarm Real-Time Rerouting by Edge Computing D* Lite Algorithm.  _Applied Sciences_ 12, 3 (2022), 1056.
  * Lewis et al. (2014) Grace Lewis, Sebastián Echeverría, Soumya Simanta, Ben Bradshaw, and James Root. 2014.  Tactical cloudlets: Moving cloud computing to the edge. In _2014 IEEE military communications conference_. IEEE, 1440–1446.
  * Li et al. (2020b) Bo Li, Qiang He, Guangming Cui, Xiaoyu Xia, Feifei Chen, Hai Jin, and Yun Yang. 2020b.  READ: Robustness-oriented Edge Application Deployment in Edge Computing Environment.  _IEEE Transactions on Services Computing_ (2020).  https://doi.org/10.1109/TSC.2020.3015316
  * Li et al. (2022b) Bo Li, Qiang He, Liang Yuan, Feifei Chen, Lingjuan Lyu, and Yun Yang. 2022b.  EdgeWatch: Collaborative investigation of data integrity at the edge based on blockchain. In _Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining_. 3208–3218.
  * Li et al. (2022c) Jing Li, Weifa Liang, Wenzheng Xu, Zichuan Xu, Xiaohua Jia, Albert Y Zomaya, and Song Guo. 2022c.  Budget-Aware User Satisfaction Maximization on Service Provisioning in Mobile Edge Computing.  _IEEE Transactions on Mobile Computing_ (2022).
  * Li et al. (2020a) Mushu Li, Nan Cheng, Jie Gao, Yinlu Wang, Lian Zhao, and Xuemin Shen. 2020a.  Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization.  _IEEE Transactions on Vehicular Technology_ 69, 3 (2020), 3424–3438.
  * Li et al. (2020c) Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020c.  Federated learning: Challenges, methods, and future directions.  _IEEE Signal Processing Magazine_ 37, 3 (2020), 50–60.
  * Li et al. (2022a) Xuejun Li, Lina Gong, Xiao Liu, Frank Jiang, Wenyu Shi, Lingmin Fan, Han Gao, Rui Li, and Jia Xu. 2022a.  Solving the last mile problem in logistics: A mobile edge computing and blockchain-based unmanned aerial vehicle delivery system.  _Concurrency and Computation: Practice and Experience_ 34, 7 (2022), e6068.
  * Lin et al. (2020) Hai Lin, Sherali Zeadally, Zhihong Chen, Houda Labiod, and Lusheng Wang. 2020.  A survey on computation offloading modeling for edge computing.  _Journal of Network and Computer Applications_ 169 (2020), 102781.
  * Lin et al. (2022) Jie Lin, Lin Huang, Hanlin Zhang, Xinyu Yang, and Peng Zhao. 2022.  A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing.  _Computer Networks_ 205 (2022), 108710.
  * Liu et al. (2020d) Baichuan Liu, Weikun Zhang, Wuhui Chen, Huawei Huang, and Song Guo. 2020d.  Online computation offloading and traffic routing for UAV swarms in edge-cloud computing.  _IEEE Transactions on Vehicular Technology_ 69, 8 (2020), 8777–8791.
  * Liu et al. (2019a) Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, and Tongqing Zhou. 2019a.  A survey on edge computing systems and tools.  _Proc. IEEE_ 107, 8 (2019), 1537–1562.
  * Liu et al. (2021b) Hong Liu, Shuaipeng Zhang, Pengfei Zhang, Xinqiang Zhou, Xuebin Shao, Geguang Pu, and Yan Zhang. 2021b.  Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing.  _IEEE Transactions on Vehicular Technology_ 70, 6 (2021), 6073–6084.
  * Liu et al. (2020c) Wanning Liu, Yitao Xu, Nan Qi, Kailing Yao, Yuli Zhang, and Wenhui He. 2020c.  Joint computation offloading and resource allocation in UAV swarms with multi-access edge computing. In _2020 International Conference on Wireless Communications and Signal Processing (WCSP)_. IEEE, 280–285.