Skip to main content
SpringerLink
Log in

Source localization in resource-constrained sensor networks based on deep learning

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Hereinafter, motes are referred to as nodes as it is more common in the literature of WSN.

References

  1. About lorawan | lora allience. https://lora-alliance.org/about-lorawan

  2. Albelwi S, Mahmood A (2017) A framework for designing the architectures of deep convolutional neural networks. Entropy 19(6):242

    Google Scholar 

  3. Alippi C (2014) Intelligence for embedded systems: a methodological approach. Springer, Cham

    Google Scholar 

  4. Alsheikh MA, Lin S, Niyato D, Tan H (2014) Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun Surv Tutor 16(4):1996–2018

    Google Scholar 

  5. Aquino G, Rubio JDJ, Pacheco J, Gutierrez GJ, Ochoa G, Balcazar R, Cruz DR, Garcia E, Novoa JF, Zacarias A (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8:46324–46334

    Google Scholar 

  6. Battistelli G, Chisci L, Farina A, Graziano A (2013) Consensus CPHD filter for distributed multitarget tracking. IEEE J Sel Top Signal Process 7(3):508–520

    Google Scholar 

  7. Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, New York

    MATH  Google Scholar 

  8. Bongiovanni R, Lowenberg-Deboer J (2004) Precision agriculture and sustainability. Precis Agric 5(4):359–387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa

    Article  Google Scholar 

  9. Chair Z, Varshney P (1986) Optimal data fusion in multiple sensor detection systems. IEEE Trans Aerosp Electron Syst AES–22(1):98–101

    Google Scholar 

  10. Chang KC, Saha RK, Bar-Shalom Y (1997) On optimal track-to-track fusion. IEEE Trans Aerosp Electron Syst 33(4):1271–1276

    Google Scholar 

  11. Chong CY, Mori S, Chang K (1990) Distributed multitarget multisensor tracking. In: Bar-Shalom Y (ed) Multitarget-multisensor tracking: advanced applications, chapter 8. Artech House, Norwood

    Google Scholar 

  12. Ciuonzo D, De Maio A, Salvo Rossi P (2015) A systematic framework for composite hypothesis testing of independent Bernoulli trials. IEEE Signal Process Lett 22(9):1249–1253

    Google Scholar 

  13. Ciuonzo D, Romano G, Salvo Rossi P (2013) Optimality of received energy in decision fusion over Rayleigh fading diversity MAC with non-identical sensors. IEEE Trans Signal Process 61(1):22–27

    MathSciNet  MATH  Google Scholar 

  14. Ciuonzo D, Salvo Rossi P (2014) Decision fusion with unknown sensor detection probability. IEEE Signal Process Lett 21(2):208–212

    Google Scholar 

  15. Ciuonzo D, Salvo Rossi P (2017) Distributed detection of a non-cooperative target via generalized locally-optimum approaches. Inf Fusion 36:261–274

    Google Scholar 

  16. Ciuonzo D, Salvo Rossi P, Willett P (2017) Generalized Rao test for decentralized detection of an uncooperative target. IEEE Signal Process Lett 24(5):678–682

    Google Scholar 

  17. Duffie JA, Beckman WA (2013) Solar engineering of thermal processes. Wiley, New York

    Google Scholar 

  18. Elias I, Rubio J, Cruz D, Ochoa G, Novoa J, Martinez D, Muñiz S, Balcazar R, Garcia E, Juarez C (2020) Hessian with mini-batches for electrical demand prediction. Appl Sci 10:2036

    Google Scholar 

  19. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  20. Gustafsson F, Gunnarsson F, Lindgren D (2012) Sensor models and localization algorithms for sensor networks based on received signal strength. EURASIP J Wirel Commun Netw 2012(1):1–13

    Google Scholar 

  21. Javadi SH (2016) Detection over sensor networks: a tutorial. IEEE Aerosp Electron Syst Mag 31(3):2–18

    Google Scholar 

  22. Javadi SH, Farina A (2020) Radar networks: a review of features and challenges. Inf Fusion 61:48–55. https://doi.org/10.1016/j.inffus.2020.03.005

    Article  Google Scholar 

  23. Javadi SH, Mohammadi A, Farina A (2019) Hierarchical copula-based distributed detection. Sig Process 158:100–106

    Google Scholar 

  24. Javadi S, Moosaei H, Ciuonzo D (2019) Learning wireless sensor networks for source localization. Sensors 19(3):635

    Google Scholar 

  25. Javadi SH, Peiravi A (2012) Reliable distributed detection in multi-hop clustered wireless sensor networks. IET Signal Process 6(8):743–750

    Google Scholar 

  26. Javadi SH, Peiravi A (2015) Fusion of weighted decisions in wireless sensor networks. IET Wirel Sensor Syst 5(2):97–105

    Google Scholar 

  27. Javadi SH, Mohammadi A, Farina A (2019) Serial Plackett fusion for decision making. IEEE Trans Aerosp Electron Syst (in press) (2019)

  28. Javadi SH, Peiravi A (2013) Weighted decision fusion vs. counting rule over wireless sensor networks: a realistic comparison. In: 2013 21st Iranian conf. electr. eng. (ICEE), pp 1–6

  29. Jayadeva, Khemchandani R Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5:905–910

    MATH  Google Scholar 

  30. Julier SJ (2008) Fusion without independence. In: IET seminar on target tracking and data fusion: algorithms and applications

