A Distributed Target Localization Algorithm for Mobile Adaptive Networks

Amin Lotfzad Pak, Azam Khalili, Md Kafiul Islam, Amir Rastegarnia

Abstract


Adaptive networks with mobile nodes possess the distributed adaptation abilities in addition to the collective patterns of motion. Thus, mobile adaptive networks have been used in new applications such as modeling the biological networks and source localization. The original mobile adaptive networks needs full cooperation among the neighbor nodes meaning that each node gathers the information from all of its neighbour nodes. This strategy requires large amount of communications per iteration per node. To address this problem, in this paper we propose a mobile diffusion adaptive network with selective cooperation. In the proposed algorithm each node selects a subset of its neighbors so that its steady-state performance be  as close as possible to the traditional mobile diffusion network. Since the selective cooperation reduces the learning rate we also use affine projection algorithm (APA) as the learning rules at the nodes. Our simulation results reveal that the proposed algorithm is able to achieve the same mean-square deviation (MSD) as the original mobile adaptive network but with a lower communication per iteration per node.

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References


S. Sardellitti, and P. Di Lorenzo, Distributed

detection and estimation in wireless sensor networks,

in E-Reference Signal Processing, R.

Chellapa and S. Theodoridis, Eds. Elsevier,

, pp. 329-408.

R. P. Yadav, P. V. Varde, P. S. V. Nataraj et

al. Model-based tracking for agent-based control

systems in the case of sensor failures, International

Journal of Automation and Computing,

, 9(6): 561-569.

Chao-Lei Wang, Tian-Miao Wang, Jian-Hong

Liang et al. Bearing-only visual SLAM for small

unmanned aerial vehicles in GPS-denied environments,

International Journal of Automation

and Computing, 2013, 10(5): 387-396.

S. Datta, K. Bhaduri, C. Giannella, R. Wolff,

and H. Kargupta, “Distributed data mining in

peer-to-peer networks,” IEEE Internet Computing.,

vol. 10, no. 4, pp. 1826, 2006.

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli,

“Fog computing and its role in the internet

of things,” in Proceedings of the first edition of

the MCC workshop on Mobile cloud computing.

ACM, 2012, pp. 1316.

J. McLurkin and D. Yamins, “Dynamic Task Assignment

in Robot Swarms,” in Robotics: Science

and Systems, vol. 8, 2005.

G. Kowadlo and R. Russell, “Robot odor localization:

a taxonomy and survey,” The International

Journal of Robotics Research, vol. 27, no.

, pp. 869894, Aug. 2008.

A. Dhariwal, G. Sukhatme, and A. Requicha,

“Bacterium-inspired robots for environmental

monitoring,” in IEEE International Conference

on Robotics and Automation, 2004, pp.

S. Squartini, D. Liu, F. Piazza, D. Zhao, and

H. He, “Computational Energy Management in

Smart Grids,” Neurocomputing, vol. 170, pp. 267

, 2015.

Y. Xia, S. C. Douglas, and D. P. Mandic, “Adaptive

frequency estimation in smart grid applications:

Exploiting noncircularity and widely linear

adaptive estimators,” Signal Processing Magazine,

IEEE, vol. 29, no. 5, pp. 4454, 2012.

I. Schizas, G. Mateos, and G. Giannakis, “Distributed

LMS for consensus-based in-network

adaptive processing,” Signal Processing, IEEE

Transactions on, vol. 57, no. 6, pp. 2365–2382,

S. Kar and J. Moura, “Convergence rate analysis

of distributed gossip (linear parameter) estimation:

Fundamental limits and tradeoffs,” Selected

Topics in Signal Processing, IEEE Journal

of, vol. 5, no. 4, pp. 674–690, Aug 2011.

J. Chen and A. H. Sayed, “Diffusion adaptation

strategies for distributed optimization and learning

over networks,” IEEE Trans. Signal Process.,

vol. 60, no. 8, pp. 4289–4305, August 2012.

