To sum up, this paper successfully demonstrates the benefits of using a single UAV system with IoT devices for crowd surveillance with the introduced video data processing offloading technique. However, further analysis of the coordination of multiple UAVs is missing.

In [202], the authors present the use of small UAVs for specific surveillance scenarios, such as assessment after a disaster. This type of surveillance applications utilizes a system that integrates a wide range of technologies involving: communication, control, sensing, image processing and networking.
This article is a product technical report and the corresponding  manufacturer successfully applied multiple practical techniques in a real UAV surveillance product.

Table <ref> summarizes the eight papers we have reviewed including their research focus and category.

§.§ Discussion

§.§.§ State-of-the-art Research

Table <ref> summarizes the advantages, disadvantages and important concerns for the UAV surveillance applications based on our literature review.  We classify the UAV surveillance research works into six categories based on their focus and content:

* Multi-UAV cooperation: this line of research focuses on developing new orchestration algorithms or architectures in the presence of multiple UAVs.

* Homeland/military security concerns: this line of research provides analysis and suggestions for the authorities with the considerations for Homeland/military security.

* Algorithm improvement: this line of research develops new algorithms for more efficient post-processing of the data (e.g., videos or photos captured by the UAVs).

* New use case: this line of research integrates UAV system into new application scenarios for potential extra benefits.

* Product introduction: this line of research describes mature techniques leveraged by a real UAV surveillance product.

Summary of Advantages, Disadvantages and Important Concerns for Surveillance applications of UAVs

Advantages             c]@l@ surveillance coverage and range improvement
better safety for human operators
robustness and efficiency in surveillance
Disadvantages          c]@l@ high accidental rate
high operating costs
difficulties in surveillance of risk societies.
Concerns     c]@l@ co-operation of multi-UAVs
post-processing algorithm improvements
privacy concerns
law enforcement

§.§ Research Trends and Future Insights

§.§.§ Research Trends

Based on the reviewed literature and our analysis, we identify that the use of research trends (e.g., machine learning algorithms, nano-sensors [204], short-range communications technologies, etc.) could bring further benefits in the area of UAV surveillance applications:

* There are several preferred features of UAV surveillance applications, especially for some military or homeland security use cases: (1) small size: this feature not only reduces the physical attack surface of the UAV but also achieves more secrete UAV patrolling. (2) high resolution photos: this feature helps the data post processing algorithms to draw more accurate conclusions or obtain more insight findings; (3) low energy consumption: this feature allows the UAV to operate for a longer time of period such that to achieve more complex tasks.

Therefore, to capture more accurate data in a more efficient and secret way, advanced sensor technologies should be considered. Such as nano-sensors (small size), ultra-high-resolution image sensors (accurate data) [205], and energy efficient sensors (low energy consumption) [206].

* To develop more efficient and accurate multi-UAV cooperation algorithms or data post-processing algorithms, advanced machine learning algorithms (e.g., deep learning [207]) could be utilized to achieve better performance and faster response.
Specifically, multi-UAV cooperation brings more benefits than single UAV surveillance, such as more wider surveillance scope, higher error tolerance, and faster task completion time. However, multi-UAV surveillance requires more advanced data collection, sharing and processing algorithms. Processing of huge amount data is time consuming and much more complex. Applying advanced machine learning algorithms (e.g., deep learning algorithm) could help the UAV system to draw better conclusions in a short period of time. For example, due to its improved data processing models, deep learning algorithms could help to obtain new findings from existing data and to get more concise and reliable analysis results.

§.§.§ Future Insights
based on our literature review and analysis, to develop or deploy effective UAV surveillance applications, we suggest the following:

* Select/design appropriate UAV system based on the state/federal laws and individual budget. For example, the design of UAVs surveillance systems should take the privacy and security requirements enforced by the local laws;

* Develop efficient customized algorithms (e.g., co-operation algorithm of multi-UAVs, photo mapping algorithms, etc.) based on system requirements. Multi-UAVs co-operation algorithm could achieve more efficient surveillance (e.g., optimum patrolling routes with minimum power consumption). Also, advanced data post-processing algorithms help the users to draw more accurate conclusions;

* Conduct various field experiments to verify newly proposed systems. Extensive field experiments in various surveillance conditions (e.g., day time, night time, sunshine day, cloudy day, etc.) should be conducted to verify the effectiveness and robustness of the UAVs systems.

§ PROVIDING WIRELESS COVERAGE

UAVs can be used to provide wireless coverage during
emergency cases where each UAV serves as an aerial wireless
base station when the cellular network goes down [208]. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for users [209]. In this section, we present the aerial wireless base stations use cases, UAV links and UAV channel characteristics. We also show the path loss models for UAVs, classify them based on environment, altitude and telecommunication link, and present some challenges facing these path loss models. Moreover, we present UAV deployment strategies that optimize several objective functions of interest. Then, we discuss the challenges facing UAV interference mitigation techniques. Finally, we present the research trends and future insights for aerial wireless base stations.

Typical Use Cases of UAV-Aided Wireless Communications.

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[  UAV links
[Control links
[Command and
control from
GCS to UAVs
[UAV status
report from
UAVs to GCS
[Sense and avoid

among UAVs
[Data links
[Direct ground
[UAV-base station
and UAV-gateway
wireless backhaul
wireless backhaul

Applications of UAV Links.

A comparison between data and control links of UAVs

Type of link   Security requirement   Latency requirement   Capacity requirement   Frequency bands

Control link   High   High   Low   L-band (960-977MHz)

C-band (5030-5091MHz)

Data link   Low   Low   High   450 MHz to mmWave

Basic Networking Architecture of UAV-Aided Wireless Communications.

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[  Classification of Path loss Models for UAVs
[Altitude of UAV
[Outdoor-ground users
[Indoor users
[Telecommunication link

Classification of Path Loss Models for UAVs.

§.§ Aerial Wireless Base Stations Use Cases

The authors in [8, 210, 211] present the typical use cases of aerial wireless base stations which are discussed in the following:

* UAVs for ubiquitous coverage: UAVs are utilized to assist wireless network in providing seamless wireless coverage within the serving area. Two example scenarios are rapid service recovery after disaster situations, and when the cellular network service is not available or it is unable to serve all ground users as shown in Figure <ref>.a.

* UAVs as network gateways: In remote geographic or disaster stricken areas, UAVs can be used as gateway nodes to provide connectivity to backbone networks, communication infrastructure, or the Internet.

* UAVs as relay nodes: UAVs can be utilized as relay nodes to provide wireless connectivity between two or more distant wireless devices without reliable direct communication links as shown in Figure <ref>.b.

* UAVs for data collection: UAVs are utilized to gather delay-tolerant information from a large number of distributed wireless devices. An example is wireless sensors in precision agriculture applications as shown in Figure <ref>.c.

* UAVs for worldwide coverage: UAV-satellite communication is an essential component for building the integrated space-air-ground network to provide high data rates anywhere, anytime, and towards the seamless wide area coverage.

§.§ UAV Links and Channel Characteristics

§.§.§ Control Links

The control links are fundamental to guarantee the safe operation of all UAVs.  These links must be low-latency, highly reliable, and secure two-way communications, usually with low data rate requirement for safety-critical information exchange among UAVs, and additionally between the UAV and ground control stations. The control links information flow can be classified into three types: 1) command and control from ground control stations to UAVs; 2) UAV status report from UAVs to ground; 3) sense-and-avoid information among UAVs. Also for autonomous UAV, which can fulfill missions depending on intelligent devices without real-time human control, the control links are important in case of emergency when human intervention is needed [8]. There are two frequency bands allocated for the control links, namely the L-band (960-977MHz) and the C-band (5030-5091MHz) [212]. For delay reasons, we always prefer the primary control links between ground control stations to UAVs, but the secondary control links via satellite could also be utilized as a backup to enhance reliability and robustness. Another key necessity for the control links is the high security. Specifically, efficient security mechanisms should be utilized to avoid the so-called ghost control scenario, a possibly disastrous circumstance in which the UAVs are controlled by unapproved operators via spoofed control or navigation signals. Accordingly, practical authentication techniques, perhaps supplemented by the emerging physical layer security techniques, should be applied for control links. Compared to data links, the control links usually have lower tolerance in terms of security and latency requirements [8].

§.§.§ Data Links

The purpose of using data links is to support task-related communications for the ground terminals which include ground base stations, mobile terminals, gateway nodes, wireless sensors, etc. The data links of UAVs need to provide the following communication services: 1) Direct mobile-UAV communication; 2) UAV-base station and UAV-gateway wireless backhaul; 3) UAV-UAV wireless backhaul. The capacity requirement of these data links depends on the communication services, ranging from low capacity (kbps) in UAV-sensor links to high speed (Gbps) in UAV-gateway wireless backhaul. The UAV data links could utilize the existing band assigned for the particular communication services to enhance performance, e.g., using millimeter wave band [213] and free space optics [214] for high capacity UAV-UAV wireless backhaul [8]. In Figure <ref>, we show the applications of UAV links. In Table <ref>, we make a comparison between control and data links.

§.§.§ UAV Channel Characteristics

Both control and data channels in UAV communication networks consist of two primary types of channels, UAV-ground and UAV-UAV channels as shown in Figure <ref>. These channels have several unique characteristics compared with the characteristics of terrestrial communication channels. While line of sight links are expected for UAV-ground channels in most cases, they could also be occasionally blocked by obstacles such as buildings. For low-altitude UAVs, the UAV-ground channels may also suffer a number of multipath components due to reflection, scattering, and diffraction by buildings, ground surface, etc. The UAV-UAV wireless channels are line of sight dominated and thus the effect of multipath is minimal compared to that experienced in UAV-ground or ground-ground channels.  Due to the continuous movements of UAVs with different velocities, the UAV-to-UAV wireless channels will have high Doppler frequencies, especially the fixed wing UAVs.  On one hand, we can utilize the dominance of the line of sight channels to achieve high-capacity for emerging mmWave communications. On the other hand, due to the continuous movements of UAVs with different velocities coupled with the higher carrier frequency in the mmWave band, the doppler shift will increase [8].

§.§ Path Loss Models

Path loss is an essential in the design and analysis of wireless communication channels and represents the amount of reduction in power density of a transmitted signal. The characteristics of aerial wireless channels are different than the terrestrial wireless channels due to the variations in the propagation environments and hence the path loss models for UAVs are also different than the traditional path loss models for terrestrial wireless channels. We classify the UAV path loss models based on environment, altitude and telecommunication link as shown in Figure <ref>

§.§.§ Air-to-Ground Path Loss for Low Altitude Platforms

The authors in [24] present a statistical propagation model for predicting the path loss between a low altitude UAV and a Ground terminal. In this model, the authors assume that a UAV transmits data to more than 37000 ground receivers and use the Wireless InSite ray tracing software to model three types of rays (Direct, Reflected and Diffracted). Based on the simulation results, they divide the receivers to three groups. The first group corresponds to receivers that have Line-of-Sight or near-Line-of-Sight conditions. The second group corresponds to receivers with non Line-of-Sight condition, but still receiving coverage via strong reflection and refraction. The third group corresponds to receivers that have deep fading conditions resulting from consecutive reflections and diffractions, the third group only represents 3% of receivers. Therefore, based on the first and second groups, the authors present the path loss model as a function of the altitude $h$ and the coverage radius $R$ as shown in Figure <ref> and it is given as follows:

\begin{equation}
\begin{split}
L(h,R)=P(LOS)\times L_{LOS}+P(NLOS)\times L_{NLOS}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
P(LOS)=\dfrac{1}{1+\alpha.exp(-\beta[\frac{180}{\pi}\theta -\alpha] )}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
L_{LOS}(dB)=20log(\dfrac{4\pi f_cd}{c})+\zeta_{LOS}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
L_{NLOS}(dB)=20log(\dfrac{4\pi f_cd}{c})+\zeta_{NLOS}
\end{split}
\end{equation}

where $P(LOS)$ is the probability of having line of sight (LOS) connection at an elevation angle of $\theta$, $P(NLOS)$ is the probability of having non LOS connection and it equals (1- $P(LOS)$), $L_{LOS}$ and $L_{NLOS}$ are the average path loss for LOS and NLOS paths. In equations (2), (3) and (4), $\alpha$ and $\beta$ are constant values which depend on the environment, $f_c$ is the carrier frequency, $d$ is the distance between the UAV and the ground user, $c$ is the speed of the light, $\zeta_{LOS}$ and $\zeta_{NLOS}$ are the average additional loss which depends on the environment.

