Vinay SridharaDepartment of Electrical and Computer Engineering

CISC 861 - Wireless Networks and Mobile Computing

Connectivity Issues in Presence of UAVs in Mobile Ad Hoc Networks

UAV Why UAVs Related Issues Related problems Heuristics Flocking Algorithm Assumptions Flocking Rules Some implementation details Performance evaluation Simulation results UAV placement problem in Urban scenarios Simulated Annealing Analysis Result Performance Evaluation Conclusions

Overview

UAV – unmanned aerial vehicle Used for surveillance and reconnaissance Projected as an important component of

MANETs in warfare

UAV

Propagation loss The slow fading due to shadows caused

by obstacles is reduced or does not even exist

The propagation loss between UAVs and between UAV and ground node

Also the propagation of ground nodes as the distance increases

UAVs can act as reliable routers for multihop communications

Motion can be controlled using algorithms

2δ

1

1

Why UAVs ?

UAVs are costly Minimize the number of UAVs Optimal placement of UAVs Load balancing

Related Issues

Sensor network coverage problem Inventory coverage problem when using

sensors for identifying the RFID of equipment

Similar Problems

H

r

R

Maximize Connectivity Minimize the number of UAV nodes

Objective

Objective: Maximize connectivity Minimize the # of UAVs

Problem Formulation: Given a distribution of N nodes on the ground

plane and the free-space transmission ranges of UAVs (R) which are flying at an altitude H, what is the minimum number of UAVs necessary such that every ground node is connected to the UAV and the UAVs for a connected subgraph?

Optimal UAV Placement

Heuristics – I Static grid based approach

Divide the air plane into rectangular / hexagonal grid and place the UAV at the center of each grid

Very simple approach Costly Might not be very effective

Random movement approach Make the UAV take a random walk above the

ground with in a specified boundary Very ad hoc method and does not optimize

anything

Local flocking rule based approach Simultaneously track and cover the ground nodes

and keep connectivity with the aerial nodes Inspired by the flocking nature of birds

Cluster based approach

Heuristics – II

Based on the local flocking rules that birds and insects exhibit

Always fly in a group Do not crash into each other Overall motion is controlled purely by

the local motion of the individual birds

Flocking Algorithm

Move to directly above the ground centroid

Tracking the ground nodes

Move towards neighboring UAV Maintain Connectivity

Move away from neighboring UAV Avoid collision and maintain coverage

Random walk in vicinity Do not remain static and heal random partitions

Flocking Rules

Safe distance Limited rotation R2 propagation loss Cannot fly very close to the ground Poor or no connectivity between

ground nodes Complete connectivity of ground

nodes is not guaranteed

Assumptions – I

Assumptions – II UAVs cannot remain absolutely stationary UAV is not a point object Start with small number of fixed UAVs and

adjust their positions Only local information is available for each

UAV Neighbor discovery protocol is running on

all ground and airborne nodes

Obtained from the periodic heartbeat messages

UAV local Information UAVs Current location (GPS Information) Number of ground nodes connected to the

UAV Ground Node local Information

Ground nodes current location (GPS Information)

Local Information

New Neighbor

Recv Heartbeat

UpdateTable

Y

• Store Separation distance

• Store number of nodes connected to neighboring UAV U(j)

• Maintain a cumulative average of number of connected ground nodes

Working …

currentt

avg1t

avgt Nα1αNN

Threshold parameters

Dmax Max distance between two nodes without losing connectivity

Dmin Minimum distance between nodes to maintain good coverage (load balancing)

DcentrMax Maximum distance UAV allowed to remain from the ground centroid

Dsafe Minimum distance to be maintained to lower the risk of collision

Drwalk Maximum distance UAV allowed to loiter

θmax Maximum turning angle

α Averaging Bias

State Machine Representation

safeyoume Dd ,

avgtt NN 1

avgtt NN 1

Move tocentroid

Attract

Repel Random walk

centrMAXcentroid Dd

centrMAXcentroid Dd

min, Dd youme

max, Dd youme

min, Dd youme

min, Dd youme

)()( yourt

met NN

)()( yourt

met NN

safeyoume Dd ,

Arrival of a heartbeat from a neighbor may result in a state transition and a new destination waypoint may be set for the UAV

There is a state-specific order of testing the conditions before rule execution at each UAV E.g. in state attract 3 conditions can be active simultaneously

