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Next-Generation WSN for Environmental Monitoring Employing Big Data Analytics, Machine Learning and Artificial Intelligence

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Evolutionary Computing and Mobile Sustainable Networks

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 53))

Abstract

A worldwide network of wireless sensors is used to monitor dynamic environmental changes with respect to time. Therefore, the data provided by these sensor networks are crucial for collecting specific information; hence data analytics is essential in such networks. For effective utilization of gathered data, big data analytics can be one of the prominent solutions since the data plays an important part in machine learning allowing the WSN to adapt the dynamic changes in environment to save cost and efforts of redesigning the present WSN. In this paper we present the advances of WSN to further develop the next-generation wireless sensor network by employing software-defined network (SDN), big data analytics, machine learning and artificial intelligence tool along with its benefits and challenges. We also discuss the software-defined wireless sensor network (SDWSN) and the possibility of application of artificial intelligence in it to meet the challenges of SDWSN and its advantages. And finally, we have discussed different problems associated with WSN network specifically for environmental monitoring and their respective solutions using different machine learning paradigms and how efficiently the adoption of big data analytics in ML and AI plays an important role to serve the improved performance requirements.

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References

  1. Alsheikh MA, Lin S, Niyato D, Hwee-Pink T (2014) Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun Surv Tutor 16:4

    Google Scholar 

  2. Matlou OG, Abu-Mahfouz AM (2017) Utilizing artificial intelligence in software defined wireless sensor network. In: IECON-43rd annual conference of the IEEE industrial electronics society, Beijing China

    Google Scholar 

  3. Abu-Mahfouz AM, Olwa T, Kurien A, Munda JL, Djouani K (2015) Towards developing a distributed autonomous energy management system (DAEMS). In: Proceedings of the IEEE AFRICON 2015 conference on green innovation for african renaissancce, pp 1–6

    Google Scholar 

  4. Dongbaare P, Chowdhury SP, Olwal TO, Abu-Mahfouz AM (2016) Smart energy management system based on an automated distributed load limiting mechanism and multi-power switching technique. In: Proceedings of the 51st International universities power engineering conference

    Google Scholar 

  5. Mudumbe MJ, Abu-Mahfouz AM (2015) Smart water meter system for user-centric consumption measurement. In: Proceedings of the IEEE international conference on Industrial Informatics, pp 9993–9998

    Google Scholar 

  6. Abu-Mahfouz AM, Haman Y, Page PR, Djouani K (2016) Real time dynamic hydraulic model for potable water loss reduction. Proc Eng 154(7):99–106

    Google Scholar 

  7. Cheng B, Cui L, Jia W, Zhao W, Gerhard PH (2016) Multiple region of interest coverage in camera sensor networks for tele-intensive care units. IEEE Trans Ind Inform 12(6):2331–2341

    Google Scholar 

  8. Silva B, Fisher RM, Kumar A, Hancke GP (2015) Experimental link quality characterization of wireless sensor networks for underground monitorig. IEEE Trans Ind Inform 11(5):1099–1110

    Google Scholar 

  9. Phala KSE, Kumar A, Hancke GP (2016) Air quality monitoring system based on ISO/IEC/IEEE 21451 standards. IEEE Sens J 16(12):5037–5045

    Google Scholar 

  10. Abu-Mahfouz AM, Hancke GP (2013) Evaluating ALWadHA for providing secure localization for wireless sensor network. In: IEEE AFRICON conference, pp 501–505

    Google Scholar 

  11. Ntuli N, Abu-Mahfouz AM (2016) A simple security architecture for smart water management system. Proc Comput Sci 83(4):1164–1169

    Google Scholar 

  12. Louw J, Niezen G, Ramotsoela TD, Abu-Mahfouz AM (2016) A key distribution scheme using elliptic curve cryptography in wirelesssensor networks. In: Proceeding of the 14th IEEE international conference on industrial Informatics, pp 1166–1170

    Google Scholar 

  13. Abu-Mahfouz AM, Hancke GP (2017) ALWadHA localization algorithm: yet more energy efficient. IEEE Access 5(5):6661–6667

    Google Scholar 

  14. Abu-Mahfouz AM, Hancke GP (2017) Localised information fusion techniques for location discovery in wireless sensor networks. Int J Sens Netw

