An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

  • Hassan HarbEmail author
  • Chady Abou Jaoude
  • Abdallah Makhoul


The Internet of Things (IoT) is a vision in which billions of smart objects are linked together. In the IoT, “things” are expected to become active and enabled to interact and communicate among themselves and with the environment by exchanging data and information sensed about the environment. In this future interconnected world, multiple sensors join the internet dynamically and use it to exchange information all over the world in semantically interoperable ways. Therefore, huge amounts of data are generated and transmitted over the network. Thus, these applications require massive storage, huge computation power to enable real-time processing, and high-speed network. In this paper, we propose a data prediction and processing approach aiming to reduce the size of data collected and transmitted over the network while guaranteeing data integrity. This approach is dedicated to devices/sensors with low energy and computing resources. Our proposed technique is composed of two stages: on-node prediction model and in-network aggregation algorithm. The first stage uses the Lagrange interpolation polynomial model to reduce the amount of data generated by sensor nodes while, the second stage uses a statistical test, i.e. Kolmogorov-Smirnov, and aims to reduce the redundancy between data generated by neighbouring nodes. Simulation on real sensed data reveals that the proposed approach significantly reduces the amount of data generated and transmitted over the network thus, conserving sensors’ energies and extending the network lifetime.


IoT WSN Data prediction Lagrange interpolation Kolmogorov-Smirnov test In-network computing Real sensor data 


Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.


