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공공안전융합기술 워크샵

드론을 활용한 이동통신 네트워크 기술

2018. 03. 08Jae-Hyun [email protected]

Wireless Internet aNd Network Engineering Research Lab.http://winner.ajou.ac.kr

School of Electrical and Computer Engineering Ajou University, Korea

Contents

Introduction

UAV Channel Model

UAV deployment

Resource Management

Security

Conclusion

2

1

2

3

4

5

6

Introduction

3

Introduction

UAV (Unmanned Aerial Vehicles)Definition [1] Aerial vehicles that do not carry a human operator can fly autonomously or

be piloted remotely

Different types of aerial objects/systems [2] Include HAP(High Altitude Platform), LAP(Low Altitude Platform),

drones(ex. quadcopter), Balloons, etc• HAP : 15 Km(altitude), 38 – 39.5 GHz (Frequency band - Global) • LAP : between 200 m to 6 km

[1] Joint Publication 1-02, “DOD Dictionary of Military and Associated Terms.”[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017[3] Airbus, “Zephyr, High Altitude Pseudo-Satellite”[4] Google, “Loon Project”, https://x.company/projects/loon/ 4

<HAP(Zephyr)> <LAP(Predator)> <Balloon(Loon Project)><Drone>

Introduction

UAV applicationPwC Poland market assessment, June 2017 [5] Predicted value of drone powered solutions

in key industries

5

※ PwC(PricewaterhouseCoopers)- An accounting firm that provides tax, HR, transactions, performance improvement, and crisis management services

[5] PwC, https://www.digitalpulse.pwc.com.au/pwc-infographic-drones-as-data-service/

Introduction

UAV application (Representative cases)Commercial [6-8] Transport : Drone Taxi(EHANG 184)

• EHANG(China, 2018)

Security : A.I. Home Robot(AEVENA Aire)

• Aevena(USA, 2017)

Monitoring : Telecom Industry• Airware(USA)

6

Military [9-11] Strike

• Enemy attack (using Hellfire missile)

UAV Swarm• Perdix-micro UAV swarm• LOCOST program

Nano Drone• Black Hornet PD-100 PRS

» Prox Dynamics(Norway, 2012)

[6] EHANG, http://www.ehang.com/ehang184[7] AEVENA, http://www.aevena.com/[8] Airware, https://www.airware.com/en/

[9] Office of Naval Research, https://www.onr.navy.mil[10] U.S. Departure of Defense, https://www.defense.gov[11] Prox Dynamics, http://www.proxdynamics.com/products/pd-100-black-hornet-prs

http://www.ehang.com/ehang184

http://www.aevena.com/

https://www.airware.com/en/

https://www.onr.navy.mil/

https://www.defense.gov/

Introduction

UAV-based wireless communication Functions and services for design of

UAV-based networking system [12] Communication relay nodes

• Connect disconnected MANET clusters» PS-LTE [13]

(KT-2017, SKT-2017)

Communication among UAVs• Extend transmission range

» UAV Swarm [9-10](DoD, MIT: Perdix-micro UAV-2016, ONR: LOCOST program-2015, Intel: PyeongChang W/O, 2018)

[9] ‘Office of Naval Research’, https://www.onr.navy.mil[10] ‘U.S. Departure of Defense,’ https://www.defense.gov [12] I. Jawahr et. al., “Communication and networking of UAV-based Systems: Classification and associated architectures,” Journal of Network and Computer

Application, vol. 84, pp. 93-108, Apr. 2017[13] SKT, http://www.sktelecom.co.kr/advertise/press_detail.do?idx=4190

7

<SKT: PS-LTE>

<DoD, MIT: Perdix-micro UAV> <Intel>※ DoD : Department of Defense※ ONR : Office of Naval Research

https://www.onr.navy.mil/

http://www.sktelecom.co.kr/advertise/press_detail.do?idx=4190

Introduction

UAV-based wireless communication Functions and services for design of

UAV-based networking system [12] Network gateway

• Provide connectivity to backbone networks,communication infrastructure, or the Internet» Mobile Base Station [14, 15]

