Applying Particle Swarm Optimization to transmit video over wireless ZigBee network

Intelligent Systems Research Centre Seminar

Dr. Iman Samizadeh, December 11th, 2013 - School of Computing

Content

• Problem• ZigBee – IEEE 802.15.4• MPEG• Swarm Intelligence/Particle Swarm Optimization • Methodology• Results• Conclusion

Problem

Transmitting video over IEEE

802.15.4

Overview of ZigBee

• Low power consumption• Low cost• Intended for WPAN (Wireless Personal Area Network)• Data rates of 250 kb/s, 40 kb/s and 20 kb/s. • Frequency Bands of 2.4GHz industrial, scientific and

medical (ISM*), 915MHz and European 868MHz band.• Supporting Star or Peer-to-Peer network.• Support for low latency devices.

ZigBee Applications

• Smart-home networking• Automotive and industrial networks• Interactive toys• Remote metering/utility meter readers• Sensor networks

ZigBee Stack

ZigBee Network Topologies

Peer-to-peer

Star

Cluster Tree

ZigBee VS Other Wireless Technologies

Y –

Po

we

r co

nsu

mp

tion

X – Bandwidth

ZigBee VS Bluetooth

• StandardBluetooth: based on IEEE 802.15.1 (WPAN)

• Maximum network speed:Bluetooth: 1 Mbit/s

• Typical network join timeBluetooth: 3 seconds

• Protocol stack sizeBluetooth: 250 Kbyte

• BatteryBluetooth: Intended for frequent recharging

• Price per chipBluetooth: $30

ZigBee: based on IEEE 802.15.4 (WPAN)

ZigBee: 250 kbit/s

ZigBee: 30 milliseconds

ZigBee: 4-32 Kbyte

ZigBee: batteries will last for up to 10 years

ZigBee: $2

Picture Expert Group (MPEG)

• MPEG-1(Good for storage on digital media such as video CDs), MPEG-2 (DVD), MPEG-3 (HDTV) and MPEG-4 (offers transparent information)

High Level Application Architecture

Conventional Methods:• Constant bit-rate (CBR) - method guarantees

traffic at a constant rate and is commonly used in typical voice, video and audio, which require more bandwidth

• Variable bit-rate (VBR ) - method is for the applications that require buffering. VBR is typically used to support compressed voice and video

MPEG Group Of Pictures

Short GOP (DVD): I-B-B-B-B-P-B-B-B-B-I-B-B-B-B-P-B-B-B-B-I

Long GOP (MPEG-4): I-B-B-B-B-B-B-B-B-B-B-P-B-B-B-B-B-B-B-B-B-B-I-B-B-B-B-B-B-B-B-B-B-P-B-B-B-B-B-B-B-B-B-B-I

Methodology

• Adaptive Rate Control over IEEE 802.15.4 using Particle Swarm Optimization

MPEG-4 encoding process

Adaptive Systems• Swarm intelligence

• Cities

• The brain

• The immune system

• Ecosystems

• Computer models

Swarm Intelligence (SI)

Origins: How can birds or fish exhibit such a coordinated collective behaviour?

•It is an artificial intelligence technique based around the study of collective behaviour in decentralized, self-organized systems.

•It is made up of a population of simple agents interacting locally with one another and with their environment.

Normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behaviour. i.e. in ant colonies, bird flocking, bacteria modelling and fish schooling

Particle Swarm Optimization (PSO)

Invented by James Kennedy and Russell Eberhart in 1995

They have included the ‘roost’ in SI, so that:

• Each agent was attracted towards the location of the roost.• Each agent remembered where it was closer to the roost.• Each agent shared information with its neighbours about its

closest location to the roost while learning from their own experience.• Each agent as the population members gradually move into better

regions of the problem space.

James and Russell suggested that the velocities and accelerations of swarm are more appropriately applied to particles.

PSO Applications

PSO can be tailor-designed to deal with specific real-world problems. For example problems with continuous, discrete, or mixed search space, with multiple local minima.

• Computer numerically controlled milling optimization

• Battery pack state-of-charge estimation

• Real-time training of neural networks•Moving Peaks (multiple peaks dynamic environment)•Oil industry

PSO propose to fly the solution in to the problem in order to resolve the problem

Quantization

• A process of representing a large -possibly infinite – set of values with a much smaller set

• One of the simplest and most general idea in lossy compression

Scalar Quantization

Q-Scale

• This is the Quantization scale value• MPEG’s Q-scale values has a significant affect on amount

of compression. • The Q-scale values in MPEG-4 can be set for I, P, and B-

frames separately. • The scale can be from 1 to 31, (Larger numbers will result

in better compression but at the expense of worse quality).• Increasing the Q-scale affected the amount of compression

the most.

