Abstract
In literature, distributed LMS algorithm for finite impulse response (FIR) systems has been studied as it is stable inherently. But infinite impulse response (IIR) systems are used in wireless sensor network (WSN) based application. Hence, adaptive IIR filter is assumed at each sensor node to estimate the parameters in the network. Diffusion mode of cooperation among the sensor nodes is incorporated. By doing this, the probability of trapping in local minima, which is the major drawback in IIR system, is reduced if each node is well connected to more number of neighbors. But the connectivity is reduced in sparse sensor network. Therefore, IIR multihop diffusion LMS (DLMS) algorithm is incorporated to the sparse sensor network. It is seen from the results of simulation that 2-hop DLMS provides best results by providing least steady-state mean square error and mean square deviation with minimum number of iterations. In order to minimize the communication overhead, block LMS is proposed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li Y, Thai MTE (2008) Wireless sensor networks and applications. Springer
Sayed AH (2014) Adaptive networks. Proc IEEE 102(4):460–497
Nayak M, Panigrahi T, Sharma R (2015) Distributed estimation using multi-hop adaptive diffusion in sparse wireless sensor networks. In: International Conference on Microwave, Optical and Communication Engineering (ICMOCE), Dec 2015, pp 318–321
Kong J-T, Lee J-W, Kim S-E, Shin S, Song W-J (2017) Diffusion LMS algorithms with multi combination for distributed estimation: formulation and performance analysis. Dig Signal Proc 71(Supplement C), 117–130
Cattivelli FS, Sayed AH (2010) Diffusion LMS strategies for distributed estimation. IEEE Trans Signal Process 58(3):1035–1048
Panigrahi T, Pradhan PM, Panda G, Mulgrew B (2012) Block least-mean square algorithm over distributed wireless sensor network. J Comput Netw Commun., vol. 2012, Hindawi Publishing Corporation
Shynk JJ (1989) Adaptive IIR filtering. IEEE ASSP Mag 6(2):4–21
Agrawal N, Kumar A, Bajaj V, Singh GK (2017) High order stable infinite impulse response filter design using cuckoo search algorithm. Int J Autom Comput 14(5):589–602
Hu W, Tay WP (2015) Multi-hop diffusion LMS for energy-constrained distributed estimation. IEEE Trans Signal Process 63(15):4022–4036
Jerew O, Blackmore K (2014) Estimation of hop count in multi-hop wireless sensor networks with arbitrary node densitym. Int J Wireless Mob Comput (IJWMC) 7(3):207–216
Schizas I, Mateos G, Giannakis G (2009) Distributed LMS for consensus-based in-network adaptive processing. IEEE Trans Signal Process 57(6):2365–2382
Nedic A, Ozdaglar A (2009) Distributed subgradient methods for multi-agent optimization. IEEE Trans Autom Control 54(1):48–61
Khalili, Rastegarnia A (2016) Tracking analysis of augmented complex least mean square algorithm. Int J Adapt Control Signal Proc 30(1):106–114
Arablouei R, Werner S, Huang Y-F, Dogancay K (2014) Distributed least mean-square estimation with partial diffusion. IEEE Trans Signal Process 62(2):472–484
Gharehshiran ON, Krishnamurthy V, Yin G (2013) Distributed energy-aware diffusion least mean squares: game-theoretic learning. IEEE J Select Top Signal Proc 7(5):821–836
Xiao L, Boyd S, Lall S (2005) A scheme for robust distributed sensor fusion based on average consensus. In: Proceedings of 4th international symposium on information processing in sensor networks, Loss Angles, CA, pp. 63–70, Apr 2005
Scarpiniti M, Comminiello D, Parisi R, Uncini A (2015) Nonlinear system identification using IIR spline adaptive filters. Signal Process 108:30–35
Majhi B, Panda G (2013) Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization. Dig Signal Proc 23(4):1303–1313
Panda G, Mulgrew B, Cowan CFN (1986) A self-orthogonalizing efficient block adaptive filter. IEEE Trans Acoust Speech, Signal Process 34(6): 1573–1582, Dec 1986
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dash, M., Panigrahi, T., Sharma, R. (2020). Distributed Estimation of IIR System’s Parameters in Sensor Network by Multihop Diffusion LMS Algorithm. In: Mohanty, M., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2774-6_3
Download citation
DOI: https://doi.org/10.1007/978-981-15-2774-6_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2773-9
Online ISBN: 978-981-15-2774-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)