(KT-2015, Verizon: Flying cell site LTE Drone-2016,AT&T: Flying COWs-2017, NTT DoCoMo-2017)

Data storage or processing• Collecting data and data storage equipment

» UAV-based IoT platform [16](IBM, Aerialtronics-2016)

8

<KT> <AT&T: Flying Cow>

<IBM, Aerialtronics>[12] I. Jawahr et. al., “Communication and networking of UAV-based Systems: Classification and associated architectures,” Journal of Network and Computer

Application, vol. 84, pp. 93-108, Apr. 2017[14] AT&T, “When COWs Fly: AT&T sending LTE Signals from drone,” http://about.att.com/innovationblog/cows_fly[15] Verizon, http://www.verizon.com/about/news/first-responders-make-calls-and-send-text-messages-using-flying-cell-site[16] IBM Watson, https://www.ibm.com/watson/

http://www.verizon.com/about/news/first-responders-make-calls-and-send-text-messages-using-flying-cell-site

Introduction

Advantages [2]Rapid, Flexible and scalable deploymentCoverage expansion Low-cost operation

Challenges of UAV-based wireless communication [17] Ensuring reliable network connectivityUAV deployment and operation

mechanism Effective resource management and

security mechanism

9[2] W. Saad, “Wireless communications and networking with unmanned aerial vehicles,” in proc. MILCOM 2017, Baltimore, MD, USA, Oct, 2017[17] Y. Zeng, R. Zhang, T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communication Magazine, vol. 54,

no. 5, pp. 36 – 42, May. 2016.

UAV Channel Model

10

RF propagation model designPropagation type LoS (Line-of-sight), NLoS (Non-Line of Sight) links MPC (Multi-Path Component)

• Reflection, scattering, diffraction by mountains, ground surface, foliage, man-made structures etc.

Main consideration LoS probability

11

UAV Channel Model (Air-to-Ground)

[18] “Propagation data and prediction methods for the design of terrestrial broadband millimetric radio access systems,” Geneva, Switzerland, REC.P.1410-2, 2003.

The height of the ray at the obstruction point

( )los tx rxlos tx

rx

r h hh hr−

= −

The probability that a LoS ray exist

1

(LoS) (building_height < )rb

losb

P P h=

=∏: number of buildings crossed rb

Example of RF propagation modelover urban environmentRadio signals emitted by a LAP Excessive pathloss [dB] (Do not consider small-scale fluctuations)

Spatial expectation of the pathloss

12

LoS

NLoS


Excessive path loss samples histogram(dense urban environment)

[19] A. A. Hourani, et al., “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol 3, no 6, pp. 569 – 572, Dec. 2014.[20] A. A. Hourani, et al., “Modeling Air-to-Ground path loss for low altitude platforms in urban environments,” in proc. Globecom 2014, Austin, TX, USA, Dec. 2014.

PL : ATG mean pathloss ξ : propagation group (LoS, NLoS) FSPL : free space pathloss

PL - FSPLξ ξη =

θ : elevation angle P(ξ, θ) : probability of occurrence of a certain propagation group

( ) LoS NLoS= PL , (LoS) PL (NLoS) PLP P Pξξ

ξ θΛ = × + ×∑

( ) ( )NLoS, 1 LoS,P Pθ θ= −


Example of RF propagation modelover urban environmentModeling LoS probability Depend on environment, height and density of

buildings• α : the ratio of built-up land area to the total

land area (dimensionless)• β : mean # of buildings per unit area • γ : building’s heights distribution

» according to Rayleigh distribution

LoS probability approximation

13

(a) (b)α 0.25 0.25β 4 1

1(LoS, )1 exp( [ ])

Pa b a

θθ

=+ − −

, 0tanrx RX

hr hθ

= →

Closed sigmoid form

a, b : S-curve parameters

2

20

( 0.5)( )1

(LoS) 1 exp2

m

n

TX RXTX

n h hhm

Pγ=

+ − − + = − −

∏

𝑚𝑚 = floor(𝑟𝑟𝑟𝑟𝑟𝑟 𝛼𝛼𝛼𝛼 − 1)

[19] A. A. Hourani, et al., “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol 3, no 6, pp. 569 – 572, Dec. 2014.