Coverage Zone By a Low Altitude UAV.

Providing Wireless Coverage for Indoor Users.

In this path loss model, the authors assume that all users are outdoor and the location of each user can be represented by an outdoor 2D point. These assumptions limit the applicability of this model when one needs to consider indoor users. The authors of [215] describe the tradeoff in this model. At a low altitude, the path loss between the UAV and the ground user decreases, while the probability of line of sight links also decreases. On the other hand, at a high altitude line of sight connections exist with a high probability, while the path loss increases.

§.§.§ Outdoor-to-Indoor Path Loss Model

The Air-to-Ground path loss model presented in [24] is not appropriate when we consider wireless coverage for indoor users, because this model assumes that all users are outdoor and located at 2D points. In [216], the authors adopt the Outdoor-Indoor path loss model, certified by the International Telecommunication Union (ITU) [217] to provide wireless coverage for indoor users using UAV as shown in Figure <ref>. The path loss is given as follows:

\begin{equation}
\begin{split}
\end{split}
\end{equation}

where $L_F$ is the free space path loss, $L_B$ is the building penetration loss, and $L_I$ is the indoor loss. Also, $d_{out}$ is the distance between the UAV and indoor user, $\theta_i$ is the incident angle, and $d_{in}$ is the indoor distance of the user inside the building. In this model, we also have $w=20$, $g_1$=32.4, $g_2$=14, $g_3$=15, $g_4$=0.5 and $f$ is the carrier frequency. The authors of [216] describe the tradeoff in the above model when the horizontal distance between the UAV and a user changes. When this horizontal distance increases, the free space path loss (i.e.,$L_F$) increases as $d_{out}$ increases, while the building penetration loss (i.e., $L_B$) decreases as the incident angle (i.e., $\theta_i$) decreases.

§.§.§ Cellular-to-UAV Path Loss Model

In [218], the authors model the statistical behavior of the path loss from a cellular base station towards a flying UAV. They present the path loss model based on extensive field experiments that involve collecting both terrestrial and aerial coverage samples. They report the value of the path loss as a function of the depression angle and the terrestrial coverage beneath the UAV as shown in Figure <ref>. The path loss is given as follows:

\begin{equation}
\begin{split}
L(d,\theta)=L_{ter}(d)+\eta(\theta)+X_{uav}(\theta)=10\alpha log(d)+\\A(\theta -\theta_o)exp(-\dfrac{\theta -\theta_o}{B})+\eta_o+N(0,a\theta +\sigma_o)
\end{split}
\end{equation}

where $L_{ter}(d)$ is the mean terrestrial path-loss at a given terrestrial distance $d$ from the cellular base station, $\eta (\theta)$ is the excess aerial path-loss, $X_{uav}(\theta)$ is a Gaussian random variable with an angle-dependent standard deviation $\sigma_{uav}(\theta)$ representing the shadowing component, $\theta$ is depression angle between the UAV and the cellular base station, $\alpha$ is the path-loss exponent. Also, $A, B, \theta_o$  and $\eta_o$ are fitting parameters.

§.§.§ Air-to-Ground Path Loss for High Altitude Platforms

The authors in [219] present an empirical propagation prediction model for mobile communications from high altitude platforms in built-up areas, where the frequency band is $2\textendash6$ GHz. The probability of LOS paths between the UAV and a ground user and the additional shadowing path loss for NLOS paths are presented as a function of the elevation angle $(\theta)$. The path loss model is defined for four different types of built-up area (Suburban area, Urban area, Dense urban area and Urban high-rise area). The path loss is given as follows:

\begin{equation}
\begin{split}
L=P(LOS)\times L_{LOS}+P(NLOS)\times L_{NLOS}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\end{split}
\end{equation}

In equation (7), $P(LOS)$ is the probability of having line of sight (LOS) connection at an evaluation angle of $\theta$, $P(NLOS)$ is the probability of having non LOS connection and it equals (1- $P(LOS)$), $L_{LOS}$ and $L_{NLOS}$ are the average path loss for LOS and NLOS paths. In equation (8), $a, b, c, d$ and $e$ are the empirical parameters. In equations (9) and (10),  $d_{km}$ is the distance between the UAV and the ground user in km, $f_{GHz}$ is the frequency in GHz, $L_s$ is a random shadowing in dB as a function of the elevation angle $(\theta)$, $\zeta_{LOS}$ and $\zeta_{NLOS}$ are the average additional losses which depend on the environment.

§.§.§ UAV to UAV path Loss Model

In general, the UAV-to-UAV wireless channels are line of sight dominated, so that the free space path loss can be adopted for the aerial channels. Due to the continuous movements of UAVs with different velocities, the UAV-to-UAV wireless channels will have high Doppler frequencies (especially the fixed wing UAVs).

In Table <ref>, we make a comparison among the path loss models based on operating frequency, altitude, environment, type of link, type of experiments and challenges.

comparison among path loss models

Path loss model    Frequency band   Altitude   Environment   Type of link   Type of experiments   Challenges

[24]	   2 GHz   LAP   Outdoor-ground users   Downlink   Simulations   1) The authors didn’t consider the indoor users.
2) The authors  didn't consider different 5G
frequency bands.

3) The locations of outdoor users are 2D.

[217]	   2 GHz to 6 GHz   LAP   Indoor users   Downlink   Real experiments   1) The authors didn’t consider the different

types of the building structures.

2) The authors  didn't consider different 5G
frequency bands.

[218]	   850 MHz   LAP   Outdoor cellular   Uplink   Real experiments   1) The authors didn’t investigate denser urban

base station          environments.

2) The authors  didn't consider different 5G
frequency bands such as mmWave.

[219]	   2 GHz to 6 GHz   HAP   Outdoor-ground users   Downlink   Simulations   1) The authors didn’t consider the indoor users
2) The authors  didn't consider different 5G
frequency bands.

3) The locations of outdoor users are 2D.

Free space	   All frequency   LAP,   UAV to UAV    Uplink,    Real experiments    1) High Doppler shift.

bands   HAP   channels   downlink

§.§ UAV Deployment Strategies

UAVs deployment problem is gaining significant importance in UAV-based wireless communications where the performance of the aerial wireless network depends on the deployment strategy and the 3D placements of UAVs. In this section, we classify the UAV deployment strategies based on the objective functions as shown in Figure <ref>.

Cellular-to-UAV Path Loss Parameters.

§.§.§ Deployment Strategies for Minimizing the Transmit Power of UAVs

The authors in [220] propose an efficient deployment framework for deploying the aerial base stations, where the goal is to minimize the total required transmit power of UAVs while satisfying the users rate requirements. They apply the optimal transport theory to obtain the optimal cell association and derive the optimal UAV's locations using the facility location framework. The authors in [215] investigate the downlink coverage performance of a UAV, where the objective is to find the optimal UAV altitude which leads to the maximum ground coverage and the minimum transmit power. The authors in [216] propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations. They study the problem of efficient UAV placement, where the objective is to minimize the total transmit power required to cover the entire high-rise building. In [221], the authors propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users. The authors in [222]
utilize UAVs to provide wireless coverage for indoor and outdoor users in massively crowded events, where the objective is to find the optimal UAV placement which lead to the minimum transmit power. In [223], the authors propose an optimal placement algorithm for UAV that maximizes the number of covered users using the minimum transmit power. The algorithm decouple the UAV deployment problem in the vertical and horizontal dimensions without any loss of optimality. The authors in [224] consider two types of
users in the network: the downlink users served by the UAV and device-to-device users that communicate directly with one another. In the mobile UAV scenario, using the disk covering problem, the entire target geographical area can be completely covered by the UAV in a shortest time with a minimum required
transmit power.  They also derive the overall outage probability for device-to-device users, and show that the outage probability increases as the number of stop points that the UAV needs to completely cover the area increases.

§.§.§ Deployment Strategies for Maximizing the Wireless Coverage of UAVs

In [209], the authors highlight the properties of the UAV placement problem, and formulate it as a 3D placement problem with the objective of maximizing the revenue of the network, which is proportional to the number of users covered by a UAV. They formulate an equivalent problem which can be solved efficiently to find the size of the coverage region and the altitude of a UAV. The authors in [225] study the optimal
deployment of UAVs equipped with directional antennas, using
circle packing theory. The 3D locations of the UAVs are
determined in a way that the total coverage area is maximized. In [226], the authors introduce network-centric and user-centric approaches, the optimal 3D backhaul-aware placement of a UAV is found for each approach. In the network-centric approach, the network tries to serve as many users as possible, regardless of their rate requirements. In the user-centric approach, the users are determined based on the priority. The total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks. The authors in [227] study an efficient 3D UAV placement that maximizes the number of covered users with different Quality-of-Service requirements. They model the placement problem as a multiple circles placement problem and propose an optimal placement algorithm that utilizes an exhaustive search over a one-dimensional parameter in a closed region. They propose a low-complexity algorithm, maximal weighted area algorithm, to tackle the placement problem. In [228], the authors aim to maximize the indoor wireless coverage using UAVs equipped with directional antennas. They present two methods to place the UAVs; providing wireless coverage from one building side and from two building sides. The authors in [229] utilize UAVs-hubs to provide connectivity to small-cell base stations with the core network. The goal is to find the best possible association of the small cell base stations with the UAVs-hubs such that the sum-rate of the overall system is maximized depending on a number of factors including  maximum backhaul data rate of the link between the core network and mother-UAV-hub, maximum bandwidth of each UAV-hub available for small-cell base stations, maximum number of links that every UAV-hub can support and minimum signal-to-interference-plus-noise ratio. They present an efficient and distributed solution of the designed problem, which performs a greedy search to maximize the sum rate of the overall network. In [230], the authors propose an efficient framework for optimizing the performance of UAV-based wireless systems in terms of the average number of bits transmitted to users and UAVs's hovering duration. They investigate two scenarios: UAV communication under hover time constraints and UAV communication under load constraints. In the first scenario, given the maximum possible hover time of each UAV, the total data service under user fairness considerations is maximized. They utilize the framework of optimal transport theory and prove that the cell partitioning problem is equivalent to a convex optimization problem. Then, they propose a gradient-based algorithm for optimally partitioning the geographical area based on the users's distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirement of each user at a given location, they minimize the average hover time needed for completely serving the ground users by exploiting the optimal transport theory.