N. Bogdanovic, J. Plata-Chaves, and

K. Berberidis, “Distributed incremental-based

lms for node-specific parameter estimation

over adaptive networks,” in Acoustics, Speech

and Signal Processing (ICASSP), 2013 IEEE

International Conference on, May 2013, pp.

–5429.

C. G. Lopes and A. H. Sayed, “Incremental

adaptive strategies over distributed networks,”

IEEE Trans. Signal Processing, vol. 55, no. 8,

pp. 4064–4077, August 2007.

S. Ram, A. Nedic, and V. Veeravalli, “Stochastic

incremental gradient descent for estimation in

sensor networks,” in Signals, Systems and Computers,

ACSSC 2007. Conference Record

of the Forty-First Asilomar Conference on, Nov

, pp. 582–586.

L. Li and J. A. Chambers, “A new incremental

affine projection-based adaptive algorithm for

distributed networks,” Signal Processing, vol. 88,

no. 10, pp. 2599 – 2603, 2008.

N. Takahashi and I. Yamada, “Incremental

adaptive filtering over distributed networks using

parallel projection onto hyperslabs,” IEICE

Technical Report, vol. 108, pp. 17–22, 2008.

A. Khalili, M. Tinati, and A. Rastegarnia, “An

incremental block lms algorithm for distributed

adaptive estimation,” in Communication Systems (ICCS), 2010 IEEE International Conference

on, Nov 2010, pp. 493–496.

M. Saeed and A. U. H. Sheikh, “A new lms strategy

for sparse estimation in adaptive networks,”

in Personal Indoor and Mobile Radio Communications

(PIMRC), 2012 IEEE 23rd International

Symposium on, Sept 2012, pp. 1722–1733.

N. Bogdanovic, J. Plata-Chaves, and

K. Berberidis, “Distributed incremental-based

lms for node-specific parameter estimation

over adaptive networks,” in Acoustics, Speech

and Signal Processing (ICASSP), 2013 IEEE

International Conference on, May 2013, pp.

–5429.

W. Bazzi, A. Rastegarnia, and A. Khalili,

“A quality-aware incremental lms algorithm for

distributed adaptive estimation,” International

Journal of Automation and Computing, vol. 11,

no. 6, pp. 676–682, 2014.

Y. Liu and W. K. Tang, “Enhanced incremental

LMS with norm constraints for distributed innetwork

estimation,” Signal Processing, vol. 94,

no. 0, pp. 373 – 385, 2014.

A. Khalili, A. Rastegarnia, W. Bazzi, and

Z. Yang, “Derivation and analysis of incremental

augmented complex least mean square algorithm,”

Signal Processing, IET, vol. 9, no. 4, pp.

–319, 2015.

A. Rastegarnia, A. Khalili, W. Bazzi, and

S. Sanei, “An incremental LMS network with reduced

communication delay,” Signal, Image and

Video Processing, pp. 1–7, 2015.

W. M. Bazzi, A. Rastegarnia, and A. Khalili, A

Quality-aware Incremental LMS Algorithm for

Distributed Adaptive Estimation, International

Journal of Automation and Computing, vol. 11,

no. 6, pp. 676-682, 2015.

C. G. Lopes and A. H. Sayed, “Diffusion leastmean

squares over adaptive networks: Formulation

and performance analysis,” IEEE Trans.

on Signal Process., vol. 56, no. 7, pp. 3122–3136,

July 2008.

O. Gharehshiran, V. Krishnamurthy, and

G. Yin, “Distributed energy-aware diffusion least

mean squares: Game-theoretic learning,” Selected

Topics in Signal Processing, IEEE Journal

of, vol. 7, no. 5, pp. 821–836, Oct 2013.

S. Kumar, A. Sahoo, and D. Acharya, “Block diffusion

adaptation over distributed adaptive networks

under imperfect data transmission,” in India

Conference (INDICON), 2014 Annual IEEE,

Dec 2014, pp. 1–5.

R. Arablouei, S. Werner, Y.-F. Huang, and

K. Dogancay, “Distributed least mean-square estimation

with partial diffusion,” Signal Processing,

IEEE Transactions on, vol. 62, no. 2, pp.

–484, Jan 2014.

