IMPLEMENTATION OF DYNAMIC REMOTE OPERATED NAVIGATION EQUIPMENT IN

WIRELESS SENSOR NETWORK USING BAT ALGORITHM

Guided by,

Mrs. C. P. SUBHAAssociate Professor Department of ECE

Submitted by

M. NASEEHA 12TC0495S. SAVETHA 12TC0540

K. SOUNDARYA 12TC0552S. ALAMELU PRIYADHARSHINI 12TCL039

CONTENT

• Objective• Introduction• Existing Works• Proposed Work• Hardware Design for Quadcomm DRONE• Software Implementation of Drone in WSN

• Results and Simulation• Conclusion• Future Scope• Reference• Query

OBJECTIVE

• To implement a Quadcomm DRONE for inter department communication and to simulate it in wireless environment for large area using IMBAT Algorithm.

INTRODUCTION

• Dynamic Remotely Operated Navigation Equipment (DRONE)

• Remotely controlled flying object• Can also fly autonomouslyApplications• Data collection and situation monitoring • Public information and advocacy • Search and rescue• Mapping

EXISTING WORKS

• UAV (Unmanned Aerial Vehicle) has been designed with the basics of aero plane.

• Then comes the glider and Drones.

• Drones has been designed for various applications.

• Target drone – Initial drone• Flying bomb drone – To deliver bombs in battle• Surveillance drone – To capture dangerous areas• Hunter killer drone – Used against terrorists and in

military• Police drone – Used by police forces in U.S and

Europe

INTRODUCTION• WSN consists of spatially distributed autonomous devices

using sensors and protocols.• Comprised of sensing, computing, communication elements.• Clustering refers to grouping of nodes.• Clustering types: Homogeneous, Heterogeneous• Homogeneous: Similar energy• Heterogeneous: Dissimilar energy

RELATED WORKS

WSN Algorithms• In WSN various algorithms has been designed to

provide efficient clustering.

• Some of them includes • HEED• UCAPN• LEACH • ACO• Firefly Algorithm

LEACH Algorithm

The operation of LEACH is divided into two phases:

• Setup Phase (Where cluster-heads are chosen)– Cluster-head Advertisement– Cluster Set-Up– Transmission schedule creation

• Steady-state Phase (The cluster-head is maintained when data is transmitted between nodes)– Data transmission to cluster heads– Signal processing (Data fusion)– Data transmission to the base station

Existing BAT Algorithm

Objective function f(x), x = (x1, ..., xd)TInitialize the bat population xi (i = 1, 2, ..., n) and viDefine pulse frequency fi at xiInitialize pulse rates ri and the loudness Aiwhile (t <Max number of iterations)Generate new solutions by adjusting frequency,and updating velocities and locations/solutions [equations (2) to (4)]if (rand > ri)Select a solution among the best solutionsGenerate a local solution around the selected best solutionend ifGenerate a new solution by flying randomlyif (rand < Ai & f(xi) < f(x))Accept the new solutionsIncrease ri and reduce Aiend ifRank the bats and find the current best xend while

PROPOSED WORK

Hardware • To build an auto-

landing drone which is controlled using RF.

Software• For a larger area the

drone is considered as a movable node and implemented using IMBAT Algorithm.

• It is simulated in WSN using NS-2

HARDWARE DESIGN

HARDWARE REQUIREMENTS

Power Supply• LIPO battery is used• 7.6 V• 1800 mAh

Brushless Motor

• 1000 KV• 2 counter clockwise

direction another 2 clockwise direction

HARDWARE REQUIREMENTS

Electronic Speed Controller (ESC )

• 7.4V to 15V• Controls current flow to

motor• Avoids motor damage

Propellor

• Wings to fly• Should be straight with

no bends• Main cause for stability

HARDWARE REQUIREMENTS

Remote Controller• Establish communication between the airborne

micro-controller band and the control placed in the ground station.

• 6 channel radio transmitter is used• Controls the drone movement by throttle condition

Working of Quadcomm drone

• Remote controller has• Roll – moves left or right • Pitch – moves forward or backward • Yaw – rotates clockwise or counter clockwise• Throttle – Gives power to airborne

• As the throttle is given the quadcopter flies.• Motor works in opposite direction to provide up

thrust.• This helps in achieving stability.

Working

HARDWARE PROTOTYPE

SOFTWARE IMPLEMENTATION

IMBAT Algorithm • Meta-heuristic optimisation algorithm.• Based on the echolocation behaviour of micro bats

with varying pulse rates of emission and loudness.