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[,draw=black] (b1) Deployment strategies
for minimizing the transmit power of UAVs
[right=5pt,draw=black] (b2) at (b1.east) Deployment strategies for maximizing the wireless coverage of UAVs
[right=5pt,draw=black] (b3) at (b2.east) Deployment strategies
for minimizing the
number of UAVs required to perform task

[right=5pt,draw=black] (b4) at (b3.east) Deployment strategies
to collect data using

[above=10pt, text width=160pt,draw=black] (top) at ($(b2.north)!.5!(b3.north)$) Classification of Deployment strategies for UAVs
(atop) at ($(top.south) + (0,-5pt)$);
(btop) at ($(b3.south) + (0,-5pt)$);
[thick] (top.south) – (atop)
(b1.north) |- (atop) -| (b4.north)
(b2.north) |- (atop) -| (b3.north);

Classification of Deployment Strategies for UAVs.

comparison among research papers related to UAV deployment strategies based on objective functions

Reference   Number of UAVs   Type of environment   Type of antenna   Operating frequency   Objective function   Deployment strategy

[220]   Multiple UAVs   Outdoor   Omni-directional   2 GHz   Minimizing the transmit power   Optimal transport theory

of UAVs

[215]   Single UAV   Outdoor   Directional   2 GHz   Minimizing the transmit power   Closed-form expression

of UAV    for the UAV placement

[216]   Single UAV   Indoor   Omni-directional   2 GHz   Minimizing the transmit power    Gradient descent

of UAV     algorithm

[221]	   Single UAV   Indoor   Omni-directional   2 GHz   Minimizing the transmit power    Particle swarm

of UAV     optimization

[222]   Single UAV   Outdoor-Indoor   Omni-directional   2 GHz   Minimizing the transmit power    Particle swarm

of UAV   optimization, K-means

with ternary search

[223]   Single UAV   Outdoor   Directional   2 GHz   Minimizing the transmit power    Optimal 3D placement

of UAV     algorithm

[224]   Single UAV   Outdoor   Directional   2 GHz   Minimizing the transmit power   Disk covering problem

of UAV

[209]   Single UAV   Outdoor   Directional   2.5 GHz   Maximizing the wireless coverage   Bisection search

of UAV   algorithm

[225]   Multiple UAVs   Outdoor   Directional   2 GHz   Maximizing the wireless coverage   Circle packing

of UAVs   theory

[226]   Single UAV   Outdoor   Directional   2 GHz   Maximizing the wireless coverage   branch and

of UAV   bound algorithm

[227]    Single UAV   Outdoor   Directional   2 GHz   Maximizing the wireless coverage   exhaustive search

of UAV   algorithm

[228]   Multiple UAVs   Indoor   Directional   2 GHz   Maximizing the wireless coverage   efficient algorithm

of UAVs

[229]   Multiple UAVs   Outdoor   Directional   2 GHz   Maximizing the wireless coverage   branch and

of UAVs   bound algorithm

[230]   Multiple UAVs   Outdoor   Directional   2 GHz   Maximizing the wireless coverage   Optimal transport

of UAVs   theory, Gradient

[231]   Multiple UAVs   Outdoor   Directional   2 GHz   Minimizing the number    Particle swarm

of UAVs   optimization

[232]	   Multiple UAVs   Outdoor   Directional   2 GHz   Minimizing the number     The cycles with limited

of UAVs    energy algorithm

[233]	   Multiple UAVs   Indoor   Omni-directional   2 GHz   Minimizing the number    Particle swarm

of UAVs   optimization, K-means

[234]	   Multiple UAVs   Outdoor   Directional   2 GHz   Minimizing the number    polynomial time

of UAVs   approximate

[235]	   Multiple UAVs   Outdoor   Directional   2 GHz   Minimizing the number    Spiral UAVs

of UAVs   placement algorithm

[236]	   Multiple UAVs   Outdoor   Directional   2 GHz   Collecting data using   Optimal transport

UAVs   theory

[237]	   Single UAV   Outdoor   Directional   2 GHz   Collecting data using   Two practical

UAV    UAV trajectories:

circular and

straight flights

[238]	   Multiple UAVs   Outdoor   Directional   2 GHz   Collecting data using   Genetics

UAVs   algorithm

[239]   Multiple UAVs   Outdoor   directional   2 GHz   Collecting data using    efficient algorithm

UAVs   for path planning

[240]   Single UAV   Outdoor   Directional   2 GHz   Collecting data using   efficient iterative

UAVs   algorithm

§.§.§ Deployment Strategies for Minimizing the Number of UAVs Required to Perform Task

The authors in [231] propose a
method to find the placements of UAVs in an area with different
user densities using the particle swarm optimization. The goal is to find the minimum number of UAVs and their 3D placements so that all the users are served. In [232], the authors study the problem of minimizing the number of UAVs required for a continuous coverage of a given area, given the recharging requirement. They prove that this problem is NP-complete. Due to its intractability, they study partitioning the coverage graph into cycles that start at the charging station. Based on this analysis, they then develop an efficient algorithm, the cycles with limited energy algorithm, that minimizes the number of UAVs required for a continuous coverage. The authors in [233] study the problem of minimizing the number of UAVs required to provide wireless coverage to indoor users and prove that this problem is NP-complete.
Due to the intractability of the problem, they use clustering to minimize the number of UAVs required to cover the indoor users. They assume that each cluster will be covered by only one UAV and apply the particle swarm optimization to find the UAV 3D location and UAV transmit power needed to cover each cluster. In [234], the authors study the problem of deploying minimum number of UAVs to maintain the connectivity of ground MANETs under the condition that some UAVs have already been deployed in the field. They formulate this problem as a minimum steiner tree problem with existing mobile steiner points under edge length bound constraints and prove that the problem is NP-Complete. They propose an existing UAVs aware polynomial time approximate algorithm to solve the problem that uses a maximum match heuristic to compute new positions for existing UAVs. The authors in [235] aim to minimize the number of UAVs required to provide wireless coverage for a group of distributed ground terminals, ensuring that each ground terminal is within the communication range of at least one UAV. They propose a polynomial-time algorithm with successive UAV placement, where the UAVs are placed sequentially starting from the area perimeter of the uncovered ground terminals along a spiral path towards the center, until all ground terminals are covered.

Interference Mitigation Techniques and Challenges.

§.§.§ Deployment Strategies to Collect Data Using UAVs

The authors in [236] propose an efficient framework for deploying and moving UAVs to collect data from
ground Internet of Things devices. They minimize the total
transmit power of the devices by properly clustering
the devices where each cluster being served by one UAV. The optimal trajectories of the UAVs are determined by exploiting the framework of optimal transport theory. In [237], the authors present a UAV enabled data collection system, where a UAV is dispatched to collect a given amount of data from ground terminals at fixed location. They aim to find the optimal ground terminal transmit power and UAV trajectory that achieve different Pareto optimal energy trade-offs between the ground terminal and the UAV. The authors in [238] study the problem of trajectory planning for wireless sensor network data collecting deployed in remote areas with a cooperative system of UAVs. The missions are given by a set of ground points which define wireless sensor network gathering zones and each UAV should pass through them to gather the data while avoiding passing over forbidden areas and collisions between UAVs. The proposed UAV trajectory planners are based on Genetics Algorithm, Rapidly-exploring Random Trees and Optimal Rapidly-exploring Random Trees. The authors in [239] design a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. They  identify the key challenges in each component and propose efficient solutions. They propose a Fast Path Planning with Rules algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. In [240], the authors jointly optimize the sensor nodes wake-up schedule and UAV’s trajectory to minimize the maximum energy consumption of all sensor nodes, while ensuring that the required amount of data is collected reliably from each sensor node. They formulate a mixed-integer non-convex optimization problem and apply the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution.

UAVs can be classified into two types: fixed wing and rotary wing, each with its own strengths and weaknesses. Fixed-wing UAV usually has high speed and payload, but they need to maintain a continuous forward motion to remain aloft, thus are not suitable for stationary uses. In contrast, rotary-wing UAV such as quadcopter, usually has limited mobility and payload, but they are able to move in any direction as well as to stay stationary in the air. Thus, the choice of UAVs critically depends on the uses [8]. In Table <ref>, we make a comparison among the research papers related to UAV deployment strategies based on the objective functions.

§.§ Interference Mitigation

One of the techniques to mitigate interference is the coordinated multipoint technique [241]. In downlink coordinated multipoint technique, the transmission aerial base stations cooperate in scheduling and transmission in order to strength the received signal and mitigate inter-cell interference [241]. On the other hand, the physical uplink shared channel (PUSCH) is received at aerial base stations in uplink coordinated multipoint technique. The scheduling decision is based on the coordination among UAVs [242]. The new challenge here is that UAVs receive interfering signals from more ground terminals in the downlink and their uplink transmitted signals are visible to more ground terminals due to the high probability of line of sight links. Therefore, the coordinated multipoint techniques must be applied across a larger set of cells to mitigate the interference and hence the coordination complexity will increase [243].

We can also mitigate interference by utilizing receiver techniques such as interference rejection combining and network-assisted interference cancellation and suppression. Compared to smart phones, we can equip UAVs with more antennas, which can be used to mitigate interference from more ground base stations. With MIMO antennas, UAVs can use beamforming to enables directional signal transmission or reception to achieve spatial selectivity which is also an efficient interference mitigation technique [243].

Another interference mitigation technique is to partition radio resources so that ground traffic and aerial traffic are served with orthogonal radio resources. This simple technique may not be efficient since the reserved radio resources for aerial traffic may be not fully utilized. Therefore, UAV operators need to provide more information about the trajectories and 3D placements of UAVs to construct more dynamic radio resource management [243].

A powerful interference mitigation technique is the power control technique in which an optimized setting of power control parameters can be applied to reduce the interference generated by UAVs. This technique can minimize interference, increase spectral efficiency, and benefit UAVs as well as ground terminals [243].

Dedicated cells for the UAVs is another option to mitigate interference, where the directional antennas are pointed towards the sky instead of down-tilted. These dedicated cells will be a practical solution especially in UAV hotspots where frequent and dense UAV takeoffs and landings occur [243]. In Figure <ref>, we summarize the interference mitigation techniques and challenges.

UAV Machine learning algorithms

Category   Learning techniques   Key characteristics    Application

Regression models   - Estimate the variables’ relationships        - Energy learning [244]

- Linear and logistics regression

| |-|-|-|
K-nearest neighbor   - Majority vote of neighbors                  - Energy learning [244]

| |-|-|-|
Supervised	   Support vector machines   - Non-linear mapping to high dimension   - MIMO channel learning [245]

learning	      - Separate hyperplane classification

| |-|-|-|
Bayesian learning	   - A posteriori distribution calculation        -  Massive MIMO learning [246]

- Gaussians mixture, expectation max       - Cognitive spectrum learning

and hidden Markov models                [247, 248, 249]

Unsupervised	   K-means clustering   - K partition clustering                        - Heterogeneous
networks [250]

- Iterative updating algorithm

| |-|-|-|
learning	    Independent
component analysis   - Reveal hidden independent factors        - Spectrum learning in [251]

cognitive radio

Markov decision processes/A partially   -  Bellman equation
maximization           - Energy harvesting [252]

observable Markov decision process   - Value iteration algorithm

| |-|-|-|
Reinforcement   Q-learning   - Unknown system
transition model        - Femto and small cells

learning      - Q-function maximization                   [253]-[254]

| |-|-|-|
Multi-armed bandit   - Exploration vs. exploitation                - Device-to-device networks [255]

- Multi-armed bandit game

§.§ Research Trends and Future Insights

§.§.§ Cloud and Big Data

A cloud for UAVs contains data storage and high-performance computing, combined with big data analysis tools [256]. It can provide an economic and efficient use of centralized resources for decision making and network-wide monitoring [256, 257, 258]. If UAVs are utilized by a traditional cellular network operator (CNO), the cloud is just the data center of the CNO (similar to a private cloud), where the CNO can choose to share its knowledge with some other CNOs or utilize it for its own business uses. On the other hand, if the UAVs are utilized by an infrastructure provider, the infrastructure provider can utilize the cloud to gather information from mobile virtual network operators and service providers. Under such scenario, it is important to guarantee security, privacy, and latency. To better exploit the benefit of the cloud, we can use a programmable network allowing dynamic updates based on big data processing, for which network functions virtualization and software defined networking can be research trends  [256].