Small buildings (a) Large building (b)


Example of RF propagation model over different urban environmentCell radius vs. Altitude ※ PLmax : Maximum allowed pathloss

14[19] A. A. Hourani, et al., “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol 3, no 6, pp. 569 – 572, Dec. 2014.

For the four urban environmentin fixed PLmax (10 dB) for 2GHz

For the urban environmentin variable PLmax


Shadowing and Rician fading model Shadowing model for HAP in built-up areas Additional shadowing loss

• The shadowing effects of buildings on NLoS Connections

Rician fading model Small scale fading

• A Strong LoS Component

K-factor• NASA measured in a near-urban environment for CNPC link

» C-band(5.06 GHz) : Avg. 27dB (Min. 12.3dB)» L-band (968 MHz) : Avg. 12.7dB (Min. 5dB)

7[21] D. W. Matolak, R. Sun, “Air-Ground channel characterization for unmanned aircraft systems: the near-urban environment,” in Proc. MILCOM 2015, Tempa, FL, USA, Oct. 2015.

2

02( 1) ( 1) ( 1)( ) exp 2K r K r K Kp r K I rξ

+ + += − − Ω Ω Ω

• K : 𝑛𝑛2

𝜎𝜎2• Ω : 𝑛𝑛2 + 𝜎𝜎2• 𝑛𝑛 : pathloss exponent• 𝐼𝐼0 : 0th Bessel function

UAV Channel Model (Air-to-Air)

RF propagation model designMain consideration Dominant LoS Component (Rician) Doppler Frequency

• Due to the potentially large relative velocity between UAVs

7

1 cos2d

vft

θπ λ∆Φ

= =∆

[17] Y. Zeng, R. Zhang, T. J. Lim, “Wireless Communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communication Magazine, vol. 54, no. 5, pp. 36 – 42, May. 2016.

Doppler Shift(The apparent change in frequency: )

UAV Channel Model (Air-to-Air)

Effect on ATA ChannelRSS vs. UAV altitude (do not consider Doppler effect)

Distance between UAV : 50 m Laboratory measurements

17[22] N. Goddemeier, and C. Wietweld, “Investigation of air-to-air channel characteristics and a UAV specific extension to the Rice model,” in proc. IEEE GlobecomWorkshop 2015, San Diego, CA, USA, Dec, 2015

ba h cσ = × +Fitting formula

σ : Avg. MPC power h : altitude

UAV : AscTec Firefly UAVs Wireless Device : Compex WLE350NX

(802.11s mesh, Frequency : 2.4 GHz, Transmit power : 20 dBm)

※ RSS : Received Signal Strength

UAV Deployment- Deployment scenario

18

UAV Deployment (Deployment Scenario)

Case of Deployment Scenario Single UAV vs. Multi UAVs Multi UAVs have more advantages than Single UAV

Single hop vs. Multi hops Infrastructure-based vs. Ad-hoc available

Hierarchical architecture

19


Single UAV vs. Multi UAVsAdvantages of multi UAVs Configured to provide services co-operatively Extend coverage UAVs are smaller and less expensive

Single hop vs. Multi hops Single hop: star topology (a), (b)Multi hops: mesh topology (c), (d)

20[23] L. Gupta et al., “Survey of Important Issues in UAV communication Networks,” IEEE Commun, Survey & Tutorials, vol. 18, no. 2, Second Quarter, 2016

Feature Single Multi

Scalability • Limited • High

Survivability • Poor • High

Speed of mission • Slow • Fast

Cost • Medium • Low

Bandwidth required • High • Medium

Complexity of control

• Low • High

< Comparison between Single and multi UAVs >

Star network Mesh network

• Point to point • Multi-point to multi-point

• Infrastructure-based• Infrastructure-based or

Ad hoc

• Single hop • Multi-hop

• Devices cannot move freely

• Free to move(infrastructure based movement is restricted)