P. Di Lorenzo and S. Barbarossa, “Distributed

least mean squares strategies for sparsity-aware

estimation over gaussian markov random fields,”

in Acoustics, Speech and Signal Processing

(ICASSP), 2014 IEEE International Conference

on, May 2014, pp. 5472–5476.

F. Wen, “Diffusion lmp algorithm with adaptive

variable power,” Electronics Letters, vol. 50,

no. 5, pp. 374–376, Feb 2014.

Y. Zhu, A. Jiang, H. K. Kwan, and K. He, “Distributed

sensor network localization using combination

and diffusion scheme,” in Digital Signal

Processing (DSP), 2015 IEEE International

Conference on, July 2015, pp. 1156–1160.

R. Arablouei, S. Werner, K. Doanay, and Y.-F.

Huang, “Analysis of a reduced-communication

diffusion {LMS} algorithm,” Signal Processing,

vol. 117, pp. 355 – 361, 2015.

J. Fernandez-Bes, L. A. Azpicueta-Ruiz,

J. Arenas-Garca, and M. T. Silva, “Distributed

estimation in diffusion networks using

affine least-squares combiners,” Digital Signal

Processing, vol. 36, pp. 1 – 14, 2015.

X. Zhao, S-Y. Tu, and A. H. Sayed, “Diffusion

adaptation over networks under imperfect information

exchange and non-stationary data” IEEE

Transactions on Signal Processing, vol. 60, no. 7,

pp. 34603475, July 2012.

S.-Y. Tu and A. Sayed, “Diffusion strategies outperform

consensus strategies for distributed estimation

over adaptive networks,” Signal Processing,

IEEE Transactions on, vol. 60, no. 12, pp.

–6234, 2012.

X. Zhao and A. Sayed, “Performance limits for

distributed estimation over lms adaptive networks,”

Signal Processing, IEEE Transactions

on, vol. 60, no. 10, pp. 5107–5124, Oct 2012.

F. Cattivelli and A. Sayed, “Diffusion lms strategies

for distributed estimation,” Signal Processing,

IEEE Transactions on, vol. 58, no. 3, pp.

–1048, March 2010.

S. Y. Tu and A. H. Sayed, “Mobile adaptive

networks,” Selected Topics on Signal Processing,

vol. 5, no. 4, pp. 649–664, August 2011.

M. Lin, M. Murthi, and K. Premaratne, “Mobile

adaptive networks for pursuing multiple targets,”

in Acoustics, Speech and Signal Processing

(ICASSP), 2015 IEEE International Conference

on, April 2015, pp. 3217–3221.

F. Cattivelli and A. H. Sayed, “Modeling bird

flight formations using diffusion adaptation,”

IEEE Trans. Signal Process, vol. 59, no. 5, pp.

–2051, May 2011.

J. Li and A. H. Sayed, “Modeling bee swarming

behavior through diffusion adaptation with

asymmetric information sharing,” EURASIP

Journal on Advances in Signal Processing, vol.

, no. 18, January 2012.

W. Ma, Z. Sun, J. Li, M. Song, and X. Lang, “An improved artificial bee colony algorithm based

on the strategy of global reconnaissance,” Soft

Computing, pp. 1–33, 2015.

J.-G. Li, Q.-H. Meng, Y. Wang, and M. Zeng,

“Odor source localization using a mobile robot

in outdoor airflow environments with a particle

filter algorithm,” Autonomous Robots, vol. 30,

no. 3, pp. 281–292, 2011.

A. Khalili, A. Rastegarnia, M. Islam, and

Z. Yang, “A bio-inspired cooperative algorithm

for distributed source localization with mobile

nodes,” in Engineering in Medicine and Biology

Society (EMBC), 2013 35th Annual International

Conference of the IEEE, July 2013, pp.

–3518.

A. Pak, S. Aghazadeh, A. Rastegarnia, and

A. Khalili, “Target position estimation with mobile

adaptive network with selective cooperation,”

in Computer Architecture and Digital Systems

(CADS), 2013 17th CSI International Symposium

on, Oct 2013, pp. 107–110.


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