§.§.§ Machine Learning

Next-generation aerial base stations are expected to learn the diverse characteristics of users’ behavior, in order to autonomously find the optimal system settings. These intelligent UAVs have to use sophisticated learning and decision-making, one promising solution is to utilize machine learning. Machine learning algorithms can be simply classified as supervised, unsupervised and reinforcement learning as shown in Table <ref>. The family of supervised learning algorithms utilizes known models and labels to enable the estimation of unknown parameters. They can be used for spectrum sensing and white space detection in cognitive radio, massive MIMO channel estimation and data detection, as well as for adaptive filtering in signal processing for 5G communications. They can also be utilized in higher-layer applications, such as estimating the mobile users’ locations and behaviors, which can help the UAV operators to enhance the quality of their services. The family of unsupervised learning algorithms utilizes the input data itself in a heuristic manner. They can be used for heterogeneous base station clustering in wireless networks, for access point association in ubiquitous WiFi networks, for cell clustering in cooperative ultra-dense small-cell networks, and for load-balancing in heterogeneous networks. They can also be utilized in fault/intrusion detections and for the users’ behavior-classification. The family of reinforcement learning algorithms utilizes dynamic iterative learning and decision-making process. They can be used for estimating the mobile users’ decision making under unknown scenarios, such as channel access under unknown channel availability conditions in spectrum sharing, base station association under the unknown energy status of the base stations in energy harvesting networks, and distributed resource allocation under unknown resource quality conditions in femto/small-cell networks [259].

§.§.§ Network Functions Virtualization

Network functions virtualization (NFV) reduces the need of deploying specific network devices for the integration of UAVs [256, 257]. NFV allows a programmable network structure by virtualizing the network functions on storage devices, servers, and switches, which is practical for UAVs requiring seamless integration to the existing network. Moreover, virtualization of UAVs as shared resources among cellular virtual network operators can decrease OPEX for each party [260]. Here, the SDN can be useful for the complicated control and interconnection of virtual network functions (VNFs) [256, 257].

§.§.§ Software Defined Networking

For mobile networks, a centralized SDN controller can make a more efficient allocation of radio resources, which is particularly important to exploit UAVs [256, 257]. For instance, SDN-based load balancing can be useful for multi-tier UAV networks, such that the load of each aerial base station and terrestrial base station is optimized precisely. A SDN controller can also update routing such that part of traffic from the UAVs is carried through the network without any network congestions [256, 257, 258]. For further exploitation of the new degree of freedom provided by the mobility of UAVs, the 3D placements of UAVs can be adjusted to optimize paging and polling, and location management parameters can be updated dynamically via the unified protocols of SDN [256].

§.§.§ Millimeter-Wave

Millimeter-wave (mmWave) technology can be utilized to provide high data rate wireless communications for UAV networks [261]. The main difference between utilizing mmWave in UAV aerial networks and utilizing mmWave in terrestrial cellular networks is that a UAV aerial base station may move around. Hence, the challenges of mmWave in terrestrial cellular networks apply to the mmWave UAV cellular network as well, including rapid channel variation, blockage, range and directional communications, multi-user access, and others [261, 262]. Compared to mmWave communications for static stations, the time constraint for beamforming training is more critical due to UAV mobility. For fast beamforming training and tracking in mmWave UAV cellular networks, the hierarchical beamforming codebook is able to create highly directional beam patterns, and achieves excellent beam detection performance [261]. Although the UAV wireless channels have high Doppler frequencies due to the continuous movements of UAVs with different velocities, the major multipath components are only affected by slow variations due to high gain directional transmissions. In beam division multiple access (BDMA), multiple users with different beams may access the channel at the same time due to the highly directional transmissions of mmWave [261, 262, 263]. This technique improves the capacity significantly, due to the large bandwidth of mmWave technology and the use of BDMA in the spatial domain. The blockage problem can be mitigated by utilizing intelligent cruising algorithms that enable UAVs to fly out of a blockage zone and enhance the probability of line of sight links [261].

§.§.§ Free Space Optical

Free space optical (FSO) technology can be used to provide wireless connectivity to remote places by utilizing UAVs, where physical access to 3G or 4G network is either minimal or never present [76]. It can be involved in the integration of ground and aerial networks with the help of UAVs by providing last mile wireless coverage to sensitive areas (e.g., battlefields, disaster relief, etc.) where high bandwidth and accessibility are required. For instance, Facebook will provide wireless connectivity via FSO links to remote areas by utilizing solar-powered high altitude UAVs [76, 264, 265]. For areas where deployment of UAVs is impractical or uneconomical, geostationary earth orbit and low earth orbit satellites can be utilized to provide wireless connectivity to the ground users using the FSO links. The main challenge facing UAV-free space optics technology is the high blockage probability of the vertical FSO link due to weather conditions. The authors in [214] propose some methods to tackle this problem. The first method is to use an adaptive algorithm that controls the transmit power
according to weather conditions and it may also adjust other system parameters such as incident angle of the FSO transceiver to compensate the link degradation, e.g., under bad weather conditions, high power vertical FSO beams should be used while low power beams could be used under good weather conditions. The second method is to use a system optimization algorithm that can optimize the UAV placement, e.g., hovering below clouds over negligible turbulence geographical area.

§.§.§ Future Insights

Some of the future possible directions for this research are:

* The majority of literature focuses on providing the wireless coverage for outdoor ground users, although 90% of the time  people are indoor and 80% of the mobile Internet access traffic
also happens indoors [266, 267, 268]. Therefore, it is important to study the indoor wireless coverage problems by utilizing UAVs.

* The majority of literature does not consider the limited energy capacity of UAV as a constraint when they study the wireless coverage of UAVs,  where the energy consumption during data transmission and reception is much smaller than the energy consumption during the UAV hovering. It only constitutes 10%-20% of the UAV energy capacity [2].

* Some path loss models are based on simulations softwares such as Air-to-Ground path loss for low altitude platforms and Air-to-Ground path loss for high altitude platforms, therefore it is necessary to do real experiments to model the statistical behavior of the path loss.

* To the best of our knowledge, no studies present the path loss models for uplink scenario and mmWave bands.

* There are challenges facing the new technologies such as mmwave. The challenges facing UAV-mmWave technology are fast beamforming training and tracking requirement, rapid channel variation, directional and range communications, blockage and multi-user access [261].

* We need more studies about the topology of UAV wireless networks where this topology remains fluid during: a) Changing the number of UAVs; b) Changing the number of channels; c) The relative placements of the UAVs altering [2].

* There is a need to study UAV routing protocols where it is difficult to construct a simple implementation for proactive or reactive schemes [2].

* There is a need for a seamless mechanism to transfer the users during the handoff process between UAVs, when out of service UAVs are replaced by active ones [2].

* Further research is required to determine which technology is right for UAV applications, where the common technologies utilized in UAV applications are the IEEE 802.11 due to wide availability in current wireless devices and their appropriateness for small-scale UAVs [1].

* D2D communications is an efficient technique to improve the capacity in ground communications systems [269]. An important problem for future research is the joint optimization of the UAV path planning, coding, node clustering, as well as D2D file sharing [8].

* Utilizing UAVs in public safety communications needs further research, where topology formation, cooperation between UAVs in a multi-UAV network, energy constraints, and  mobility model are the challenges that are facing UAVs in public safety communications [9].

* In UAVs-based IoT services, it is difficult
to control and manage a high number of UAVs. The reason
is that each UAV may host more than one IoT device, such as different types of cameras and aerial sensors. Moreover, conflict of interest between different devices likely to happen many times, e.g., taking two videos
from two different angles from one fixed placement. For future research, we need to propose efficient algorithms to
solve the conflict of interest among Internet of things devices on-board [3].

* For future research, it is important to propose efficient techniques that manage and control the power consumption of Internet of things devices on-board [3].

* The security is one of the most critical issues to think about in UAVs-based Internet of things services, methods to avoid the aerial jammer on the communications are needed [3].

§ PART III: KEY CHALLENGES AND CONCLUSION

§ KEY CHALLENGES

§.§ Charging Challenges

UAV missions necessitate an effective energy management for battery-powered UAVs. Reliable, continuous, and smart management can help UAVs to achieve their missions and prevent loss and damage. The UAV's battery capacity is a key factor for enabling persistent missions. But as the battery capacity increases, its weight increases, which cause the UAV to consume more energy for a given mission. The main directions in the literature to mitigate the limitations in UAV's batteries are: (1) UAV battery management, (2) Wireless charging for UAVs, (3) Solar powered UAVs, and (4) Machine learning and communications techniques.

§.§.§ Battery Management

Battery management research in UAVs includes planning, scheduling, and replacement of battery so UAVs can accomplish their flight missions.  This has been studied in the literature from different perspectives. Saha et al. in [270] build a model to predict the end of battery charge for UAVs based on particle filter algorithm. They utilize a discharge curve for UAV Li-Po battery to tune the particle filter. They have shown that the depletion of the battery is not only related to the initial Start of Charge (SOC) but also, load profile and battery health conditions can be crucial factors. Park et al. in [271]  investigate the battery assignment and scheduling for UAVs used in delivery business. Their objective is to minimize the deterioration in battery health. After splitting the problem into two parts; one for battery assignment and the other for battery scheduling. Heuristic algorithm and integer linear programming are used to solve the assignment and scheduling problems, respectively. The idle time between two successive charging cycles and the depth of the discharge are the main factors that control these algorithms. The use of UAVs in long time and enduring missions makes UAV battery swapping solutions necessary to accomplish these missions. An autonomous battery swapping system for UAVs is first introduced in [272]. The battery swapping consists of landing platform, battery charger, battery storage compartment, and micro-controller. Similar concept to this autonomous battery swapping system was adopted and improved by different researchers in [273, 274, 275, 276]. The hot swap term is adopted to refer to the continuous powering for the UAV during battery swapping. Figure <ref> shows an illustration of hot swapping systems. First, the UAV is connected to an external power supply during the swapping process. This will prevent data loss during swapping as it usually happens in cold swapping. Second, the drained battery is removed and stored in multi-battery compartment and charging station to be recharged. Third, a charged battery is installed in the UAV, and finally, the external power supply is disconnected. Another important aspect of the improvements presented in [273, 274, 275, 276] is the several designs for the landing platform, which is an important part of the swapping system because it can compensate the error in the UAV positioning on the landing point.  Table <ref> shows the classification of autonomous battery swapping systems. It can be seen from this table that the swapping time for most of these systems is around one minute. This is a short period comparing to the battery charging average time, which is between 45-60 minutes.