• Not self configuring • Self configuring

< Comparison between Star and Mesh network >


Hierarchical Architecture – Military case JALN (Joint Aerial Layer Network) - DoD Augmentation and Extension of tactical networks that will support operations in

challenging or degraded communications environments within a JOA

Core functions HCB: Long haul reach-back DARE

• WB DARE» Communications access waveforms

• NB DARE» Fielded waveforms needing gateway

functionality

Transition: Gateway functionality to bridge nets

21[24] “Joint Concept for Command and Control of the Joint Aerial Layer Network,” Joint Chiefs of Staff, 2015.03

※ JOA : Joint Operating Aera

NB


22[24] “Joint Concept for Command and Control of the Joint Aerial Layer Network,” Joint Chiefs of Staff, 2015.03

※ SOF : Special operation forces※ MAF : Missile Alert Facility※ Austere environment : Infrastructure will be weak to nonexistent


Hierarchical Architecture – Military caseASIMUT project Aims at decreasing the operator workflow during a surveillance mission lead by

swarm of UAVs• European Defense Agency,

THALES, Bordeaux Univ., Luxmbourg Univ., Fraunhofer IOSB, Fly-&-Sense» 2015

23[25] ASIMUT project, “https://asimut.gforge.uni.lu/”

※ ASIMUT : Aided to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques


Hierarchical Architecture – Military caseNext generation tactical communication networks with space and aerial Future military communication network target service proposal including

Aerial communication relay after TICN power-up Network architecture

• By considering traffic size, mission and communication system» Satellite(commercial, MILSAT, etc)» UAV(High capacity, Low capacity)» Ground(TICN, Solider)

24

UAV Deployment- Cell planning

25

UAV Deployment (Cell planning)

Case of cell planningDownlink performance Provide coverage for ground users in desired area Derived coverage probability

• Find out the optimal coverage, UAV altitude, UAV transmit power

Uplink performance UAV collect data from ground users in desired area

• UAVs update their locations based on active devices» Mobile cell case

26

Cell planning for coverage performance using stationary UAVs UAV Downlink coverage performance Provide coverage for ground users in desired area

Main consideration Coverage probability of a UAV

• Find the coverage radius of each UAV for ground userconsidering the interference from other UAVs» The altitude (h) and locations of the UAVs» Directional antennas gain (beamwidth : θB / 2)» Transmit power (lifetime) additional constraint

Efficient deployment strategy for multi-UAVs• The number of available UAVs

27


[26] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEECommunications Letters, vol. 20, no. 8, pp. 1647-1650, Aug. 2016.

Cell planning for coverage performance using stationary UAVs Coverage probability For a ground user, located at a distance from the projection

a given UAV j

• PLoS : LoS probability (including altitude, PNLoS = 1 - PLoS)• Pmin : minimum received power requirement

for successful detection• Pmin : minimum received power requirement

for successful detection• LdB : path-loss• Pt : transmit power• G : antenna gain ( ) • μ, σ : mean and variance of the shadow fading

28


,cov , min

min dB 3dB LoS min dB 3dB NLoSLoS, NLoS,

LoS NLoS

Prob Prob ( )

r jr j

t tj j

PP P db P

N I

P L P G P L P GP Q P Q

β

µ µσ σ

= ≥ = ≥ +

+ − − + + − − += +

min 10log( ) P N Iβ β= +

β : SINR threshold

N : Noise power

: Mean interference power received from the

nearest UAV

LoS NLoS

10 10LoS,k NLoS,k

4g( ) 10 10

nc k

t kf d

P P Pc

µ µ πϕ

−− − = + 2

23dB 29000 / BG θ≈

I

tan2B

ur r h θ ≤ =


Cell planning for coverage performance using stationary UAVs Optimal UAV coverage area Coverage radius of a UAV

Optimal multiple UAVs deployment (no overlapping)