    Google Scholar 

  15. Silva B, Hancke GP (2016) IR-UWB-Based non-line-of-sight identification in harsh environments: principles and challenges. IEEE Trans Ind Inform 12(3):1188–1195

    Google Scholar 

  16. Chiwewe TM, Mbuya CF, Hancke GP (2015) Using cognitive radio for interference resistant industrial wireless sensor networks: an overview. IEEE Trans Ind Inform 11(6):1466–1481

    Google Scholar 

  17. Kaur H, Sahore S (2016) A survey on wireless sensor network (wsn) security using AI methods. Int J Latest Trends Eng Technol 7(4):234–239

    Google Scholar 

  18. Kobo HI, Abu-Mahfouz AM, Hancke GP (2017) A survey on software defined wireless networks: challenges and design requirements. IEEE Access 5(1):1872–1899

    Google Scholar 

  19. Modieginyane KM, Letswamotse BB, Malekian R, Abu-Mahfouz AM (2017) Software defined wireless sensor network application opportunities for efficient network management: a survey. Comput Electr, Eng

    Google Scholar 

  20. Xiang W, Wang N, Zhou Y (2016) An energy efficient routing algorithm for software defined wireless sensor networks. IEEE Sens J 16(20):7393–7400

    Google Scholar 

  21. Ayodele TO (2010) Introduction to machine learning, in new advances in machine learning. InTech, Rijeka

    Google Scholar 

  22. Ramesh Babu KR, Suja GJ, Samuel P, Jos S (2015) Performance analysis of Big data gathering in wireless sensor network using an EM based clustering scheme. In: IEEE Fifth international conference on advances in computing and communications

    Google Scholar 

  23. Ndiaya M, Hancke GP, Abu-Mahfouz (2017) Software defined networking for improved wireless sensor networking for improved wireless sensor network management: a survey. Sensors 17(5):1031, 1–32

    Google Scholar 

  24. Duan Y, Luo Y, Li W, Pace P, Fortino G (2018) Software defined wireless sensor networks: a review. In: Proceeding of the 2018 IEEE 22nd international conference on computer supported cooperative work in design

    Google Scholar 

  25. Luo T, Tan H, Quek TQS (2012) Sensor open flow: enabling software defined wireless sensor networks. IEEE Commun Lett 16(11):1896–1899

    Google Scholar 

  26. De Gante A, Aslan M, Matrawy A (2014) Smart wireless sensor network management based on software defined networking. In: IEEE 27th Biennial symposium on Communications, pp 71–75

    Google Scholar 

  27. Han Z, Ren W (2014) A novel wireless sensor network structure based on SDN. Int J Distrib Sens Netw 10(3):1–7

    Google Scholar 

  28. Mohapatra R, Mishra S, Mohapatra T (2012) Coverage problem in wireless sensor networks. Comp Cytogenet 2(1):67–72

    Google Scholar 

  29. Arumuganm G, Ponnuchamy T (2015) Ea-leach: development of energy efficient leach protocol for data gathering in wsn. EURASIP J Wirel Commun Netw 2015(1):1–9

    Google Scholar 

  30. Figueiredo CMS, dos Santos AL, Loureiro AAF, Nogueira JM (2005) Policy-based adaptive routing in autonomous wsns. In: IEEE ambient network international conference on distributed systems: operations and management. Springer, Berlin, pp 206–219

    Google Scholar 

  31. Shanmugapriya S, Shivakumar M (2015) Context based route model for policy based routing in wsn using sdn approach. In: iSRASE

    Google Scholar 

  32. Wang C, Sohraby K, Daneshmand M, Hu Y (2006) A survey of transport protocol for wireless sensor networks. IEEE Netw 20(3):34–40

    Google Scholar 

  33. Tian D, Georganas N (2003) A node scheduling scheme for energy conservation in large wireless sensor networks. Wireless Commun Mob Comput 3(2):271–290

    Google Scholar 

  34. Xing G, Wang X, Zhang Y, Lu C, Pless R, Gill C (2005) Integrated coverage and connectivity configuration for energy conservation in sensor networks. ACM Trans Sens Netw 1(1):36–72