Illustration of Battery Hot Swapping System.

classification of autonomous battery swapping systems

Ref # /Year     UAV Type     Battery Type     Positioning System    Swapping time   Hot vs Cold Swapping

[272]/2010    Quadrotor   	Li-Po   	V-Shape Basket   	2 min.	    Cold swap
[273]/2011    Quadrotor   	Li-Po    Sloped landing pad   	11.8±3 sec.   	Hot swaps

[274]/2012    Quadrotor   	Li-Po	    Dynamic forced positioning (Arm Actuation)   	47-60 sec.   	Cold swap

[276]/2015    Quadrotor   	Li-Po    	Sloped landing pad   	60-70 sec.   	Hot swaps

[275]/2015    Quadrotor   	Li-Po    	Rack with landing guide   	60 sec.   	Hot swaps

§.§.§ Wireless Charging For UAV

Simic et. Al in [277] study the feasibility of recharging the UAV from power lines while inspecting these power lines. Experimental tests show the possibility of energy harvesting from power lines. The authors design a circular antenna to harvest the required energy from power lines. Wang et al. in [278] propose an automatic charging system for the UAV. The system uses charging stations allocated along the path of UAV mission. Each charging station consists of wireless charging pad, solar panel, battery, and power converter. All these components are mounted on a pole. The UAV employs GPS module and wireless network module to navigate to the autonomous charging station. When the power level in the UAV's battery drops to a predefined level. The UAV will communicate with the central control room, which will direct the UAV to the closest charging station. The GPS module will help the UAV to navigate to the assigned station. Different wireless power transfer technologies were used in literature to implement the automatic charging stations. In [279, 280, 281], the authors utilize the magnetic resonance coupling technology to implement an automatic drone charging station. Dunbar et al. in [282] use RF Far field Wireless Power Transfer (WPT) to recharge micro UAV after landing. A flexible rectenna was mounted on the body of the UAV to receive RF signals from power transmitter. Mostafa et al. in [283]  develop a WPT  charging system based on the capacitive coupling technology. One of the major challenges that face the deployment of autonomous wireless charging stations is the precise landing of the UAV on the charging pad. The imprecise landing will lead to imperfect alignment between the power transmitter and the power receiver, which means less efficiency and long charging time. Table <ref> shows precise methodologies adopted in the literature. In [278], the authors use GPS to land the UAV on the charging pad. In [284], the authors corporate the GPS system with camera and image processing system to detect the charging station. The authors in [280] allow for imprecise landing on wide frame landing pad. Then a wireless charging transmitting coil is stationed perfectly in the bottom of the UAV by using a stepper motor and two laser distance sensors.

classification of Precise Landing Methodologies

Ref # /Year       Precise Landing Methodology

[278]/2016    GPS system

[280]/2017    Landing frame with moving transmitting coil

[284]/2017    GPS system and vision-based target detection

§.§.§ Solar Powered UAVs

For long endurance and high altitude flights, solar-powered UAVs (SUAVs) can be a great choice. These UAVs use solar power as a primary source for propulsion and battery as a secondary source to be used during night and sun absence conditions. The literature in flight endurance and persistent missions for SUAVs can be classified into two main directions: path planning and optimization  and hybrid Models.  Path planning and optimization includes: (i) gravitational potential energy planning. This scheme optimizes the path of the UAV over three stages, ascending and battery charging stage during the day using solar power, descending stage during the night using gravitational potential energy, and level flight stage using battery [285, 286, 287], (ii) optimal path planing while tracking ground object [288, 289], (iii) optimal path planning based on UAV kinematics and its energy loss model [290], and (iv) optimal path planning utilizing wind and metrological data [291, 292].  On the other hand, Hybrid models include: (i) UAVs with hybrid power sources such as solar, battery, and fuel cells [293] and (ii) UAVs with a hybrid propulsion system that can be transformed from fixed wing to quadcopter UAV [294, 295].

§.§.§ Machine Learning and Communications Techniques

Communication equipment and their status (transmit, receive, sleep, or idle) can affect drone flight time. Utilizing the state-of-the-art machine learning techniques can result in smarter energy management. These technologies and their role in UAV persistent  missions are covered in the literature under the following themes:

* Energy efficient UAV networks: This area covers maximizing energy efficiency in the network layer, data link layer, physical layer, and cross-layer protocols in a bid to help UAVs to perform long missions. Gupta et al. in [296] list and compare several algorithms used in each layer.

* Energy-based UAV fleet management: This includes modifying the mobility model of UAV fleet to incorporate energy as decision criterion as in [297]  to determine the next move of each UAV inside a fleet.

* Machine learning: This area covers research that utilizes machine learning techniques for path planning and optimization applications, taking into consideration energy limitations. Zhang et al. in  [298] use deep reinforcement learning to determine the fastest path to a charging station. While Choi et al. in  [299] use a density-based spatial clustering algorithm to build a two-layer obstacle collision avoidance system.

Several challenges still exist in the area of battery recharging and need to be appropriately addressed, including:

* Advancements in wireless charging techniques such as magnetic resonant coupling and inductive coupling techniques are popular and suitable to be used because both of them have acceptable efficiency for small to mid-range distances. The advancements in this technology are expected to have a positive impact on the use of UAVs.

* Development of battery technologies will allow UAVs to extend flight ranges. While Lithium-Ion batteries still dominant, PEM Fuel cells can be more convenient in UAVs because of their higher power density.

* Motion planning of UAVs is an extremely important factor since it affects the wireless charging efficiency and the average power delivered .

* The vast majority of the research, concerning UAV power management, focuses on multi-rotor UAVs. Other types of UAVs, such as fixed-wing UAVs, need more attention in future research.

* Artificial intelligence, especially deep learning, can promote more advances in the field of UAV power management. Many research frontiers still need to be explored in this field such as; Deep reinforcement learning in the path planning and battery scheduling, convolutional neural networks in identifying charge stations and precise landing in a charging station, and recurrent neural networks in developing discharge models and precisely predicting the end of the charge.

* Image processing techniques and smart sensors can also play an important role in identifying charging stations and facilitating precise positioning of UAVs on them.

§.§ Collision Avoidance and Swarming Challenges

One of the challenges facing UAVs is collision avoidance. UAV can collide with obstacles, which can either be moving or stationary objects in either indoor or outdoor environment. During UAV flights, it is important to avoid accidents with these obstacles. Therefore, the development of UAV collision avoidance techniques has gained research interest [60, 300]. In this section, we first present the major categories of collision avoidance methods. Then, the challenges associated with UAVs collision avoidance are presented. Moreover, we provide research trends and some future insights.

§.§.§ Collision Avoidance Approaches

Many collision avoidance approaches have been proposed in order to avoid potential collisions by UAVs.
The authors in [60] presented major categories of collision avoidance methods.
These methods can be summarized as geometric approaches [301], path planning approaches [59], potential field approaches [302, 303], and vision-based approaches [304]. Several collision avoidance techniques can be utilized for an indoor environment such as vision based methods (using cameras and optical sensors), on-board sensors based methods (using IR, ultrasonic, laser scanner, etc.) and vision based combined with sensor based methods [305]. The collision avoidance approaches are discussed in the following:

* Geometric Approach is a method that utilizes
the geometric analysis to avoid the collision. In [301], the geometric approach was utilized to ensure that the predefined minimum separation distance was not violated. This was done by computing the distance between two UAVs and the time required for the collision to occur. Moreover, it was used for path planning to avoid UAV collisions with obstacles.
Several different approaches utilize this method to avoid collision such as Point of Closest Approach [301], Collision Cone Approach [306, 302], and Dubins Paths Approach [307, 308].

* Path Planning Approach, which is also referred to as the optimized trajectory approach [309], is a grid based method that utilizes the path re-planning algorithm with graph search algorithm to find a collision free trajectory during the flight. It uses geometric techniques to find an efficient and collision free trajectory, so it shares some similarities with the geometric approach. This method divides the map into a grid and represents the grid as a weighted graph [59]. The grid with graph search algorithm are used to find collision free trajectory towards the desired target.

* Potential Field Approach is proposed by [303] to be used as a collision avoidance method for ground robots. It has also been utilized for collision avoidance among UAVs and obstacles. This method uses the repulsive force fields which cause the UAV to be repelled by obstacles. The potential function is divided into attracting force field which pulls the UAV towards the goal, and repulsive force field which is assigned with the obstacle[302].

* Vision-based obstacle detection approaches utilize images from small cameras mounted on UAVs to tackle collision challenges. Advances in integrated circuits technology have enabled the design of small and low power sensors and cameras. Combined with advances in computer vision methods, such camera can be used in effective obstacle detection and collision avoidance [304]. Moreover, this approach can be used efficiently for collision avoidance in the indoor environment. Such cameras provide real-time information about walls and obstacles in this environment. Many researchers utilized this method to avoid a collision and to provide fully autonomous UAV flights [310, 311, 312, 313]. The researchers in [311] use a monocular camera with forward facing to generate collision-free trajectory. In this method, all computations were performed off-board the UAV. To solve the off-board image processing problem, the authors in [314] proposed a collision avoidance system for UAVs with visual-based detection. The image processing operation was performed using two cameras and a small on-board computation unit.

§.§.§ Challenges

There are several challenges in the area of collision avoidance approaches and need to be appropriately addressed, including:

* The geometric approach utilizes the information such as location and velocity, which can be obtained using Automatic Dependent Surveillance Broadcast (ADS-B) sensing method. Thus, it is not applicable to non-aircraft obstacles. Furthermore, input data from ADS-B is sensitive to noise which hinders the exact calculation requirement of this approach. Moreover, ADS-B requires cooperation from another aircraft, which can be an intruder, which is referred to as cooperative sensing.

* In non-cooperative sensing, the geometric approach requires UAVs to sense and extract information about the environment and obstacles such as position, speed and size of the obstacles. One of the possible solutions is to combine with a vision-based approach that uses a passive device to detect obstacles. However, this approach requires significant data processing and only can be used when the objects are close enough. Therefore, hardware limitation for on-board processing should be taken into consideration.

* The diverse robotic control problem that allows UAV to perform complex maneuvers without collision can be solved using the standard control theory. However, each solution is limited to a specific case and not able to adapt to changes in the environments. This limitation can be overcome by learning from experience. This approach can be achieved using deep learning technique. More specifically, deep learning allows inferring complex behaviors from raw observation data. However, this approach has issues with samples efficiency. For real time application, deep learning requires the usage of on-board GPUs. In [58], a review of deep learning methods and applications for UAVs were presented.

* In the context of multi-UAV systems, the collision avoidance among UAVs is a very complicated task, a technique known as formation control can be used for obstacle avoidance. More specifically, cooperative formation control algorithms were developed as a collision-avoidance strategy for the multiple UAVs [315, 316]. A recent advancement in this area employs Model Predictive Controllers (MPC) in order to reduce computation time for the optimization of UAV's trajectory.

* The limited available power and payload of UAVs are challenging issues, which restrict on-board sensors requirements such as sensor weight, size and  required power. These sensors  such  as  IR, Ultrasonic, laser scanner, LADAR  and  RADAR  are  typically  heavy and large for sUAV [317].

* The high speed of UAVs is another challenge. With speed ranges between 35 and 70 Km/h, obstacle avoidance approach must be executed quickly to avoid the collision [317].

* The use of UAVs in an indoor environment is a challenging task and it needs higher requirements than outdoor environment. It is very difficult to use GPS for avoiding collisions in an indoor environment, usually indoor is a GPS denied environment. Moreover, RF signals cannot be used in this environment, RF signals could be reflected, and degraded by the indoor obstacles and walls.

* Vision-based collision avoidance methods using cameras suffer from heavy computational operations for image processing. Moreover, these cameras and optical sensors are light sensitive
(need sufficient lighting to work properly), so steam and smoke could cause the collision avoidance system to fail. Therefore, sensor based collision avoidance methods can be used to tackle these problems [318, 319].

* Some of the vision based collision avoidance approaches use a monocular camera with forward facing to generate collision free trajectory. In this method, all computations are performed off-board, with 30 frames per second which are sent in real time over Wi-Fi network to perform image processing at a remote base station. This makes UAV sensing and avoiding obstacles a challenging task [311]

§.§.§ Future Insights

Based on the reviewed literature of collision avoidance challenges, we suggest these possible future directions:

* UAVs could be integrated with vision sensors, laser range finder sensors, IR, and/or ultrasound sensors to avoid collisions in all directions [318].

* UAV control algorithms can be developed for autonomous hovering without collision, and to ensure the completion of their mission successfully.

* Standardization rules and regulations for UAVs are highly needed  around the world to regulate their operations, to reduce the likelihood of collision among UAVs, and to guarantee safe hovering [320, 321].

* Under the deep learning techniques with Model Predictive Controllers (MPC), further work can be done on developing methods to generate guiding samples for more superior obstacle avoidance. The main concern in the deploying deep learning models is the processing requirement. Therefore, hardware limitation for on-board processing should be taken into consideration.

* More studies are required to improve indoor and outdoor collision avoidance algorithms in order to compute smoother collision free paths, and to evaluate optimized trajectory in terms of aspects such as energy consumption [314].

* On-board processing is required for many UAV operations, such as dynamic sense and avoid algorithms, path re-planning algorithms and image processing. Design on-board powerful processor devices with low power consumption is an active area for future researches [58].