  31. Katenka N, Levina E, Michailidis G (2008) Local vote decision fusion for target detection in wireless sensor networks. IEEE Trans Signal Process 56(1):329–338

    MathSciNet  MATH  Google Scholar 

  32. Kay SM (1998) Fundamentals of statistical signal processing, volume 2: detection theory. Prentice Hall, Upper Saddle River

    Google Scholar 

  33. Ketabchi S, Moosaei H, Razzaghi M, Pardalos PM (2019) An improvement on parametric -support vector algorithm for classification. Ann Oper Res 276:155-168

    MathSciNet  MATH  Google Scholar 

  34. Krishnamachari B, Iyengar S (2004) Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans Comput 53(3):241–250

    Google Scholar 

  35. Liu C, Fang D, Yang Z, Jiang H, Chen X, Wang W, Xing T, Cai L (2016) RSS distribution-based passive localization and its application in sensor networks. IEEE Trans Wirel Commun 15(4):2883–2895

    Google Scholar 

  36. Manyika J, Durrant-Whyte H (1994) Data fusion and sensor management: a decentralized information-theoretic approach. Ellis Horwood, Hempstead

    Google Scholar 

  37. Masazade E, Niu R, Varshney PK, Keskinoz M (2010) Energy aware iterative source localization for wireless sensor networks. IEEE Trans Signal Process 58(9):4824–4835

    MathSciNet  MATH  Google Scholar 

  38. Maxim Integrated: SOT temperature sensors with period/frequency output (2014). Rev. 1

  39. Meda-Campana JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973

    Google Scholar 

  40. Mouazen AM (2006) Soil Survey Device. International publication published under the patent cooperation treaty (PCT). World Intellectual Property Organization, International Bureau. International Publication Number: WO2006/015463; PCT/BE2005/000129; IPC: G01N21/00; G01N21/0

  41. Niu R, Varshney PK (2005) Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J Wirel Commun Netw 2005(4):462–472

    MATH  Google Scholar 

  42. Niu R, Varshney PK (2008) Performance analysis of distributed detection in a random sensor field. IEEE Trans Signal Process 56(1):339–349

    MathSciNet  MATH  Google Scholar 

  43. Niu R, Varshney PK, Cheng Q (2006) Distributed detection in a large wireless sensor network. Inf Fusion 7(4):380–394

    Google Scholar 

  44. Rossia JL, Chetehounab K, Collinc A, Morettia B, Balbia JH (2010) Simplified flame models and prediction of the thermal radiation emitted by a flame front in an outdoor fire. Combust Sci Technol 182(10):1457–1477

    Google Scholar 

  45. Rubio dJ (2009) Sofmls: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Google Scholar 

  46. Rybicki GB, Lightman AP (1979) Radiative processes in astrophysics. Wiley-Interscience, New York

    Google Scholar 

  47. Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. Adv Agron 107(C):163–215. https://doi.org/10.1016/S0065-2113(10)07005-7

    Article  Google Scholar 

  48. Texas Instruments: analog temperature sensor, RTD and precision NTC Thermistor IC (2015)

  49. Uc11-n1 lorawan sensor node. https://www.ursalink.com/en/n1-lorawan-sensor-node/

  50. Viscarra Rossel RA, Adamchuk VI, Sudduth KA, McKenzie NJ, Lobsey C (2011) Proximal soil sensing. An effective approach for soil measurements in space and time, vol 113. Elsevier Inc, Amsterdam. https://doi.org/10.1016/B978-0-12-386473-4.00010-5

    Book  Google Scholar 

  51. Viswanathan R, Thomopoulos SCA, Tumuluri R (1988) Optimal serial distributed decision fusion. IEEE Trans Aerosp Electron Syst 24(4):366–376

    Google Scholar 

  52. Viswanathan R, Varshney PK (1997) Distributed detection with multiple sensors: part Ifundamentals. Proc IEEE 85(1):54–63

    Google Scholar 

  53. Vrindts E, Mouazen AM, Reyniers M, Maertens K, Maleki MR, Ramon H, De Baerdemaeker J (2005) Management zones based on correlation between soil compaction, yield and crop data. Biosyst Eng 92(4):419–428. https://doi.org/10.1016/j.biosystemseng.2005.08.010

    Article  Google Scholar 

  54. Williams JL, Fisher JW, Willsky AS (2007) Approximate dynamic programming for communication-constrained sensor network management. IEEE Trans Signal Process 55(8):4300–4311

    MathSciNet  MATH  Google Scholar 

  55. Zuo L, Niu R, Varshney PK (2011) Conditional posterior Cramer Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Trans Signal Process 59(1):1–14

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

Authors acknowledge the financial support received from the European Commission for SIEUSOIL project (No. 818346).

Author information

Authors and Affiliations

  1. Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000, Gent, Belgium

    S. Hamed Javadi, Angela Guerrero & Abdul M. Mouazen

Authors
  1. S. Hamed Javadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela Guerrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdul M. Mouazen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Hamed Javadi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. They also declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javadi, S.H., Guerrero, A. & Mouazen, A.M. Source localization in resource-constrained sensor networks based on deep learning. Neural Comput & Applic 33, 4217–4228 (2021). https://doi.org/10.1007/s00521-020-05253-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05253-3

Keywords

Search

Navigation