    Google Scholar 

  35. Hua C, Yum TP (2007) Asynchronous random sleeping for sensor networks. ACM Trans Sens Netw 3(3):1–25

    Google Scholar 

  36. Kumar S, Lai TH, Balogh J (2004) On k-coverage in a mostly sleeping sensor network. In: Proceeding of the tenth annual international conference on mobile computing and networking. ACM, pp 144–158

    Google Scholar 

  37. Nath S, Gibbons PB (2007) Communication via fireflies: geographic routing on duty-cycled sensors. In: IEEE 6th international conference on information processing in sensor networks, pp 440–449

    Google Scholar 

  38. Wang Y, Chen H, Wu X, Shu L (2016) An energy-efficient sdn based sleep scheduling algorithm for wsn. J Netw Comput Appl 59:39–45

    Google Scholar 

  39. Yuan Z, Wang L, shu L, Hara T and Qin Z. (2011) A balanced energy consumption sleep scheduling algorithm in wireless sensor networks, IEEE in wireless communications and mobile computing conference (IWCMC), pp. 831–835

    Google Scholar 

  40. Wang Y, Chen H, Wu X, Shu L (2015) Improving wsns sleep scheduling mechanism with sdn-like architecture. In: International conference on information processing in sensor networks. ACM, pp 338–339

    Google Scholar 

  41. Levendovszky J, Tornia K, Treplan G, Olah A (2011) Novel load balancing algorithms ensuring uniform packet loss probabilities for wsn. In: IEEE in vehicular technology conference (VTC spring), pp 1–5

    Google Scholar 

  42. Zhang Y, Sun G, Li W (2011) Dehca: load balance clustering algorithm for energy heterogeneous wsn based on distance. Appl Mech Mater 44–47:3294–3298

    Google Scholar 

  43. Wang M, Li S-N, Li Z-G (2011) Multiple routing with load balancing based on ant colony algorithm in wsn. Comput Eng 37(14):1–4

    Google Scholar 

  44. Anatoliy S, Hu Z, Vasyl Y (2015) Increasing the data transmission robustness in wsn using the modified error correction codes on residue number system. Elektronika ir electrotechnika 21(1):76–81

    Google Scholar 

  45. Hu Z, Wang M, Yan X, Yin Y, Luo Z (2015) A comprehensive security architecture for sdn. In: IEEE in intelligence in next generation networks (ICIN), pp 30–37

    Google Scholar 

  46. Smeliansky R (2014) Sdn for network security. In: IEEE in science and technology conference (Modern networking technologies) (MoNeTec), pp 1–5

    Google Scholar 

  47. Yoon C, Park T, Lee S, Kang H, Shin S, Zhang Z (2015) Enabling security function with sdn: a feasibility study. Comput Netw 85:19–35

    Google Scholar 

  48. Prajapati J, Jain SC. (2018) Machine learning techniques and challenges in wireless sensor networks. In: Proceeding of the 2nd International Conference on inventive communication and computational technologies. IEEE

    Google Scholar 

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

    Google Scholar 

  50. Jiang C, Zhang H, Ren Y, Han Z, Chen KC, Hanzo L (2017) Machine learning paradigms for next generation wireless networks. In: IEEE International conference on communications (ICC)

    Google Scholar 

  51. Box GE, Tiao GC (2011) Bayesian inference in statistical analysis, vol 40. Wiley, Hoboken

    MATH  Google Scholar 

  52. Alpaydm E (2014) Introduction to machine learning, 3rd edn. The MIT Press, Cambridge

    Google Scholar 

  53. Winter J, Xu Y and Lee W-C. (2005) Energy efficient processing of k nearest neighbour queries in location-aware sensor networks. In: Proceeding 2nd international conference mobile ubiquitous systems: networking and services, pp 281–292

    Google Scholar 

  54. Jayaraman PP, Zaslavsky A, Delsing J (2010) Intelligent processing of k-nearest neighbors queries using mobile data collectors in location aware 3D wireless sensor network. Trend in applied intelligent systems. Springer, Berlin, pp 260–270

    Google Scholar 

  55. Steinwart I, Christmann A (2008) Support vector machines. Springer, New York

    MATH  Google Scholar 

  56. Morelande M, Moran B, Brazil M (2008) Bayesian node localisation in wireless sensor networks, In: Proceedings of IEEE international conference acoustics. Speech signal process, pp 2545–2548