§.§ Networking Challenges

Fluid topology and rapid changes of links among UAVs are the main characteristics of multi-UAV networks or FANET [2]. A UAV in FANET is a node flying in the sky with 3D mobility and speed between 30 to 460 km/h. This high speed causes a rapid change in the link quality between a UAV and other nodes, which introduces different design challenges to the communications protocols. Therefore, FANET needs new communication protocols to fulfill the communication requirements of Multi-UAV systems [322].

§.§.§ FANET Challenges

* One of the challenging issues in FANET is to provide wireless communications for UAVs, (wireless communications between UAV-GCS, UAV-satellite, and UAV-UAV). More specifically, coordination, cooperation, routing and communication protocols for the UAVs are challenging tasks due to the frequent connections interruption, fluid network topology, and limited energy resources of UAVs. Therefore, FANET requires some special hardware and a new networking model to address these challenges
[323, 3].

* The UAV power constraints limit the computation, communication, and endurance capabilities of UAVs. Energy-aware deployment of UAVs with Low power and Lossy Networks (LLT) approach can be efficiently used to handle this challenge [8, 324].

* FANET is a challenging environment for resources management in view of the special characteristics of this network. The challenge is the complexity of network management, such as the configuration difficulties of the hardware components of this network [325]. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are useful approaches to tackle this challenge [326].

* Setting up an ad-hoc network among nodes of UAVs is a challenging task for FANET, due to the high node mobility, large distance between nodes, fluid network topology, link delays and high channel error rates. Therefore, transmitted data across these channels could be lost or delayed. Here the loss is usually caused by disconnections and rapid changes in links among UAVs. Delay-Tolerant Networking (DTN) architecture was introduced to be used in FANET to address this challenge [327, 328].

§.§.§ UAV New Networking Trends

§.§.§ $a$) Delay-Tolerant Networking (DTN)

The DTN architecture was designed to handle challenges facing the dynamic environments as in FANET.
In FANET, DTN approach based on store-carry-forward model can be utilized to tackle the long delay for packets delivery. In this model, a UAV can store, carry and forward messages from source to destination with long term data storage and forwarding functions in order to compensate intermittent connectivity of links [330, 331].

DTN can be used with a set of protocols operating at MAC, transport and application layers to provide reliable data transport functions, such as Bundle Protocol (BP) [332], Licklider Transmission Protocol (LTP) [333] and Consultative Committee for Space Data Systems (CCSD) File Delivery Protocol (CFDP) [334]. These protocols use store, carry and forward model, so UAV node keeps a copy for each sent packet until it receives acknowledgment from the next node to confirm that the packet has been received successfully.

BP and LTP are protocols developed to cope with FANET challenges and solve the performance problems for FANET.
The BP, forms a store, carry and forward overlay networks, to handle message transmissions, receptions and re-transmissions using a convergence layer protocol (CLP) services [332, 335] with the underlying transport protocols such as TCP-based [336], UDP-based [337] or LTP-based [333].
The CFDP was designed for file transfer from source to destination based on store, carry and forward approach for DTN paradigm. It can be run over either reliable or unreliable service mode using transport layer protocols (TCP or UDP) [338].

A routing strategy that combines DTN routing protocols in the sky and the existing Ad-hoc On-demand Distance Vector (AODV) on the ground for FANETs was proposed in [331]. In this work, they implement a DTN routing protocol on the top of traditional and unmodified AODV. They also use UAV to store, carry and forward the messages from source to destination.

§.§.§ $b$) Network Function Virtualization (VFV)

NFV is a new networking architecture concept for network softwarization and it is used to enable the networking infrastructure to be virtualized. More specifically, an important change in the network functions provisioning approach has been introduced using NFV, by leveraging existing IT virtualization technologies, therefore, network devices, hardware and underlying functions can be replaced with virtual appliances. NFV can provide programming capabilities in FANETs and reduces the network management complexity challenge  [339, 340].

The research in  [256], proposed drone-cell  management  framework (DMF) using UAVs act as aerial base stations with a multi-tier drone-cell network to complement the terrestrial cellular network based on NFV and SDN paradigms.
In [341], the authors proposed a video monitoring platform as a service (VMPaaS) using swarm of UAVs that form a FANET in rural areas. This platform utilizes the recent NFV and SDN paradigms.

Due to the complexity of the interconnections and controls in NFV, SDN can be consolidated with NFV as a useful approach to address this challenge.

§.§.§ $c$) Software-Defined Networking (SDN)

SDN is a promising network architecture, which provides a separation between control plane and data plane. It can also provide a centralized network programmability with global view to control network. Benefiting from the centralized controller in SDN, the original network switches could be replaced by uniform SDN switches [342]. Therefore, the deployment and management of new applications and services become much easier. Moreover, the network management, programmability and reconfiguration can be greatly simplified [343].

FANET can utilize SDN to address its environment's challenges and performance issues, such as dynamic and rapid topology changes, link intermittent between nodes; when UAV goes out of service due to coverage problems or for battery recharging. SDN also can help to address the complexity of network management[2].

OpenFlow is one of the most common SDN protocols, used to implement SDN architecture and it separates the network control and data planes functionalities.
OpenFlow switch consists of flow Table, OpenFlow Protocol and a Secure Channel [344]. UAVs in FANETs can carry OpenFlow Switches. The SDN control plane could be centralized (one centralized SDN controller), decentralized (the SDN controller is distributed over all UAV nodes), or hybrid in which the processing control of the forwarded packets can be performed locally on each UAV node and control traffic also exists between the centralized SDN controller and all other SDN elements [2].

The controller collects network statistics from OpenFlow switches, and it also needs to know the latest topology of the UAVs network. Therefore, it is important to maintain the connectivity of the SDN controller with the UAV nodes.
The centralized SDN controller has a global view of the network.
OpenFlow switches contain software flow tables and protocols to provide a communication between network and control planes, then the controller determines the path and tells network elements where to forward the packets [344].

Figure <ref> shows separation of control and data planes in SDN platform with OpenFlow interface.

Control and Data Planes in SDN Platform.

§.§.§ $d$) Low power and Lossy Networks (LLT)

In FANETs, UAV nodes are typically characterized by limited resources, such as battery, memory and computational resources. LLN composed of many embedded devices such as UAVs with limited power, and it can be considered as a promising approach to be used in FANET to handle power challenge in UAVs [324, 345].

The Internet Engineering Task Force (IETF) has defined routing protocol for LLN known as routing for low-power and lossy network (RPL) [324], this protocol can utilize different low power communication technologies, such as low power WiFi, IEEE 802.15.4 [346], which is a standard for low-power and low rate wireless communications and can be used for UAV-UAV communications in FANET [347]. The IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) defines a set of protocols that can be used to integrate nodes with IPv6 datagrams over constrained networks such as FANET [348].

§.§.§ Future Insights

Based on the reviewed literature of the networking challenges and new networking trends of FANET, we suggest these future insights:

* SDN services can be developed to support FANET functions, such as surveillance, safety and security services.

* Further studies are needed to study the SDN deployment in FANET with high reliability, reachability and fast mobility of UAV nodes [349]

* More research is needed to develope specific security protocols for FANETs based on the SDN paradigm.

* More studies are needed to replace the conventional network devices by fully decoupled SDN switching devices, in which the control plane is completely decoupled from data plane and the routing protocols can be
executed on-board from SDN switching devices [349].

* For NFV, more research is needed on optimal virtual network topology, customized end-to-end protocols and dynamic network embedding [343].

* New routing protocols need to be tailored to FANETs to conserve energy, satisfy bandwidth requirement, and ensure the quality of service [323].

* Most of the existing MANET routing protocols partially fail to provide reliable communications among UAVs. So, there is a need to design and implement new routing protocols and networking models for FANETs [7].

* FANETs share the same wireless communications bands with other applications such as satellite communications and GSM networks. This leads to frequency congestion problems.  Therefore, there is a need to standardize FANET communication bands to mitigate this problem [2].

* FANETs lack security support. Each UAV in FANET is required to exchange messages and routing information through wireless links. Therefore, FANETs are vulnerable to attacks. As a result, it is important to design and implement secure FANET routing protocols.

* The design of congestion control algorithms become an important issue for FANETs. Current research efforts focus on modifying and improving  protocols instead of developing new transport protocols that better suite  FANETs.  [350].

§.§ Security Challenges

Figure <ref> illustrates the general architecture of UAV systems [351], which includes: (1) UAV units; (2) GCSs; (3) satellite (if necessary); and (4) communications links. Various components of the UAV systems provide a large attack surface for malicious intruders which brings huge cyber security challenges to the UAV systems. In this section, to present a comprehensive view of the cyber security challenges of UAV systems, we first summarize the attack vectors of general UAV systems. Then, based on the attack vectors and the potential capabilities of attackers, we classify the cyber attacks/challenges against UAV systems into different categories. More importantly, we present a comprehensive literature review of the state-of-the-art countermeasures for these security challenges. Finally, based on the reviewed literature, we summarize the security challenges and provide high level insights on how to approach them.

§.§.§ Attack Vectors in UAV Systems

Based on the architecture illustrated in Figure <ref>, we identify four attack vectors:

* Communications links: various attacks could be applied to the communications links between different entities of UAV systems, such as eavesdropping and session hijacking.

* UAVs themselves: direct attacks on UAVs can cause serious damage to the system. Examples of  such attacks are signal spoofing and identity hacking.

* GCSs: attacks on GCSs are more fatal than others because they issue commands to actual devices and collect all the data from the UAVs they control. Such attacks normally involve malwares, viruses and/or key loggers.

* Humans: indirect attacks on human operators can force wrong/malicious operating commands to be issued to the system. This type of attacks is usually enabled with social engineering techniques.

§.§.§ Taxonomy of Cyber Security Attacks/Challenges Against UAV Systems

Based on the attack vectors and capabilities of attackers, as well as the work in [351], we classify the possible attacks against UAV systems into different categories. Figure <ref> presents a graph that summarizes the proposed attack taxonomy. This attack model defines three general cyber security challenges for the UAV systems, namely, confidentiality challenges, integrity challenges, and availability challenges.

Confidentiality refers to protecting information from being accessed by unauthorized parties. In other words, only the people who are authorized to do so can gain access to data. Attackers could compromise the confidentiality of UAV systems by various approaches (e.g., malware, hijacking, social engineering, etc.) utilizing different attack vector.

Integrity ensures the authenticity of information. Attackers could modify or fabricate information of UAV systems (e.g., data collected, commands issued, etc.) through communications links, GCSs or compromised UAVs. For example, GPS signal spoofing attack.

Availability ensures that the services (and the relevant data) that UAV systems carry are running as expected and are accessible to authorized users. Attackers could perform DoS (Deny of Service) attacks on UAV systems by, for example, flooding the communications links, overloading the processing units, or depleting the batteries.

General Architecture of UAV Systems. Reprinted from [351].

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parent anchor=south,
child anchor=north,

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[ Cyber security challenges of UAV applications
malware, etc.]
[  Hijacking,
[Comms. links
Hijacking, etc.]
[Buffer overflow,
flooding, etc.]
[Fabrication Modification
[Signal spoofing,
MitM, etc.]

Cyber Attack/Challenges Taxonomy in UAV Systems.

§.§.§ Literature Review of the State-of-the-art Security Attacks/Challenges and Countermeasures

The UAV related cyber security research can be classified into three main categories as shown in Figure <ref>, namely:

* A: Specific attack discussion: This line of research focuses on one specific type of attack (e.g., signal spoofing) and proposes corresponding analysis or countermeasures.

* B: General security analysis: This line of research presents the high level analysis, discussion or modeling of various attacks that exist in current UAV systems.

* C: Security framework development: This line of research introduces new monitoring systems, simulation test beds or anomaly detection frameworks for the state-of-the-art UAV applications.

