Skip to main content

Scope of Machine Learning in Mobile Wireless Sensor Networks

  • Conference paper
  • First Online:
Emerging Technologies in Data Mining and Information Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 491))

  • 720 Accesses

Abstract

This work highlights the scope machine learning approaches in Mobile Wireless Sensor Networks. As Mobile Wireless Sensor Network faces numerous challenges in terms of energy conservation, data collection and aggregation, fault tolerance, QoS. Sink Mobility, etc. Machine learning is the branch of Artificial intelligence used to analyse data for making predictions, so as to get the optimized results. Here, work shows how the machine learning approaches can be used in sensor networks to improve network performance by extending lifetime, data collection and aggregation, handling mobility of sink node, QOS, fault tolerance, etc.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gupta K, Garg A, Singh A (2019) Cluster based energy efficient routing protocol (EERP) for mobile wireless sensor network. Int J Recent Technol Eng (IJRTE) 8

    Google Scholar 

  2. Popovici E (2016) An energy aware adaptive sampling algorithm for energy harvesting WSN with energy hungry sensors. Sensors 16(4):448

    Article  Google Scholar 

  3. Deniz F, Bagci H, Korpeoglu I, Yaz A (2016) An adaptive, energy aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks. Ad Hoc Netw 44:104–117

    Google Scholar 

  4. Peng B, Li L (2015) An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cognit Neurodyn 9(2):249–256

    Google Scholar 

  5. Alduraibi F, Lasla N, Younis M (2016) Coverage-based node placement optimization in wireless sensor network with linear topology. In: Proceedings on IEEE international conference on communications (ICC), pp 1–6

    Google Scholar 

  6. [ 6]Marfievici R, Murphy AL, Picco GP, Ossi F, Cagnacci F (2013) How environmental factors impact outdoor wireless sensor networks: a case study. In: Proceedings of IEEE 10th international conference on mobile ad-hoc and sensor systems, pp 565–573

    Google Scholar 

  7. Aziz AA, Sekercioglu YA, Fitzpatrick P, Ivanovich M (2013) A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. IEEE Commun Surv Tuts 15(1):121–144

    Google Scholar 

  8. Zhang Y, Zhang X, Ning S, Gao J, Liu Y (2019) Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks. IEEE Access 7:55873–55884

    Google Scholar 

  9. Mahapatra RK, Shet NSV (2018) Topology control in wireless sensor networks: a survey. In Innovations in electronics and communication engineering. Springer, Singapore, pp 335–346

    Google Scholar 

  10. Gupta K, Singh A, Juneja D (2015) An improved cluster head election algorithm for mobile wireless sensor networks. JNCET 2015

    Google Scholar 

  11. Nath MP, Pandey P, Somu K, Amalraj P (2018) Artificial intelligence and machine learning: the emerging milestones in software development. Int J Res Sci Innov 5(9):36–44

    Google Scholar 

  12. Nath MP, Priyadarshini SBB, Mishra D, Borah S (2020) A comprehensive study of contemporary IoT technologies and varied machine learning (ML) schemes. In: Proceedings of the international conference on computing and communication (IC32020), 2020, Sikkim, India, pp 623–634

    Google Scholar 

  13. Mohanty SN, Lydia EL, Elhoseny M, Al Otaibi MMG, Shankare K (2020) Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks. Phys Commun 2020

    Google Scholar 

  14. Lakshmanaprabu SK, Mohanty SN, Krishnamoorthy S, Uthayakumar J, Shankar K (2019) Online clinical decision support system using optimal deep neural networks. Appl Soft Comput J 81(4):105–116

    Google Scholar 

  15. Abu-Mostafa YS, Magdon-Ismail M, Lin H-T (2012) Learning from data. AMLBook

    Google Scholar 

  16. Zhao W, Su S, Shao F (2018) Improved DV-hop algorithm using locally weighted linear regression in anisotropic wireless sensor networks. Wirel Personal Commun 98(4):3335–3353

    Article  Google Scholar 

  17. Kang J, Park YJ, Lee J, Wang SH, Eom DS (2018) Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans Ind Electron 65(5):4279–4289

    Article  Google Scholar 

  18. Khan F, Memon S, Jokhio SH (2016) Support vector machine-based energy aware routing in wireless sensor networks. In: 2016 2nd international conference on robotics and artificial intelligence (ICRAI), 2016, pp 1–4

    Google Scholar 

  19. Jafarizadeh V, Keshavarzi A, Derikvand T (2017) Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks. Wirel Netw 23(3):779–785

    Article  Google Scholar 

  20. Braca P, Willett P, LePage KD, Marano S, Matta V (2014) Bayesian tracking in under-water wireless sensor networks with port-starboard ambiguity. IEEE Trans Signal Process 62(7):1864–1878

    Article  MathSciNet  Google Scholar 

  21. Gispan L, Leshem A, Be'ery Y (2017) Decentralized estimation of regression coefficients in sensor networks. Digit Signal Process 68 (2017) 16–23.

