Abstract
In this chapter, distributed parameter estimation problems are discussed with special emphasis on target localization in WSNs. Following the structure outlined in Chap. 2 and followed in Chap. 3, fundamental limits of distributed estimation with Byzantines are first presented. After characterizing these limits, several countermeasures are discussed, among which some are specific to the estimation framework as they leverage the structure of the parameter space. Specifically, recently developed error-correcting codes-based mitigation scheme is discussed.
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
Notes
- 1.
Here, the set of honest sensors is first player, and the set of Byzantines is the second player.
- 2.
Interested reader is referred to Sect. 2.2 of [17] for further information on Euler–Lagrange equation and variational calculus.
- 3.
It is assumed that N is divisible by \(M^k\) for \(k=0,1,\ldots , \log _M N-1\).
References
Aurenhammer F (1991) Voronoi diagram-A survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–405. https://doi.org/10.1145/116873.116880
Berkhin P (2002) Survey of clustering data mining techniques. Technical report. Accrue Software Inc, San Jose, CA, USA
Chen Y, Kar S, Moura JMF (2018) Resilient distributed estimation through adversary detection. IEEE Trans Signal Process 66(9):2455–2469. https://doi.org/10.1109/TSP.2018.2813330
Doucet A, Wang X (2005) Monte Carlo methods for signal processing: a review in the statistical signal processing context. IEEE Signal Process Mag 22(6):152–170. https://doi.org/10.1109/MSP.2005.1550195
Fudenberg D, Tirole J (1991) Game theory. MIT Press, Cambridge, MA
Kar S, Moura JMF (2013) Consensus+innovations distributed inference over networks. IEEE Signal Process Mag 30(3):99–109. https://doi.org/10.1109/MSP.2012.2235193
Li D (2003) Hu YH (2003) Energy-based collaborative source localization using acoustic micro-sensor array. EURASIP J Appl Signal Process 4:331–337. https://doi.org/10.1155/S1110865703212075
Masazade E, Niu R, Varshney PK (2010) Energy aware iterative source localization schemes for wireless sensor networks. IEEE Trans Signal Process 58(9):4824–4835. https://doi.org/10.1109/TSP.2010.2051433
Meesookho C, Mitra U, Narayanan S (2008) On energy-based acoustic source localization for sensor networks. IEEE Trans Signal Process 56(1):365–377. https://doi.org/10.1109/TSP.2007.900757
Niu R, Varshney PK (2006) Target location estimation in sensor networks with quantized data. IEEE Trans Signal Process 54(12):4519–4528. https://doi.org/10.1109/TSP.2006.882082
Ozdemir O, Niu R, Varshney PK (2009) Channel aware target localization with quantized data in wireless sensor networks. IEEE Trans Signal Process 57(3):1190–1202. https://doi.org/10.1109/TSP.2008.2009893
Rawat A, Anand P, Chen H, Varshney PK (2011) Collaborative spectrum sensing in the presence of Byzantine attacks in cognitive radio networks. IEEE Trans Signal Process 59(2):774–786. https://doi.org/10.1109/TSP.2010.2091277
Ribeiro A, Giannakis GB (2006) Bandwidth-constrained distributed estimation for wireless sensor networks- Part I: Gaussian case. IEEE Trans Signal Process 54(3):1131–1143. https://doi.org/10.1109/TSP.2005.863009
Shen X, Hu YH (2005) Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor network. IEEE Trans Signal Process 53(1):44–53. https://doi.org/10.1109/TSP.2004.838930
Van Trees HL (1968) Detection, estimation and modulation theory, vol 1. Wiley, New York, NY
Van Trees HL, Bell KL (2007) Bayesian bounds for parameter estimation and nonlinear filtering/tracking. Wiley-IEEE, Hoboken, NJ
vanBrunt B (2004) The calculus of variations. Springer, New York, NY
Vempaty A, Agrawal K, Chen H, Varshney PK (2011) Adaptive learning of Byzantines’ behavior in cooperative spectrum sensing. In: Proceedings of the 2011 IEEE Wireless Communications and Networking Conference (WCNC), pp 1310–1315. https://doi.org/10.1109/WCNC.2011.5779320
Vempaty A, Ozdemir O, Agrawal K, Chen H, Varshney PK (2013) Localization in wireless sensor networks: Byzantines and mitigation techniques. IEEE Trans Signal Process 61(6):1495–1508. https://doi.org/10.1109/TSP.2012.2236325
Vempaty A, Han YS, Varshney PK (2014) Target localization in wireless sensor networks using error correcting codes. IEEE Trans Inf Theory 60(1):697–712. https://doi.org/10.1109/TIT.2013.2289859
Wang TY, Han YS, Varshney PK, Chen PN (2005) Distributed fault-tolerant classification in wireless sensors networks. IEEE J Sel Areas Commun 23(4):724–734. https://doi.org/10.1109/JSAC.2005.843541
Wang TY, Han YS, Chen B, Varshney PK (2006) A combined decision fusion and channel coding scheme for distributed fault-tolerant classification in wireless sensors networks. IEEE Trans Wireless Commun 5(7):1695–1705. https://doi.org/10.1109/TWC.2006.1673081
Yao C, Chen PN, Wang TY, Han YS, Varshney PK (2007) Performance analysis and code design for minimum Hamming distance fusion in wireless sensor networks. IEEE Trans Inf Theory 53(5):1716–1734. https://doi.org/10.1109/TIT.2007.894670
Zhang J, Blum RS, Lu X, Conus D (2015) Asymptotically optimum distributed estimation in the presence of attacks. IEEE Trans Signal Process 63(5):1086–1101. https://doi.org/10.1109/TSP.2014.2386281
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Vempaty, A., Kailkhura, B., Varshney, P.K. (2018). Distributed Estimation and Target Localization. In: Secure Networked Inference with Unreliable Data Sources. Springer, Singapore. https://doi.org/10.1007/978-981-13-2312-6_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-2312-6_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2311-9
Online ISBN: 978-981-13-2312-6
eBook Packages: Computer ScienceComputer Science (R0)