Table <ref> summarizes the 15 relevant papers that we have reviewed including their attack vectors and categories. In addition, for the papers that discuss specific attacks/challenges, we summarize the proposed countermeasures, their limitations and propose high level countermeasures as guidelines for future enhancements.

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[  Security works
[A: Specific attacks
[B: Security framework
[B-1: Monitoring
[B-2: Simulation
test beds
[B-3:  Anomaly
detection frameworks
[C: General security

Summary of the Literature of the State-of-the-Art Attacks/Challenges and Countermeasures in UAV systems.

The summary of the literature of state-of-the-art attacks and countermeasures for UAV

1|c|       1c|Year     1c|Attack Vector     1c|Category                                                          1c|c]@c@Proposed
Countermeasure                                                              1c|Limitations                                                                                1c|c]@c@Suggested
[351]              2012                                   Communication Links, UAV, GCSs                  C                                                                                N/A                                                                                N/A                                                                                N/A
[352]             2013                                   Communication Links                             c]@l@A: Signal integrity
(GPS spoofing)                   c]@l@1. Cryptography based
signal authentication;
2. Multiple receiver design.                                        c]@l@1. Lack of details;
2. Require hardware changes
(i.e., complexity and cost issue).                                        c]@l@1. Strong authentication
(e.g., trust platform module,
Kerberos, etc.);
2. Signal distortion detection;
3. Direction-of-arrival sensing
(i.e.,  transmitter antenna
direction detection).
[353]             2013                                   Communication Links, UAV, GCSs                  B-1                                                                                N/A                                                                                N/A                                                                                N/A
[354]          2014                                   Communication Links, UAV, GCSs                  C                                                                                N/A                                                                                N/A                                                                                N/A
[355]               2015                                   Communication Links, UAVs                       c]@l@A: Routing attack
(eavesdropping, DOS, etc.)          c]@l@A secure routing protocol
and its modeling process.                                                                c]@l@Lack of evaluations
or security proof.                                                                                c]@l@Perform evaluations under
various attack scenarios
or provide theoretical
security proof.
[356]             2015                                   UAVs                                            B-2                                                                                N/A                                                                                N/A                                                                                N/A
[357]         2015                                   UAVs                                            B-1                                                                                N/A
[358]               2015                                   Communication Links, UAVs                       A: DDoS                                                                                c]@l@Botnet platform
(as a future work).                                                                               c]@l@There is no concrete
solution provided.                                                                                c]@l@1. Delivers behavior-based
anomaly detection
(e.g., packet analysis about
its type, rate, volume,
source IP, etc.);
2. Enables immediate
response to DDoS attacks;
[189]     2016                                   Communication Links, UAV, GCSs                  C                                                                                N/A                                                                                N/A                                                                                N/A
[359]         2016                                   Communication Links, UAV, GCSs                  B-3                                                                                N/A                                                                                N/A                                                                                N/A
[360]      2016                                   Communication Links, UAVs                       c]@l@A: Signal integrity
(sensor input spoofing)          c]@l@Optical flow analysis
(RANSAC algorithm
[361])                                             c]@l@The attack model is too
demanding (i.e., it is difficult
to perform this attack. ).                                      c]@l@1. Strong firewall/authentication
schemes to prevent hijacking attacks;
2. Delivers behavior-based
anomaly detection.
[362]         2016                                   UAVs                                            A: UAV hijacking                                                                                c]@l@Profiling of in-flight
statistics.                                                                                c]@l@Temporary control instability
(e.g., short time decreasing of
amplitude) caused by
the attacker cannot be detected.     c]@l@1. Decreasing the detection
2. Strong firewall/authentication
schemes to prevent hijacking attacks;
3. Packets analysis of malicious
injected commands.
[363]         2016                                   N/A                                             B-2                                                                                N/A                                                                                N/A                                                                                N/A
[364]          2016                                   Communication Links                             c]@l@A: Hijacking
(MitM attack)                           c]@l@1. XBee 868LP on-board
2. Dedicated hardware
3. Application layer
encryption.     No details provided.                                                                                c]@l@1. Integrate detection and immediate
response mechanisms.
2. Strong authentication
(e.g., trust platform module, Kerberos, etc.);
[365]       2017                                   Communication Links                             c]@l@A: Authentication/key
management targeted attacks     c]@l@A set of modified
authentication and key
management protocols.                                                  c]@l@Lack of evaluations
or security proof.                                                                                c]@l@Perform evaluations under
various attack scenarios
or provide theoretical
security proof.
[366]      2017                                   Communication Links, GCS, UAV                   C                                                                                N/A                                                                                N/A                                                                                N/A

* Challenge of specific attack - GPS spoofing: GPS spoofing attack refers to the malicious attempt to manipulate the GPS signals in order to achieve the benefits of the attackers.

In [352], the authors provide an analysis of UAV hijacking attack (e.g., GPS signal spoofing) with an anatomical approach and outline a model to illustrate: (1) that such attacks can be observed to reveal their vulnerabilities; (2) how to exploit such attacks; (3) how to provide countermeasures and risk mitigation; and (4) details of the impact of such attacks. The use of anatomical investigation of a UAV hijacking aims to provide insights that help all the practitioners of the UAV systems. The article mainly focuses on the analysis of GPS signal spoofing attacks. However, the proposed countermeasures (i.e., cryptography based signal authentication and multiple receiver design) are not novel and are only described at high level without sufficient details.

* Challenge of specific attack - DDoS (Distributed Deny of Service) attack: DDoS attack is an attempt to make the UAV systems unavailable/unreachable by overwhelming it with traffic from multiple sources.

In [358], the authors present a platform to conduct a DDoS attack against UAV systems and layout the general mitigation method. Firstly, the authors present a DDoS attack modeling and specification. Then, they analyze the effects caused by this kind of attack. However, the proposed attack scenario is too simple (i.e., UDP flooding) and only a high level solution is said to be included in the future work.

* Challenge of specific attack - sensor input spoofing attack: In [360], the authors introduce a new attack against UAV systems, named sensor input spoofing attack. If the attacker knows exactly how the sensor algorithms work, he can manipulate the victim's environment to create a new control channel such that the entire UAV system is in dangerous. In addition, they provide suitable mitigation for the introduced attack. To perform the proposed sensor input spoofing attack, the attacker must meet three requirements: (1) environment influence requirement: the attacker must be able to modify the physical phenomenon that the sensor measures; (2) plausible input requirement: the attacker must be able to generate valid input that the sensor system can read; and (3) meaningful response requirement: the attacker must be able to generate meaningful UAV behavior corresponding to the spoofed input. This attack model is too demanding to be practical for many UAV applications and  no convincing justifications were provided.

* Challenge of specific attack - hijacking attacks: hijacking attack is a type of attack in which the attacker takes control of a communication between two parties and masquerades as one of them (e.g., inject extra commands).

In [364],  the authors demonstrate the capabilities of the attackers who target UAV systems to perform Man-in-the-Middle attacks (MitM) and control command injection attacks. They also propose corresponding countermeasures to help address these vulnerabilities. However, the introduced MitM attack requires fully compromised UAV system via reverse-engineering techniques which largely limits the feasibility/practicability of this type of attacks. In addition, the proposed encryption scheme lacks the necessary details that warrant credible evaluation of robustness and security.

The article of [362] proposes an anomaly detection scheme based on flight patterns fingerprint via statistical measurement of flight data. Firstly, a baseline flight profile is generated. Then, simulated hijacking scenarios are compared to the baseline profile to determine the detection result. The proposed scheme is able to detect all direct hijacking scenarios (i.e., assume a fully compromised UAV and the flight plan has been altered to some random places.). However, temporary control  instability (e.g., short time decreasing of amplitude) caused by the attacker cannot be detected.

* Challenge of specific attack - Authentication/key management targeted attacks: The authors in [365]  first propose a communication architecture to integrate LTE technology into integrated CNPC (Control and Non-Payload Communication) networks. Then, they define several security requirements of the proposed architecture. Also, they modify the authentication, the key agreement and the handover key management protocols to make them suitable to the integrated architecture. The proposed modified protocol is proved to outperform the LTE counterpart protocols via a comparative analysis. In addition, it introduces almost the same amount of communications overhead.

* Challenge of security framework development: In [353], the authors introduce UAVSim, a simulation testbed for Unmanned Aerial Vehicle Networks cyber security analysis. The proposed test bed can be used to perform various experiments by adjusting different parameters of the networks, hosts and attacks. The UAVSim consists of five modules: (1) Attack library: generating various attacks (i.e., jamming attacks and DoS attacks); (2) UAV network module: forming different UAV networks; (3) UAV model library: providing different host functions (e.g., attack hosts or UAV hosts); (4) Graphical user interface; (5) Result analysis module; and (6) UAV Module browser. The proposed test bed is helpful in analyzing existing attacks against UAV systems by adjusting different parameters. However, the proposed attack library only contains two types of attacks for now (i.e., jamming attacks and DoS attacks). Therefore, UAVSim must be enriched with other types of attacks to improve its applicability and usability.

The authors in [356] present R2U2, a novel framework for run-time monitoring of security threats and system behaviors for Unmanned Aerial Systems (UAS). Specifically, they extend previous R2U2 from only monitoring of hardware components to both hardware and software configuration monitoring to achieve security threats diagnostic. Also, the extended version of R2U2 provides detection of attack patterns (i.e., ill-formatted and illegal commands, dangerous commands, nonsensical or repeated navigation commands and transients in GPS signals) rather than component failures. In addition, the FPGA implementation ensures independent monitoring and achieves software re-configurable feature. The extended version of R2U2 is more advanced than the original prototype in terms of attack pattern recognition and secure & independent monitoring. However, the independent monitoring system introduces a new security hole (i.e., the monitoring system itself) without introducing appropriate security mechanism to mitigate it.

In the work of [357], the authors present a UAV monitoring system that captures flight data to perform real-time abnormal behavior detection. If an abnormal behavior is detected, the system will raise an alert. The proposed monitoring system includes the following features:

* A specification language for UAV in-flight behavior modeling;

* An algorithm to covert a given flight plan into a behavioral flight profile;

* A decision algorithm to determine if an actual UAV flight behavior is normal or not based on the behavioral flight profile;

* A visualization system for the operator to monitor multiple UAVs.

The proposed monitoring system can detect abnormal behaviors based on the in flight data. However, attacks which do not alter in-flight data are not detectable (e.g., attacks that only collect flight data).

The authors of [359] propose and implement a cyber security system to protect UAVs from several dangerous attacks, such as attacks that target data integrity and network availability. The detection of data integrity attacks uses Mahalanobis distance method to recognize the malicious UAV that forwards erroneous data to the base station. For the availability attacks (i.e., wormhole attack in this paper), the detection process is summarized as follows:

* The UAV relays a packet when it becomes within the range of the base station. The packet includes: (1) node's type (source, relay or destination); and (2) its next (and previous) hops.

* The base station collects the forwarded packets from the UAVs, verifies whether the relay node forwards a packet or not and computes the Message Dropping Rate (MDR).

* The base station will raise an alert if the MDR is higher than a false MDR assigned to a normal UAV.

The proposed detection scheme for data integrity attacks and network availability attacks is proved to achieve high detection accuracy via simulations. The attack model assumes that every UAV node has the detection module and the node which is determined as malicious node would lose the ability to invoke the detection module. However, there is no co-operation detection algorithm provided and only single node detection procedure is introduced.

In the work of [363], the authors present an improved mechanism for UAV security testing. The proposed approach uses a behavioral model, an attack model, and a mitigation model to build a security test suite. The proposed security testing goes as follows:

* Model system behavior: associates behavior criteria (BC) with a behavioral model (BM) and build a behavioral test (BT).

* Attack type definition: defines attack types (A) with attack criteria (AC) and determine attack applicability matrix (AM).