    Google Scholar 

  22. De Paola A, Ferraro P, Gaglio S, Re GL, Das SK (2017) An adaptive Bayesian system for context-aware data fusion in smart environments. IEEE Trans Mob Comput 16(6):1502–1515

    Article  Google Scholar 

  23. Li Y, Parker LE (2014) Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks. Inf Fusion 15:64–79

    Article  Google Scholar 

  24. Xie M, Hu J, Han S, Chen H-H (2013) Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 24(8):1661–1670

    Article  Google Scholar 

  25. Gholipour M, Haghighat AT, Meybodi MR (2017) Hop-by-Hop congestion avoidance in wireless sensor networks based on genetic support vector machine. Neurocomputing 223:63–76

    Article  Google Scholar 

  26. Tang T, Liu H, Song H, Peng B (2016) Support vector machine based range-free localization algorithm in wireless sensor network. In: International conference on machine learning and intelligent communications, 2016, pp 150–158

    Google Scholar 

  27. Kim W, Stankovi MS, Johansson KH, Kim HJ (2015) A distributed support vector machine learning over wireless sensor networks. IEEE Trans. Cybern 45(11):2599–2611

    Article  Google Scholar 

  28. Sun QY, Sun YM, Liu XJ, Xie YX, Chen XG (2018) Study on fault diagnosis algorithm in WSN nodes based on RPCA model and SVDD for multi-class classification. Cluster Comput 1–15

    Google Scholar 

  29. Li J, Liu D (2016) An energy-aware distributed clustering routing protocol for energy harvesting wireless sensor networks. In: 2016 IEEE/CIC international conference on communications in China (ICCC)

    Google Scholar 

  30. Deepshikha, Arora P, Varsha (2017) Enhanced NN based RZ LEACH using hybrid ACO/PSO based routing for WSNs. In: IEEE 2017 8th international conference on computing, communication and networking technologies (ICCCNT)

    Google Scholar 

  31. Yang B, Lei Y, Yan B (2016) Distributed multi-human location algorithm using Naive Bayes classifier for a binary pyroelectric infrared sensor tracking system. IEEE Sens J 16(1):216–223

    Article  Google Scholar 

  32. Shu J, Liu S, Liu L, Zhan L, Hu G (2017) Research on link quality estimation mechanism for wireless sensor networks based on support vector machine. Chin J Electr 26(2):377–384

    Article  Google Scholar 

  33. Alotaibi B, Elleithy K (2016) A new MAC address spoofing detection technique based on random forests. Sensors 16(3):1–14

    Article  Google Scholar 

  34. Elghazel W, Medjaher K, Zerhouni N, Bahi J, Farhat A, Guyeux C, Hakem M (2015) Random forests for industrial device functioning diagnostics using wireless sensor networks. In: Aerospace conference, 2015 IEEE, 2015, pp 1–9

    Google Scholar 

  35. Jain B, Brar G, Malhotra J (2018) EKMT-k-means clustering algorithmic solution for low energy consumption for wireless sensor networks based on minimum mean distance from base station. In: Networking communication and data knowledge engineering. Springer, pp 113–123

    Google Scholar 

  36. Neamatollahi P, Abrishami S, Naghibzadeh M, Moghaddam MHY, Younis O (2018) Hierarchical clustering-task scheduling policy in cluster-based wireless sensor net- works. IEEE Trans Ind Inf 14(5):1876–1886

    Article  Google Scholar 

  37. Zhang R, Pan J, Xie D, Wang F (2016) NDCMC: a hybrid data collection approach for large-scale WSNs using mobile element and hierarchical clustering. IEEE Internet Things J 3(4):533–543

    Article  Google Scholar 

  38. Zhang R, Pan J, Liu J, Xie D (2015) A hybrid approach using mobile element and hierarchical clustering for data collection in WSNs. In: Proceedings of IEEE wireless communications and networking conference (WCNC), 2015, pp 1566–1571

    Google Scholar 

  39. Li X, Ding S, Li Y (2017) Outlier suppression via non-convex robust PCA for efficient localization in wireless sensor networks. IEEE Sens J 17(21):7053–7063

    Article  Google Scholar 

  40. Morell A, Correa A, Barceló M, Vicario JL (2016) Data aggregation and principal component analysis in WSNs. IEEE Trans Wirel Commun 15(6):3908–3919

    Google Scholar 

  41. Wu M, Tan L, Xiong N (2016) Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Inf Sci 329:800–818

    Article  Google Scholar 

  42. Oikonomou P, Botsialas A, Olziersky A, Kazas I, Stratakos I, Katsikas S, Di-mas D, Mermikli K, Sotiropoulos G, Goustouridis D et al (2016) A wireless sensing system for monitoring the workplace environment of an industrial installation. Sens Actuators B 224:266–274

    Article  Google Scholar 

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

    Google Scholar 

  44. Wang J, Gao Y, Liu W, Wu W, Lim SJ (2019) An asynchronous clustering and mobile data gathering schema based on timer mechanism in wireless sensor networks. Comput Mater Continua 58(3): 711–725

    Google Scholar 

  45. Wang J, Gao Y, Zhou C, Sherratt S, Wang L (2020) Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs. Comput Mater Continua 62(2):695–711

    Google Scholar 

  46. Feng Z, Fu J, Du D, Li F, Sun S (2017) A new approach of anomaly detection in wireless sensor networks using support vector data description. Int J Distrib Sens Netw 13(1):1–14

    Article  Google Scholar 

  47. Gupta K, Garg A, Singh A (2019) An efficient approach to secure mobile wireless sensor network from node replication attack. J Comput Theor Nanosci 16(9):3885–3891

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kavita Gupta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, K., Bansal, S., Khurana, A. (2023). Scope of Machine Learning in Mobile Wireless Sensor Networks. In: Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Piuri, V. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4193-1_52

Download citation

Publish with us

Policies and ethics