Com

parin

g V

BR

, C

BR

and

PS

O

ANOVA Test for 240 Frames

Kruskal–Wallis – Nonparametric Box plot for 20 GOPs

Peak Signal-to-noise-Ratio

Method Frame rate PSNR

VBR with gaussian noise 379kbps 20.9351 dB

CBR with gaussian noise 266kbps 17.8369 dB

PSO with gaussian noise 209kbps 20.8851 dB

VBR CBR PSO

Conclusion

• The computer simulation results confirm that use of Particle Swarm Optimization to develop an adaptive rate control, improves the quality of picture whilst reducing data loss and communication delay, when compared to conventional MPEG video transmissions. Also, achieve an optimum level of quality of picture whilst maintaining the ZigBee target bitrate, increases the available bandwidth and reducing the data loss.

That's all I have for now,

Thank You!

References

1.  IEEE Computer Society, (August 31, 2007). IEEE Standard 802.15.4a (2007)

2. J. Xie, L.-T. Chia, Enhancement layer rate control for high bitrate SNR scalable video coding. J. Vis. Commun. Image R. 16, pp. 159–179

3. D. Isovic and G. Fohler, “Quality aware MPEG-2 stream adaptation in resource constrained systems,” in 16th Euromicro Conference on Real-time Systems (ECRTS 04), Catania, Sicily, Italy, July 2004.

4. D. Isovi´c, G. Fohler, L. Steffens, Real-time issues of MPEG-2 playout in resource constrained systems. Journal of embedded copmuting, issue 3, Jun 2004, pp. 239 - 256 (2005)

5. A. Zainaldin, I. Lambadaris, B. Nandy, Adaptive Rate Control Low bit-rate Video Transmission over Wireless Zigbee Networks. Communications, 2008. ICC '08. IEEE International Conference on May 2008, pp. 52–58 (2008)

6. M. Shafei, M. Rezaei, S. Tavakoli, F. Mohanna, A Fuzzy Bit Allocation Algorithm for Variable Bit Rate Video Coding. Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pp. 1–4 (2011)

7. J. Kennedy, R.C, Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco, CA (2004)

8. J. Kennedy, R.C, Eberhart, Particle swarm optimization, Proc. IEEE Int'l Conf. on Neural Networks, IV, pp. 1942–1948. Piscataway, NJ: IEEE Service Center (1995)

9. A. Owen, I. Harvey, Adapting Particle Swarm Optimization for Fitness Landscapes with Neutrality, 2007 IEEE Swarm Intelligence Symposium (2007)

10. C. Ji, Y. Zhang, M. Tong, S. Yang, Particle Filter with Swarm Move for Optimization, Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X, pp. 909–918 (2008)

11. R. Hassan, B. Cohanim, O. D. Weck, G. Venter., A Comparison Of Particle Swarm Optimization And The Genetic Algorithm. Structures, Structural Dynamics, and Materials Conference 2005, pp. 1—13, (2005)

References

12. H. Azarkish, H. Farahat, S. Masoud, S. H. Sarvari, Comparing the Performance of the Particle Swarm Optimization and the Genetic Algorithm on the Geometry Design of Longitudinal Fin. World Academy of Science, Engineering and Technology. vol. 1, issue 1, February 2012, pp. 1--5, (2012)

13. S.N. Sivanandam, S.N. Deepa, A Comparative Study Using Genetic Algorithm and Particle Swarm Optimization for Lower Order System Modelling. International Journal of the Computer the Internet and Management. Date 2002; pp. 1--10, (2002)

14. I.E.G. Richardson, I.E.G.: H.264 and MPEG-4 Video Compression, John Wiley & Sons Ltd, Chichester, England (2003)

15. I. E. G. Richardson, H.264 and MPEG-4 Video Compression Video Coding for Next-Generation Multimedia: Video Coding for Next-generation Multimedia - ISBN-10: 0470848375 (2003)

16. M.E.H. Pedersen, SwarmOps for Matlab - Numeric & Heuristic Optimization, November 2010, http://www.hvass-labs.org/projects/swarmops/matlab/ (2010)