• : vector location of UAV j• M : number of UAVs

29


( )* * * 2 , , arg max , 1,......,

st. 2 , 1,......,

tan( / 2)

j u u

j k u

j u c

u B

r h r Mr j M

r r r j k M

r r R

r h θ

= ∈

− ≥ ∉ ∈

+ ≤

≤

jr

# of UAVs Coverage radius of each UAV

Maximum total coverage

1 Rc 12 0.5Rc 0.53 0.464Rc 0.6465 0.370Rc 0.6859 0.261Rc 0.689

CirclePacking problem

covmax | ( , , )u t Br r P r P θ ε= ≥


Number of UAVs vs. Total coverage, vs. lifetime

Rc : 5000 m , θB : 80°

Cell planning for coverage performance using stationary UAVs Number of UAVs vs. altitude Rc : 5000 m θB increases, each UAV coverage increases

30


Number of UAVs ↑ , altitude ↓(because size of each UAV coverage is decreased)

Number of UAVs ↑ , transmit power ↓(because size of each UAV coverage is decreased)


Cell planning for coverage performance using stationary UAVs The network model which have a UAV with fixed coverage Deriving optimal UAV location (x,y,z) to support ground users while minimizing the

power consumption of UAV • Objective function is an expression for pathloss equations which is associated with UAV u

and ground user i

31


[27] 조준우, 김재현, 음수빈, “Particle swarm optimization을 활용한 도시환경 하 저고도 UAV 최적 배치,” 한국통신학회 동계종합학술대회, 2018년 1월

,1

,

-1,

,

minimize

s.t.

0 | tan ( / ) | / 2 1, ... ,

k

total i ui

i u max

i u

i u u

u

PL PL

PL PL

h d i Id r

h z h

π

=

=

<

< < =

≤

< <

∑

urban_net, urban_net 1, ... ,

i ix y i I∀ ∈ ∀ ∈ =

Particle SwarmOptimization

32


Cell planning with D2D deviceUAV Downlink performance with D2D device (UAV+ Devices Interference model) Downlink users located uniformly in the cell D2D users whose distribution follows Homogeneous Poisson Point Process

Main consideration Average sum rates (downlink user + D2D users)

• Coverage probability• Find out the optimal UAV altitude while average

sum rates are maximized

[28] M. Mozaffari et. al, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Trans. Wirel. Commun., Feb. 2016

33


Cell planning with D2D deviceAverage rate Derived from coverage probability D2D and Downlink users Assume

• Downlink user :

• D2D user :

Average sum rate

• μ : Service rate• 𝜆𝜆𝑑𝑑𝑑𝑑 : Downlink density• 𝜆𝜆𝑑𝑑 : D2D density

Number of D2D users in region (radius Rc): Derived from Homogenous Poisson Point Process


34


Cell planning with D2D deviceUAV altitude vs. system sum rate

𝒅𝒅𝟎𝟎(m)(D2D distance)

Optimal altitude (m)

30 400

25 350

20 300

Sum rates are depends on UAV altitude and D2D distance(because of interference)


Cell planning for IoT devicesUsed as aerial base stations to collect data from IoT devices UAVs dynamically update their locations (update time)

based on IoT activation process• Case of UAV path-planning

Main consideration Optimal locations of the UAVs associated given active IoT

devices• The transmit power of the IoT devices, 3D locations of the UAVs

IoT activation models• Periodic : IoT can report their data periodically• Probabilistic : IoT can have probabilistic activation

(Beta distribution)

35


[29] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles(UAVs) for energy-efficient internet of things communications,” IEEE trans. Wirel. Commun., vol. 16, no. 11, pp. 7574 – 7589, Nov. 2017.