IMBAT (Improved Bat) Algorithm

• Random deployment of nodes.• Formation of clusters with a cluster head selection.• Initializing bat population with desired parameters.• Bats are the movable nodes which is considered as a

drone.• Based on the energy and distance bats move to the

called node and collect the information.• The gathered information is send to the receiver node.

IMBAT Algorithm • Initialize the bat population xi and vi for i = 1 to n• Pulse frequency is defined• Initialize pulse rates ri and the loudness Ai• while (t < Tmax) // number of iterations– Generate new solutions by adjusting frequency, and– Updating velocities and locations/solutions– if(rand > ri )• Select a solution among the best solutions• Generate a local solution around the best solution

– End if

IMBAT Algorithm

– Generate a new solution by flying randomly– if(rand(0,1) < Ai and f(xi) < f(x))• Accept the new solutions• Increase ri and reduce Ai

– end if– Rank the bats and find the current best

• end while• Post process results and visualization

IMBAT Algorithm

Advantages

• Simple, Flexible and Easy to implement.

• Solve a wide range of problems and highly non linear problems efficiently.

• Works well with complicated problems.

• It avoids void routing.

Difference between Existing BAT and IMBAT algorithm

Existing BAT Algorithm• In existing BAT

algorithm homogeneous energy is provided.

• Cluster head is chosen based on parameters such as energy and distance.

IMBAT Algorithm• IMBAT algorithm

heterogeneous energy is provided.

• Cluster head is chosen based on parameters such as link capability and overhead.

RESULTS AND SIMULATION

Node Deployment Energy vs time

RESULTS AND SIMULATION

No. of alive nodes vs time

PDR vs No. of rounds

PDR vs No. of rounds

HARDWARE PROTOTYPE

CONCLUSION

Quadcomm Drone has been effectively employed for inter departmental communication to deliver things or messages.

Quadcomm Drone has been designed with increased stability with reduced vibration.

The major advantage in Quadcomm Drone is the Auto Landing Capability which reduces the risk factors.

Designed in a simple and cost effective manner.

CONCLUSION

• Quadcomm Drone is implemented as a movable node in Wireless Sensor network for larger area using IMBAT Algorithm.

• Simulation results of IMBAT Algorithm shows that– The packet delivery ratio has been increased to about 20%

compared to existing algorithm and about 50 % compared to LEACH algoritm.

– Thus alive nodes as increased by 35 % compared to existing Bat Algorithm and about 47 % compared to LEACH algorithm.

– Residual Energy has increased upto 16% when compared to LEACH.

FUTURE SCOPE

• GPS and Camera can be easily interfaced with our drone for auto fly-back, tracking and security purposes.

• Our Drone can be converted to a fully automated one by using Bluetooth or Wi-Fi technology.

REFERENCES

[1] Travis Dierks, and Sarangapani Jagannathan, “Output Feedback Control of a Quad rotor UAV Using Neural Networks”, IEEE Transactions On Neural Networks, Vol. 21, No. 1, January 2010, pp. 50-66.

[2] Guoqing Zhou,,“Geo-Referencing of Video Flow From Small Low- Cost Civilian UAV”, IEEE Transactions On Automation Science And

Engineering, Vol. 7, No. 1, January 2010, pp. 156-166.

[3] A.M. cho, Jihoon Kim, Sanghyo Lee, Changdon, “Wind Estimation and Airspeed Calibration using a UAV with a Single-Antenna GPS Receiver

and Pitot Tube”, IEEE Transactions On Aerospace And Electronic Systems Vol. 47, No.1 , January 2011,pp.109-117.

[4] F. Remondino, L. Barazzetti, F. Nex , M. Scaioni , D. Sarazzi , “Uav Photogrammetry For Mapping And 3d Modeling”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-1/C22 UAV-g 2011.

REFERENCES

[5] Sonia Goyal, Manjeet Singh Patterh, “Wireless Sensor Network Localization Based on BAT Algorithm”, International Journal of Emerging Technologies in Computational and Applied Sciences, vol.4, issue:5, March-May 2013, pp. 507- 512.

[6] Zhan-Yang Xu, Song-Gang Zhao and Zheng-Jun Jing, ”A Clustering Sleep Scheduling Mechanism Based on Sentinel Nodes Monitor for WSN “, International Journal of Smart Home Vol. 9, No. 1 (2015), pp. 23- 32.

[7] Priyanka Bhoyar, Sunil Gupta, Bharti Masram,” Progressive Sleep Scheduling for Energy Efficient Wireless Sensor Network “, International

Journal on Recent and Innovation Trends in Computing and Communication, Vol.3,issue:3, March 2015.