Currently the UAVs do not remember the neighbor updates that causes the state transition if this state is maintained then rule execution can be more

intelligent

avgtt NN 1 .3

centrMAXcentroid Dd .1

min, .2 Dd youme Order of execution is 3, 2 and 1Order of execution is 3, 2 and 1

Rule Execution

The performance is compared with that of static grid based approach

Metrics # of disconnected components Cumulative average over time (Load

Balancing Metric) Two simulation scenarios are evaluated

Random Motion in constrained area Directed group motion (marching)

Performance Evaluation

Simulation Parameters

4020 and 2020)( 2 KmArea

100N

%20/18],[ maxmin smVV

speriodHeartbeat 5

25.0

KmDrwalk 5.0

KmD 12max

kmD 10min

6max

KmHUAV 10 4#

WdbmPowerTx 233 dbGainAntenna 1

Simulation Results - No Marching

Simulation Results – Marching

Topology information is very important There tends to be a concentration of nodes at the

intersections In addition to UAVs not colliding, care should be taken to

see that UAVs do not collide with the high raised buildings Buildings act as obstacles that cause slow fading when the

ground nodes are very close to them There might be other objects that act as scatterers Diffraction becomes important factor

Mobility modeling is very important Random waypoint simulation is not feasible for city

scenarios Nodes always move along a pre-defined path like sidewalks

and roads and hallways inside the buildings

UAV Placement – Urban Warfare Scenario

Simulated annealing A generalization of a Monte Carlo method

for examining the equations of state and frozen states of n-body systems [Metropolis et al. 1953]

The concept is based on the way the liquids freeze and the metals get into crystalline form as the temperature decreases

Simulated Annealing

Algorithm Objective

To find the global minimum In our case find a point where there is maximum

connectivity

do for number of iterationsfind (NewPos)if (NewPos better than oldPos)

acceptelse

accept with a probability given by end

Significance of temperature

T

dE

e

Scenario

Area ~ 500 X 500 m2

# of ground nodes = 30 Simulation time = 100s # of buildings = 10 # of floors = 3 and Height = 3.5m Buildings are assumed to be homogeneous Height of UAV plane = 30m Mobility – constrained mobility Pause time distribution – exponentially

distributed with mean 20

Simulation Parameters

Analysis – I

Analysis – II

Result

0 0.05 0.1 0.15 0.2 0.25 0.3-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7simulated annealing num iter=100 initial temp=4.00

relative error (BestMax-FoundMax)/BestMax

pro

b

0 0.005 0.01 0.015 0.02 0.025 0.03-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

simulated annealing num iter=1000 initial temp=2.00

relative error (BestMax-FoundMax)/BestMax

pro

b

Evaluation metric Optimal connectivity in presence of a single UAV

node

Performance

Need to incorporate different parameters like Velocity Rotational Angle Etc

Need to consider load balancing issues under the presence of more than one UAV node

Need to evaluate the algorithm with different topologies

Need to consider different mobility scenarios E.g. group mobility

Conclusions & Future Work

[1] K. Kar and S. Banerjee, ìNode Placement for Connected Coverage in Sensor Networks,î WiOpt 2003 Workshop, INRIA Sophia-Antipolis, France, March 2003.

[2] C. W. Reynolds, ìFlocks, Herds, and Schools: A Distributed Behavioral Model,î Computer Graphics, 21(4) (SIGGRAPH '87 Conference Proceedings), pp. 25-34, 1987.

[3] K. Xu, X. Hong, M. Gerla, H. Ly, and D. L. Gu, ìLandmark routing in large wireless battleeld networks using UAVs,î MILCOM 2001 - IEEE Military Communications Conference, no. 1, October 2001, pp. 230-234.

[4] Metropolis,N., A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller, "Equation of State Calculations by Fast Computing Machines", J. Chem. Phys.,21, 6, 1087-1092, 1953.

[5] “An Intelligent Approach to Coordinated Control of Multiple Unmanned Aerial Vehicles” George Vachtsevanos, Liang Tang, Johan Reimann gjv@ece.gatech.edu ltang@ece.gatech.edu gtg221d@prism.gatech.edu School of Electrical and Computer Engineering Georgia Institute of Technology, Atlanta, GA, 30332. U.S.A.

[6] UAV Placement for Enhanced Connectivity in wireless Adhoc Networks by Majid Raissi-Dehkordi, Karthikeyan Chandrashekar,John S. Baras

References

Thank you

How is load balancing achieved in flocking algorithm ?

In the flocking approach only local information is used. What is the main advantage of this ?

Questions