Cell planning for IoT devicesOptimal locations of the UAVs (given active IoT devices) Relation between IoT transmit power

and UAV locations

• vj : 3D location of UAV j • Pi : IoT Transmit power• : average pathloss (IoT – UAV)• Ln : number of IoT devices

36


ijg

< Example of UAVs’ locations and their associated IoT devices >

UAV served 100 devices Total IoT devices : 500


Cell planning for IoT devices IoT activation model Periodic [0, T ]

» bn : the exact number of active IoT devices» n : update number» L : number of IoT devices» : indicator function which can only be

equal 1 or 0» tn, tn-1 : update time» τi : activation period of devices i

37


Update number vs. number of active IoT device (will need to served)


Cell planning for IoT devices IoT activation model Probabilistic [0, T ]

» tn, tn-1 : update time» I : regularized incomplete beta function» an : average number of active device» L : number of IoT devices» κ, ω : beta distribution parameters

38


Update number vs. number of active IoT device (will need to served)

[29] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles(UAVs) for energy-efficient internet of things communications,” IEEE trans. Wirl. Commun., vol. 16, no. 11, pp. 7574 – 7589, Nov. 2017.

Cell planning for IoT devicesMethod of set update times Ensure that the number of IoT

devices (which needs to be served) at each update time does not exceed a specific number, a

39


[29] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles(UAVs) for energy-efficient internet of things communications,” IEEE trans. Wirl. Commun., vol. 16, no. 11, pp. 7574 – 7589, Nov. 2017.

Resource management

40

Bandwidth requirementVariety of data types and requirements in each UAV Data type

• CNPC(Control and Non-Payload Communication) link : Telemetry (Tm), Telecommand (Tc)

• Video, SAR data, voice, etc.

Requirement• Operation environment (number of users, devices, etc.)

Maximizing bandwidth efficiency method while meeting the requirement

Energy Limited batteries of UAV Hovering time (Flight time)

Minimizing hovering time and optimizing the service performance

41

Resource management

Resource management using Dynamic TDMAResource allocation algorithm Maximize the network throughput while satisfying the minimum data rate

requirement• Maximizing the number of critical data in TDMA frame

» Critical data : control message (Tc, Tm)» Uncritical data : video, voice

Derived timeslot size and decide number oftimeslot to allocate for each data

42

Resource management

[30] H. R. Cheon, J. W. Cho, J. H. Kim, “Dynamic resource allocation algorithm of UAS by network environment and data requirement,” in proc. ICTC 2017, jeju, Korea, 18 - 20, Oct. 2017.

※ Tc : Telecommand※ Tm : Telemetry

Environment 1 Environment 2

Requirementdata rate

(bps)

Tc 30.794 k Tc 4,933

Tm 44.734 k Tm 28,774

Video 27.7 k Video 4,800

Voice 270 k Voice 1 M

Resource management using Dynamic TDMAAllocation algorithm

43

Resource management

①

②

③

④


Resource management using Dynamic TDMAPerformance result Compared dynamic and fixed unit timeslot (1 ms)

44

Resource management

Fixed Dynamic

Environment 1 22 22

Environment 2 40 133

< The maximum number of UAVs>


Resource management for providing data services for ground usersResource : bandwidth, UAV energy (hover time)

Main consideration Optimal cell partitioning with fair resource allocation

• Maximize the average number of bit that transmitted to the users» Each cell partition is assigned to one UAV

Minimum average hover time• Using optimal cell partitioning• Find out the relationship between bandwidth

and UAV energy

45

Resource management

[31] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Wireless Communication using unmanned aerial vehicles (UAVs): optimal transport theory for hover time optimization,” accepted in IEEE Trans. Wirel. Commun., 2017

Resource management for providing data services for ground usersOptimal cell partitioning Maximizing the average data service by optimal partitioning of the target area

which is served by UAV i high complexity !• Using transport theory (Kantorovich Duality Theorem)

» Transportation from users to UAVs

46

Resource management

• : cell partition• 𝑀𝑀 : total number of UAVs • 𝛾𝛾𝑖𝑖 : SINR• 𝑇𝑇𝑖𝑖 : effective transmission time of UAV 𝑖𝑖• 𝛼𝛼𝑖𝑖 , : resource allocation factor

Spatial distribution

of users

Average data service at location (𝑥𝑥, 𝑦𝑦) ∈ 𝐴𝐴𝑖𝑖(including total available bandwidth for UAV i)


Resource management for providing data services for ground usersOptimal cell partitioning Proposed method vs. Weighted Voronoi

47

Resource management

Users’ distribution (σ0) vs. FairnessCell partitioning (Proposed, Weighted Voronoi)

