To deliver the things without human requirement but with full safety and to detect the place path and persons using RF controller by implementing concept of drone for a smaller and to implement the same for a larger network by implementing the Bat Algorithm in Wireless Sensor Network.

1. IMPLEMENTATION OF DYNAMIC REMOTE
OPERATED NAVIGATION EQUIPMENT IN
WIRELESS SENSOR NETWORK USING BAT
ALGORITHM
Guided by,
Mrs. C. P. SUBHA
Associate Professor
Department of ECE
Submitted by
M. NASEEHA 12TC0495
S. SAVETHA 12TC0540
K. SOUNDARYA 12TC0552
S. ALAMELU PRIYADHARSHINI 12TCL039

2. 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

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

4. INTRODUCTION
• Dynamic Remotely Operated Navigation Equipment
(DRONE)
• Remotely controlled flying object
• Can also fly autonomously
Applications
• Data collection and situation monitoring
• Public information and advocacy
• Search and rescue
• Mapping

5. 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

6. 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

7. 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

8. 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

9. Existing BAT Algorithm
Objective function f(x), x = (x1, ..., xd)T
Initialize the bat population xi (i = 1, 2, ..., n) and vi
Define pulse frequency fi at xi
Initialize pulse rates ri and the loudness Ai
while (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 solutions
Generate a local solution around the selected best solution
end if
Generate a new solution by flying randomly
if (rand < Ai & f(xi) < f(x))
Accept the new solutions
Increase ri and reduce Ai
end if
Rank the bats and find the current best x
end while

10. 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

11. HARDWARE DESIGN

12. 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

13. 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

14. 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

15. 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.

16. Working

17. HARDWARE PROTOTYPE

18. SOFTWARE IMPLEMENTATION
IMBAT Algorithm
• Meta-heuristic optimisation algorithm.
• Based on the echolocation behaviour of micro bats
with varying pulse rates of emission and loudness.

19. 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.

20. 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

21. 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

22. 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.

23. 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.

24. RESULTS AND SIMULATION
Node Deployment Energy vs time

25. RESULTS AND SIMULATION
No. of alive nodes vs time PDR vs No. of rounds

26. HARDWARE PROTOTYPE

27. 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.

28. 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.

29. 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.

30. 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.

31. 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.