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Date Created: 09/19/15
Connectivity Issues in Presence of UAVs in Mobile Ad Hoc Networks Vinay Sridhara Department of Electrical and Computer Engineering CISC 861 Wireless Networks and Mobile Computing Overview EIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEI 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 UAV III UAV unmanned aerial vehicle III Used for surveillance and reconnaissance III Projected as an important component of MANETs in warfare Foldout wings Propeller blades Arxi Why UAVs III Propagation loss I The slow fading due to shadows caused by obstacles is reduced or does not even exist M 1 l The propagation loss betweer UAV51 and between UAV and ground node I Also the propagation of ground nodes as the distance increases III UAVs can act as reliable routers for multihop communications III Motion can be controlled using algorithms Related Issues III UAVs are costly III Minimize the number of UAVs III Optimal placement of UAVs III Load balancing Similar Problems III Sensor network coverage problem III Inventory coverage problem when using sensors for identifying the RFID of equipment Objective III Maximize Connectivity III Minimize the number of UAV nodes Optimal UAV Placement III Objective I Maximize connectivity I Minimize the of UAVs III Problem Formulation l Given a distribution of N nodes on the ground plane and the freespace 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 Heuristics I III Static grid based approach I Divide the air lane into rectangular hexagonal gri and place the UAV at the center of each grid I Very simple approach I Costly I Might not be very effective III Random movement approach I Make the UAV take a random walk above the ground with in a specified boundary I Very ad hoc method and does not optimize anything Heuristics II El Local flocking rule based approach I Simultaneously track and cover the ground nodes and keep connectivity with the aerial nodes l Inspired by the flocking nature of birds El Cluster based approach Flocking Algorithm III Based on the local flocking rules that birds and insects exhibit III Always fly in a group III Do not crash into each other III Overall motion is controlled purely by the local motion of the individual birds Flocking Rules 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 Assumptions I III Safe distance III Limited rotation III R2 propagation loss III Cannot fly very close to the ground III Poor or no connectivity between ground nodes III Complete connectivity of ground nodes is not guaranteed Assumptions II I I I 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 Local Information III Obtained from the periodic heartbeat messages III UAV local Information I UAVs Current location GPS Information I Number of ground nodes connected to the UAV III Ground Node local Information I Ground nodes current location GPS Information Working Update Table 0 Store Separation distance 0 Store number of nodes connected to neighboring UAV U0 0 Maintain a cumulative average of number of connected ground nodes vag mg 1 armament Threshold parameters Dmax Max distance between two nodes without losing connectivity Dmm 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 Drwallt Maximum distance UAV allowed to loiter 6max Maximum turning angle a Averaging Bias State Machine Representation me WW Nt lt Nt d ltD me yau min Rule Execution 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 statespecific order of testing the conditions before rule execution at each UAV I Eg in state attract 3 conditions can be active simultaneously 1d gtD centroid centrMAX 2 d lt Dmin mey0u 3 M lt fof III Currentl the UAVs do not remember the neighbor updates that causes t e state transition I if this state is maintained then rule execution can be more intelligent Order of execution is 3 2 and 1 Performance Evaluation III The performance is compared with that of static grid based approach III Metrics I of disconnected components I Cumulative average over time Load Balancing Metric III Two simulation scenarios are evaluated l Random Motion in constrained area I Directed group motion marching Simulation Parameters 7139 Area 10112 z 20 x20 and 20 x40 amax E N 100 Heartbeat periad 5s UAV4 HzloKm meVmaX18msi20 a 025 Dmax 12Km Dmalk 05Km Dmin 2 10km Tx Power 33dbm z 2W Antenna Gain 1db Simulation Results No Marching in 1 fixed 3mg ccal F 2 2c 7 7 15 7 7 n 7 7 W c 4 EC use 5 2C1 23 3 T39In39ue sli919l1 pain 7 559 Simulation Results Marching x m 33 SE2 Numbw ofstcornscted No 4c 2 k c A A 4 r3 710 7 150 28 253 3 c I Inie slice eeh represents 5 seccn s 1 2L 4 l use e e W e e 65 1 e e 4 3 2 3 7 k 7 r 25 3 c UAV Placement Urban Warfare Scenario III 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 predefined path like sidewalks and roads and hallways inside the buildings Simulated Annealing III Simulated annealing l A generalization of a Monte Carlo method for examining the equations of state and frozen states of nbody systems Metropolis et al 1953 l The concept is based on the way the liquids freeze and the metals get into crystalline form as the temperature decreases Algorithm III Objective I To find the global minimum I In our case find a point where there is maximum conneCtIVIty do for number of iterations nd NewPos if NewPos better than odPos accept else dE accept with a probability given by 7 end 6 III Significance of temperature T I I rdk S cenarlo Simulation Parameters EIEIEIEIEIEIEIEIEI Area 500 X 500 m2 of ground nodes 30 Simulation time 1005 of buildings 10 of floors 3 and Height 35m Buildings are assumed to be homogeneous Height of UAV plane 30m Mobility constrained mobility Pause time distribution exponentially distributed with mean 20 Analysis 4011 g mm 250 3U 3539 Analysis II Resuh prob Performance III Evaluation metric l Optimal connectivity in presence of a single UAV node simulated annealing num iter100 initial temp400 simulated annealing num iter1000 initial temp200 prob 005 01 relative error BestM 15 03 0 0005 001 0015 002 0025 003 aX39FOUNdMaXVBEStMaX relative error BestMax FoundMaxBestMax Conclusions amp Future Work El Need to incorporate different parameters like I Velocity I Rotational Angle I 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 I Eg group mobility References 1 K Kar and S Bane 39ee iNode Placement for Connected Coverage in Sensor ggggorksn WiOpt 003 Workshop INRIA SophiaAntipolis France March 2 C W Reynolds iFlocks Herds and Schools A Distributed Behavioral Modeli gzmlingustsr Graphics 21 4 SIGGRAPH 3987 Conference Proceedings pp 25 3 K Xu X Hon M Gerla H Ly and D LA Gu ILandmark routin in large wireless batt eeld networks using UAVs MILCOM 2001 IEEE litary Communications Conference no 1 October 2001 pp 230234 4 MetropolisN A Rosenbluth M Rosenbluth A Teller E Teller quotEquation of State Calculations by Fast Computing Machinesquot J Chem Phys21 6 10871092 1953 5 An Intelligent Approach to Coordinated Control of Multiple Unmanned Aerial Vehicles George Vachtsevanos Liang Tang Johan Reimann jvecegatec edu ltangecegatechedu gtg221dprismgatechedu chool of Electrical and Com uter Engineering Georgia Institute of Technology Atlanta GA 30 32 USA 6 UAV Placement for Enhanced Connectivity in wireless Adhoc Networks by Majid RaissiDehkordi Karthikeyan ChandrashekarJohn S Baras Thank you Questions III How is load balancing achieved in flocking algorithm III In the flocking approach only local information is used What is the main advantage of this