<proposed> <Weighted Voronoi> Non-uniform Uniform


Resource management for providing data services for ground usersHover time Contain effective data transmission time and Control time

• Effective data transmission time : UAV services the users• Control time : hover time – Effective data transmission time (initiate connections)

Minimize average hover time of UAV i to service partition

48

Resource management

• 𝑁𝑁 : total # of users• 𝑢𝑢(𝑥𝑥, 𝑦𝑦) : load (in bits) of a user located at (𝑥𝑥, 𝑦𝑦)• 𝐶𝐶𝑖𝑖𝐵𝐵 : Shannon capacity ( )• 𝑔𝑔𝑖𝑖 : additional control time

effective data transmission time

Control time


Resource management for providing data services for ground usersBandwidth vs. hover time

49

Resource management

• Optimal : derived from optimal hover time eq.• Equal : providing same bandwidth to users

Hover time vs. # of UAVs, bandwidth

UAV power ↓ , Number of UAVs ↑ , Bandwidth usage ↑


Security

50

51

Security

사진 : 조선일보, http://m.chosun.com/svc/article.test.html?sname=news&contid=2015080303188: YTN, http://www.isstime.co.kr/news/articleView.html?idxno=23724: 한국일보, http://www.hankookilbo.com/v/05b1bc47f0154d659dc5fab486a3dce7: Drone blogs, https://www.droneblog.com/2017/10/24/counter-drone-market-solutions-overview-and-perspectives/

http://m.chosun.com/svc/article.test.html?sname=news&contid=2015080303188

http://www.isstime.co.kr/news/articleView.html?idxno=23724

http://www.hankookilbo.com/v/05b1bc47f0154d659dc5fab486a3dce7

General attack possibilitiesHardware attack Attacker has access to the UAV

components directly• components

» Main program/processor (processing sensor data, control), magnetometer, power system, etc.

Attack method• Carried out during the maintenance and

storage of the UAV or during the manufacturing and delivering

52

Security

[32] Openworksenginnering, https://openworksengineering.com/[33] A. Kim et. al., “Cyber attack vulnerabilities analysis for unmanned aerial vehicles,” Infotech@aerospace, 2012

https://openworksengineering.com/

General attack possibilitiesWireless attack An attacker uses the wireless communication

channels to alter data the UAV

Hijacking (application study: Parrot AR.Drone)• De-authentication attack

» AR.Drone has its own Wi-Fi network and allows users to connect and control

53

Security

[33] A. Kim et. al., “Cyber attack vulnerabilities analysis for unmanned aerial vehicles,” Infotech@aerospace, 2012.[34] C, Rani et. al., “Security of unmanned aerial vehicle systems against cyber-physical attacks,” The Journal of Defense Modeling and Simulation,

vol. 13, no.3, pp. 331 – 342, 2016

< Hacking tool: Aircrack-ng>

< Hijacking procedure >

General attack possibilities Sensor spoofing Attacks are directed towards on-board

sensors that depend on the outside environment• GPS receivers, radar, sonar, IR sensors, etc.

GPS spoofing attack (application study: USRP)• USRP : GPS record-modify-and-reply system• procedure

1. Acquire and track the coarse/acquisition signals2. Produce and calibrate a fake signals3. Align the forged and authentic GPS signal4. Raise the power of the forged signal to suppress the

authentic signal

54

Security

[33] A. Kim et. al., “Cyber attack vulnerabilities analysis for unmanned aerial vehicles,” Infotech@aerospace, 2012.[35] D. He et. al., “Communication security of unmanned aerial vehicles,” IEEE Wireless Communications, vol. 24, no. 4, pp. 134 – 139, Aug. 2017.