    Google Scholar 

  57. Lu C-H, Fu L-C (2009) Robust location-aware activity recognition using wireless sensor network in an attentive home. IEEE Trans Autom Sci Eng 6(4):598–609

    Google Scholar 

  58. Shareef A, Zhu Y, Musavi M (2008) Localization using neural networks in wireless sensor networks. In: Proceedings of 1st international conference mobile wireless middleware. Operating systems, and applications, pp 1–7

    Google Scholar 

  59. Yu L, Wang N, Meng X (2005) Real-time forest fire detection with wireless sensor networks. In: Proceedings. 2005 ınternational conference on wireless communications, networking and mobile computing, vol 2, pp 1214–1217

    Google Scholar 

  60. Bahrepour M, Meratnia N, Poel M, Taghikhaki, Havinga PJ. (2010) Distributed event detection in wireless sensor network for disaster management. In: Proceedings of 2nd 2010 international conference on intelligent networking and collaborative, pp 507–512

    Google Scholar 

  61. Kim M, Park M-G (2009) Bayesian statistical modelling of system energy saving effectiveness for MAC protocols of wireless sensor network. Software engineering, artificial intelligence, networking and parallel/distributed Computing, vol 209. Studies in computational Intelligence. Springer, Berlin, pp 233–245

    Google Scholar 

  62. Shen Y-J, Wang M-S (2008) Broadcast scheduling in wireless sensor networking using fuzzy Hopfield neural network. Exp Syst Appl 34(2):900–907

    Google Scholar 

  63. Kulkarni RV, Venayagamoorthy GK (2009) Neural network based secure media access control protocol for wireless sensor network. In: Proceedings of IJCNN, pp 3437–3444

    Google Scholar 

  64. Janakiram D, Adi malikarjuna Reddy V, Phani Kumar A (2006) Outlier detection in wireless sensor networks using Bayesian belief networks. In: Proceedings 1st ınternational conference on communication systems software & middleware, pp 1–6

    Google Scholar 

  65. Branch JW, Giannella C, Szymanski B, Wolff R, Kargupta H (2013) In-network outlier detection in wireless sensor networks, knowl. Inf Syst 34(1):23–54

    Google Scholar 

  66. Kaplantzis S, Shilton A, Mani N, Sekerciouglu Y (2007) Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In: Proceedings 3rd ınternational conference on ıntelligent sensors, sensor networks and ınformation, pp 335–340

    Google Scholar 

  67. Rajasegarar S, Leckie C, Palaniswami M, Bezdek J (2007) Quarter sphere based distributed anomaly detection in wireless sensor networks. In: Proceedings IEEE ınternational conference on communications, pp 3864–3869

    Google Scholar 

  68. Snow A, Rastogi P, Weckman G, Snow A, Rastogi P and Weckman G. (2005) Assessing dependability of wireless networks using neural networks. In: Proceedings IEEE military communications conference, vol 5, pp 2809–2815

    Google Scholar 

  69. Moustapha A, Selmis R (2008) Wireless sensor network modelling using modified recurrent neural networks, application to fault detection. IEEE Trans Instrum Meas 57(5):981–988

    Google Scholar 

  70. Wang Y, Martonosi M, Peh L-S (2007) Predicting link quality using supervised learning in wireless sensor networks. ACM SIGMOBILE Mob Comput Commun Rev 11(3):71–83

    Google Scholar 

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Authors and Affiliations

  1. G. H. Raisoni College of Engineering, Nagpur, India

    Rumana Abdul Jalil Shaikh & Harikumar Naidu

  2. CSIR-NEERI, Nagpur, India

    Piyush A. Kokate

Authors
  1. Rumana Abdul Jalil Shaikh
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  2. Harikumar Naidu
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  3. Piyush A. Kokate
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Correspondence to Rumana Abdul Jalil Shaikh .

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Editors and Affiliations

  1. Research and Industry Incubation Center, Department of Information Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, India

    V. Suma

  2. Department of Technology and Maritime Innovation, University of Southeast, Horten, Norway

    Noureddine Bouhmala

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Shaikh, R.A.J., Naidu, H., Kokate, P.A. (2021). Next-Generation WSN for Environmental Monitoring Employing Big Data Analytics, Machine Learning and Artificial Intelligence. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_20

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  • DOI: https://doi.org/10.1007/978-981-15-5258-8_20

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