* Determine security test requirements: determines at which point during the operation of a behavioral test an attack will occur.

* Test generation: a required mitigation process is injected.

This work provides a systematic approach to identify vulnerabilities in UAV systems and provides corresponding mitigation. However, the proposed mitigation methods are limited to state roll back or re-executing. In other words, other prevention or detection methods (e.g., encryption or anomaly detection) cannot be included in the proposed scheme.

* Challenges of security analysis works: In the work of [351], the authors perform an overall security threat analysis of a UAV system as described in Figure <ref>.  A cyber security threat model is proposed and analyzed to show existing or possible attacks against UAV systems. This model helps designers and users of UAV systems across different aspects, including: (1) understanding threat profiles of different UAV systems; (2) addressing various system vulnerabilities; (3) identifying high priority threats; and (4) selecting appropriate mitigation techniques. In addition, a risk evaluation mechanism (i.e., a standard risk evaluation grid included in the ETSI threat assessment methodology ) is used to assess the risk of different threats in UAV systems. Even though this paper provides a comprehensive profile of existing or potential attacks for UAV systems, the proposed attacks are general/high level threats that did not take into consideration hardware and software differences among different UAV systems.

The survey paper of [189] reviews various aspects of drones (UAVs) in future smart cities, relating to cyber security, privacy, and public safety. In addition, it also provides representative results on cyber attacks using UAVs. The studied cyber attacks include de-authentication attack and GPS spoofing attack. However, no countermeasures are discussed.

The authors in [366] introduce an implementation of an encrypted Radio Control (RC) link that can be used with a number of popular RC transmitters. The proposed design uses Galois Embedded Crypto library together with openLRSng open-source radio project. The key exchange algorithm is described below:

* $TX$ first generates the ephemeral key $K_e$ and , then encrypts $K_e$ using permanent key $K_p$ and $IV_{rand}$. The encrypted result is $m_1^{TX}$ and sent to $RX$.

* $RX$ decrypts $m_1^{TX}$ with $K_p$ to obtain $K_e$, then generate $K_e^{'}$ and encrypts $K_e^{'}$ with $K_e$ and $IV_0=0$. The encrypted result is $m_2^{RX}$ and sent to $TX$.

* $TX$ decrypts $m_2^{RX}$ with $K_e$ and $IV_0=0$ to verify that $RX$ has a copy of $K_e$. Then $TX$ sends $ACK$ message encrypted with $K_e^{'}$ and $IV_0$ to $RX$ as a confirmation (denoted as $m_3^{TX}$ ).

* $RX$ receives $m_3^{TX}$it to verify that $TX$ has a copy of $K_e^{'}$.  Key exchange is then successful.

The proposed scheme achieves secure communication link for open source UAV systems. However, the symmetric key is assumed to be generated in a third-party trusted computer and hard-coded in the source code, which degrades the security guarantees and limits the feasibility of the scheme.

§.§.§ Summarizing of Cyber Security Challenges for UAV Systems

Based on the reviewed literature, we summarize four key findings of the cyber security challenges on UAV applications:

* DoS attacks and hijacking attacks (i.e., for signal spoofing) are the most prevailing threats in the UAV systems: Various DoS attacks have been found and proved to cause serious availability issues in UAV systems. In addition, signal spoofing via hijacking attacks is able to severely damage the behaviors of certain UAV systems.

Possible solution: (1) Strong authentication (e.g., trust platform module, Kerberos, etc.); (2) Signal distortion detection; and (3) Direction-of-arrival sensing (i.e., transmitter antenna direction detection).

* Existing countermeasure algorithms are limited to single UAV systems: most of the existing schemes are designed only for single UAV systems. A few of them discuss multiple UAV scenarios without providing any concrete solutions.

Possible solution: develop co-operation countermeasure algorithms for multiple UAVs. For example, modify and adopt existing distributed security frameworks (e.g., Kerberos) to multiple UAVs systems; (2)

* Most of the current security analysis of UAV systems overlook the hardware/software differences (e.g., different hardware platform, different communication protocols, etc.) among various UAV systems: On one hand, some of the attacks only exist in specific hardware or software configuration. On the other hand, most of the proposed countermeasures are well studied solutions in other communication systems and there could be many deployment difficulties when applying them on different UAV systems.

Possible solution: design unified/standard deployment interface or language for the diverse UAV systems.

* Current UAV simulation test beds are still far from mature: Existing emulators for UAV security analysis are limited to few attack scenarios and specific hardware/software configurations.

Possible solution: leveraging powerful simulation tools (e.g., Labview) or design customized simulation environments.

§ CONCLUSION

The use of UAVs has become ubiquitous in many civil applications. From rush hour delivery services to scanning inaccessible areas, UAVs are proving to be critical in situations where humans are unable to reach or cannot perform dangerous/risky tasks in a timely and efficient manner. In this survey, we review UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses.

In SAR operations, UAVs can provide timely disaster warnings and assist in speeding up rescue and recovery operations. They can also carry medical supplies to areas that are classified as inaccessible. Moreover, they can quickly provide coverage of a large area without ever risking the security or safety of the personnel involved. Using UAVs in SAR operations reduces costs and human lives.

In remote sensing, UAVs equipped with sensors can be used as an aerial sensor network for environmental monitoring and disaster management. They can provide numerous datasets to support research teams, serving a broad range of applications such as drought monitoring, water quality monitoring, tree species, disease detection, etc. In risk management, insurance companies can utilize UAVs to generate NDVI maps in order to have an overview of the hail damage to crops, for instance.

Civil infrastructure is expected to dominate the addressable market value of UAV that is forecast to reach $\$45$ Billion in the next few years. In construction and infrastructure inspection applications, UAVs can be used to monitor real-time  construction project sites. They can also be utilized in power line and gas pipeline inspections. Using UAVs in civil infrastructure applications can reduce work-injuries, high inspection costs and time involved with conventional inspection methods.

In agriculture, UAVs can be efficiently used in irrigation scheduling, plant disease detection, soil texture mapping, residue cover and tillage mapping, field tile mapping, crop maturity mapping and crop yield mapping. The next generation of UAV sensors can provide on-board image processing and in-field analytic capabilities, which can give farmers instant insights in the field, without the need for cellular connectivity and cloud connection.

With the rapid demise of snail mail and the massive growth of e-Commerce, postal companies have been forced to find new methods to expand beyond their traditional mail delivery business models. Different postal companies have undertaken various UAV trials to test the feasibility and profitability of UAV delivery services. To make UAV delivery practical, more research is required on UAVs design. UAVs design should cover creating aerial vehicles that can be used in a wide range of conditions and whose capability rivals that of commercial airliners.

UAVs have been considered as a novel traffic monitoring technology to collect information about real-time road traffic conditions. Compared to the traditional monitoring devices, UAVs are cost-effective and can monitor large continuous road segments or focus on a specific road segment. However, UAVs have slower speeds compared to vehicles driving on highways. A possible solution might entail changing the regulations to allow UAVs to fly at higher altitudes. Such regulations would allow UAVs to benefit from high views to compensate the limitation in their speed.

One potential benefit of UAVs is the capability to fill the gaps in current border surveillance by improving coverage along remote sections of borders. Multi-UAV cooperation brings more benefits than single UAV surveillance, such as wider surveillance scope, higher error tolerance, and faster task completion time. However, multi-UAV surveillance requires more advanced data collection, sharing, and processing algorithms. To develop more efficient and accurate multi-UAV cooperation algorithms, advanced machine learning algorithms could be utilized to achieve better performance and faster response.

The use of UAVs is rapidly growing in a wide range of wireless networking applications. UAVs can be used to provide wireless coverage during emergency cases where each UAV serves as an aerial wireless base station when the cellular network goes down. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates. Utilizing UAVs in wireless networks needs further research, where topology formation, cooperation between UAVs in a multi-UAV network, energy constraints, and mobility modelس are challenges facing UAVs in wireless networks.

Throughout the survey, we discuss the new technology trends in UAV applications such as mmWave, SDN, NFV, cloud computing and image processing. We thoroughly identify the key challenges for UAV civil applications such as charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. In conclusion, a complete legal framework and institutions regulating the civil uses of UAVs are needed to spread UAV services globally. We hope that the key research challenges and opportunities described in this survey will help pave the way for researchers to improve UAV civil applications in the future.

§ ACRONYMS

The acronyms and abbreviations used throughout the survey and their
definitions are listed bellow:

6LoWPAN	IPv6 over Low Power Wireless Personal Area Networks.

AC	Attack Criteria.

AODV	Ad-hoc On-demand Distance Vector.

ATG	Air-to-Ground.

BC	Behavior Criteria.

BDMA	Beam Division Multiple Access.

BM	Behavioral Model.

BP	Bundle Protocol.

BT	Behavioral Test.

CC	Capacitive Coupling.

CCSD	Consultative Committee for Space Data Systems.

CFDP	CCSD File Delivery Protocol.

CLP	Convergence Layer Protocol.

CNN	Convolutional Neural Network.

CNO	Cellular Network Operator.

CNPC	Control and Non-Payload Communication.

CoA	Certificate of Authorization.

DDoS	Distributed Deny of Service.

DMF	Drone-cell  management  frame-work.

DoS	 Deny of Service.

DTN	Disruption Tolerant Networking.

EVI   Enhanced Vegetation Index.

FAA	Federal Aviation Administration.

FANETs  Flying Ad-Hoc Networks.

FLIR	Forward Looking Infrared.

FMI	Fourier Mellin Invariant.

FSO	Free Space Optical.

GCS	Ground Control Station.

GIS	Geographic Information System.

GPS	Global Position System.

GNDVI   Green Normalized Difference Vegetation index.

GVI     Green Vegetation Index.

HAP	High Altitude Platform.

IETF	Internet Engineering Task Force.

IMU	Inertial Sensor.

IoT	Internet of Things.

IR	Infra Red.

ITU	International Telecommunication Union.

KDOT	Kansas Department of Transportation.

LAP	Low Altitude Platform.

LLT	Low power and Lossy Networks.

LOS	Line of Sight.

LRF	Laser Range Finder.

LTP	Licklider Transmission Protocol.

MAC	Medium Access Control.

MANETs	Mobile Ad-hoc Networks.

MB-LBP	Multi-scale Block Local Binary Patterns.

MDR	Message Dropping Rate.

MEC	Mobile Edge Computing.

MitM	Man-in-the-Middle attacks.

mmWave	Millimeter-Wave.

MPC	Model Predictive Controller.

MRC	Magnetic Resonance Coupling.

NDVI	Normalized difference vegetation index.

NLOS	Non-Line of Sight.

OBIA      Object-Based Image Analysis.

OD	Origin-Destination.

PA	Precision Agriculture.

PBR	Performance-Based Regulations.

PCA	Point of closest Approach.

PG&E	Pacific Gas and Electric Company.
PUSCH	Physical Uplink Shared Channel.

PVI   Perpendicular Vegetation Index.

RC	Radio Control.

RPL	Routing for low-power and Lossy network.

RSU	Roadside Unit.

SAC	Special Airworthiness Certificate.

SAR	Search and Rescue.

SAVI  Soil Adjusted Vegetation Index.

SDN	Software-Defined Networking.

SHM   Structural Health Monitoring.

SOC	Start of Charge.

SUAVs	Solar-Powered UAVs.

sUAV	Small Unmanned Aerial Vehicle.

SVM	Support Vector Machine.

TIR    Thermal Infrared.

UAS	Unmanned Aircraft Systems.

UAV	Unmanned Aerial Vehicle.

VANETs	Vehicular Ad-hoc Networks.

NFV	Network Function Virtualization.

VIs	Vegetation Indices.

VMPaaS	Video Monitoring Platform as a Service.

VTOL	Vertical Take-Off and Landing.

WPT	Wireless Power Transfer.

WSN	Wireless Sensor Networks.

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