Real GPS signal Latitude : 31.23, longitude : 121.39

After spoofingLatitude : 0, longitude :0

CountermeasureApproach to solving the wireless attack Encryption

• Enabling WPA2 encryption» The key length should be appropriately chosen to defeat brute force attacks

Disable the broadcast of SSID• Alternatively, access to systems can be restricted to a pre-registered MAC address only• Additional RF front-ends, additional weight and cost

Defense against DDoS Intrusion detection systems (IDS)

• Inspects the system behavior to find anomalies

55

Security

[34] C. Rani et. al., “Security of unmanned aerial vehicle systems against cyber-physical attacks,” The Journal of Defense Modeling and Simulation, vol. 13, no.3, pp. 331 – 342, 2016

[35] D. He. al., “Communication security of unmanned aerial vehicles,” IEEE Wireless Communications, vol. 24, no. 4, pp. 134 – 139, Aug. 2017.

CountermeasureApproach to solving the GPS jamming and spoofing Jamming-to-noise sensing defense

• Monitoring the total received power in the GPS band of interest• The threshold is set to a suitable value

» False alarm

Multi-antenna defense• Difficult to mimic the relative carrier phase of the GPS signals as seen by multiple spatially-

separated antennas• Additional RF front-ends, additional weight and cost

Cryptographic authentication for C/A (Coarse/Acquisition) signals• Required software and hardware modifications

56

Security

[35] D. He. al., “Communication security of unmanned aerial vehicles,” IEEE Wireless Communications, vol. 24, no. 4, pp. 134 – 139, Aug. 2017.

Conclusion

57

Conclusion

Summary Introduction Introduction to UAV-based wireless communication

• Functions of UAV» UAV-UAV, relay node, gateway

Challenges of UAV-based wireless communication• keywords to overcome challenges

» UAV channel model, UAV deployment, Resource management, Security

UAV Channel model Air-to-ground

• LoS probability• Altitude vs. cell radius

Air-to-Air• Dominant LoS• Doppler frequency

58

Conclusion

SummaryUAV deployment Deployment scenario

• Multi UAV, Multi-hop, Hierarchical

Cell planning case• Downlink : basic, with D2D devices

» Derived the coverage probability, find out the optimal value

• Uplink : IoT» Derived IoT activation model, find out the optimal value

Resource management Variety of data types and requirements in each UAV UAV energy (hovering time)

• Cell partitioning for fair resource allocation• Bandwidth vs. hovering time trade off.

59

Conclusion

SummarySecurity General attack possibility

• Hardware, wireless attack, sensor spoofing

Countermeasure

60

Reference

61

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Network and Computer Application, vol. 84, pp. 93-108, Apr. 2017[13] SKT, http://www.sktelecom.co.kr/advertise/press_detail.do?idx=4190[14] AT&T, “When COWs Fly: AT&T sending LTE Signals from drone,” http://about.att.com/innovationblog/cows_fly[15] Verizon, http://www.verizon.com/about/news/first-responders-make-calls-and-send-text-messages-using-flying-cell-site[16] IBM Watson, https://www.ibm.com/watson/[17] Y. Zeng, R. Zhang, T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE

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USA, Dec. 2014.

Reference

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[21] D. W. Matolak, R. Sun, “Air-Ground channel characterization for unmanned aircraft systems: the near-urban environment,” in Proc. MILCOM 2015USA, Oct. 2015.

[22] N. Goddemeier, and C. Wietweld, “Investigation of air-to-air channel characteristics and a UAV specific extension to the Rice model,” in proc. IEEE Globecom Workshop 2015, San Diego, CA, USA, Dec, 2015

[23] L. Gupta et al., “Survey of Important Issues in UAV communication Networks,” IEEE Commun, Survey & Tutorials, vol. 18, no. 2, Second Quarter, 2016

[24] “Joint Concept for Command and Control of the Joint Aerial Layer Network,” Joint Chiefs of Staff, 2015.03[25] ASIMUT project, “https://asimut.gforge.uni.lu/”[26] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless

coverage,” IEEE Communications Letters, vol. 20, no. 8, pp. 1647-1650, Aug. 2016.[27] 조준우, 김재현, 음수빈, “Particle swarm optimization을 활용한 도시환경 하 저고도 UAV 최적 배치,” 한국통신학회 동계종합학술대회,

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Aug. 2017

Thank you !

Q & A63

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