  1. 1.
    Cse A, Maheswari U (2018) A survey on techniques for energy efficient routing in WSN. Int J Sensors Sensor Netw 6(1):8–15Google Scholar
  2. 2.
    Dias Gabriel Martins, Bellalta B, Oechsner S (2016) A survey about prediction-based data reduction in wireless sensor networks. J ACM Comput Surveys (CSUR) 49(3):58Google Scholar
  3. 3.
    Singh VK, Singh VK, Kumar M (2017) In-network data processing based on compressed sensing in WSN: A survey. An Int J Wireless Personal Commun 96(2):2087–2124Google Scholar
  4. 4.
    Shivaprasad Yadav SG, Chitra A, Lakshmi Deepika C (2015) Reviewing the process of data fusion in wireless sensor network: a brief survey. Int J Wireless Mobile Comput 8(2):130–140Google Scholar
  5. 5.
    Samarah S (2016) Vector-based data prediction model for wireless sensor networks. Int J High Performance Comput Netw (IJHPCN) 9(4):310–315Google Scholar
  6. 6.
    Krishna G, Singh SK, Singh JP, Kumar P (2018) Energy conservation through data prediction in wireless sensor networks. In: Proceedings of 3rd international conference on internet of things and connected technologies (ICIoTCT), Jaipur, India, pp 986–992, March 26–27Google Scholar
  7. 7.
    Md MI, Nazi ZA, Aowlad Hossain ABM, Md MR (2018) Data prediction in distributed sensor networks using Adam Bashforth Moulton method. J Sensor Technol 8:48–57Google Scholar
  8. 8.
    Islam MM, Nazi ZA, Rana MM, Hossain AABM (2017) Information prediction in sensor networks using Milne-Simpson’s scheme. In: Proceedings of the international conference on advances in electrical engineering, pp 494–498Google Scholar
  9. 9.
    Yoon I, Kun Noh D (2018) Energy-aware control of data compression and sensing rate for wireless rechargeable sensor networks. J Sensors 18(8):2609Google Scholar
  10. 10.
    Xu Q, Akhtar R, Zhang X, Wang C (2018) Cluster-based arithmetic coding for data provenance compression in wireless sensor networks, vol 2018Google Scholar
  11. 11.
    Kim S, Cho C, Park K-J, Lim H (2017) Increasing network lifetime using data compression in wireless sensor networks with energy harvesting. Int J Distributed Sensor Netw 13(1)Google Scholar
  12. 12.
    Sheltami T, Musaddiq M, Shakshuki E (2016) Data compression techniques in wireless sensor networks. J Future Generation Comput Syst 64(C):151–162Google Scholar
  13. 13.
    Liang Y., Li Y. (2014) An efficient and robust data compression algorithm in wireless sensor networks, Journal of Future Generation Computer Systems. IEEE Commun Lett 18(3):439–442Google Scholar
  14. 14.
    Wu M, Tan L, Xiong N (2015) A structure fidelity approach for big data collection in wireless sensor networks. Sensors J 15:248–273Google Scholar
  15. 15.
    Dhimal S, Sharma K (2015) Energy conservation in wireless sensor networks by exploiting inter-node data similarity metrics. Int J Energy Inf Commun 6(2):23–32Google Scholar
  16. 16.
    Ozdemir S, Peng M, Xiao Y (2015) PRDA: Polynomial regression-based privacy-preserving data aggregation for wireless sensor networks. Wireless Commun Mobile Comput J 15:615–628Google Scholar
  17. 17.
    Bahi J, Makhoul A, Medlej M (2014) A two tiers data aggregation scheme for periodic sensor networks. Ad Hoc & Sensor Wireless Netw 21((1-2)):77–100Google Scholar
  18. 18.
    Al-Tabbakh SM (2017) Novel technique for data aggregation in wireless sensor networks, International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), Gafsa, Tunisia, October 20-22, pp 1–8Google Scholar
  19. 19.
    Harb H, Makhoul A, Couturier R (2015) An enhanced k-means and anova-based clustering approach for similarity aggregation in underwater wireless sensor networks. IEEE Sensors J 15(10):5483–5493Google Scholar
  20. 20.
    Zhang D, Zhang T, Zhang J, Dong Y, Zhang X-D (2018) A kind of effective data aggregating method based on compressive sensing for wireless sensor network, vol 2018Google Scholar
  21. 21.
    Li G, Chen H, Peng S, Li X, Wang C, Yu S, Yin P (2018) A collaborative data collection scheme based on optimal clustering for wireless sensor networks. Sensors (Basel) 18(8):2487Google Scholar
  22. 22.
    Srikanth N, Ganga Prasad MS (2018) Efficient clustering protocol using fuzzy K-means and midpoint algorithm for lifetime improvement in WSNs. Int J Intel Eng Syst 11(4):61–71Google Scholar
  23. 23.
    Khan A, Gupta CP, Sharma I (2015) Addressing data aggregation using polynomial regression in WSNs. Int J Sensors, Wireless Commun Control 5(2):114–120Google Scholar
  24. 24.
    Ghosh S, Misra IS (2017) Design and testbed implementation of an energy efficient clustering protocol for WSN. In: IEEE International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), Shillong, India, vol 6-7, pp 1–6Google Scholar
  25. 25.
    Mahajan S, Banga VK (2015) Inter cluster data aggregation balanced energy efficient network integrated super heterogeneous protocol for wireless sensor networks, Twelfth International Conference on Wireless and Optical Communications Networks (WOCN), Bangalore, India Sept. 9-11, pp 1–6Google Scholar
  26. 26.
    Zhao D, Bu L, Alippi C, Wei Q (2017) A Kolmogorov-Smirnov test to detect changes in stationarity in big data. IFAC-PapersOnLine 50(1):14260–14265Google Scholar
  27. 27.
    Antoneli F, Passos FM, Lopes LR, Briones MRS (2018) A Kolmogorov-Smirnov test for the molecular clock based on Bayesian ensembles of phylogenies. PLOS ONE J 13(1):1–22Google Scholar
  28. 28.
    Trusina J, Franc J, Kus V (2017) Statistical homogeneity tests applied to large data sets from high energy physics experiments. J Phy: Conf Series 936(1):1–6Google Scholar
  29. 29.
    Madden S (2004) Intel berkeley research lab.
  30. 30.
    Heinzelman Wendi Beth (June 2000) Application Specific Protocol Architectures for Wireless Networks, PhD thesis Massachusetts Institute of TechnologyGoogle Scholar
  31. 31.
    Heinzelman WB, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks, Proceedings of the 33rd Hawaii international conference on system sciencesGoogle Scholar
  32. 32.
    Harb H, Makhoul A, Jaoude CA (2018) A real-time massive data processing technique for densely distributed sensor networks. IEEE Access 6:56551–56561Google Scholar
  33. 33.
    Tayeh GB, Makhoul A, Laiymani D, Demerjian J (2018) A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks. Pervasive and Mobile Computing 49:62–75Google Scholar
  34. 34.
    Harb H, Makhoul A (2018) Energy-efficient sensor data collection approach for industrial process monitoring. IEEE Trans Industrial Inf 14(2):661–672Google Scholar
  35. 35.
    Harb H, Makhoul A, Laiymani D, Jaber A (2017) A distance-based data aggregation technique for periodic sensor networks. ACM Trans Sensor Netw 13(4):32:1–32:40Google Scholar
  36. 36.
    Behera TM, Mohapatra SK, Samal UC, Khan MS, Daneshmand M, Gandomi AH (2019) Residual energy based cluster-head selection in WSNs for IoT application. IEEE Int Things J 1(1):1–8Google Scholar
  37. 37.
    Biswas S, Saha J, Nag T, Chowdhury C, Neogy S (2016) A novel cluster head selection algorithm for energy-efficient routing in wireless sensor network. 2016 IEEE 6th international conference on advanced computing (IACC), pp 588–593Google Scholar
  38. 38.
    Priyadarshini RR, Sivakumar N (2018) Cluster head selection based on minimum connected dominating set and bi-partite inspired methodology for energy conservation in WSNs, Journal of King Saud University-Computer and Information Sciences, 1–20Google Scholar
  39. 39.
    Yousif YK, Badlishah R, Yaakob N, Amir A (2018) An energy efficient and load balancing clustering scheme for wireless sensor network (WSN) based on distributed approach. J Phys Conf Series 1019(1):012007Google Scholar
  40. 40.
    Kang S (2019) Energy optimization in cluster-based routing protocols for large-area wireless sensor networks. J Symmetry 11(1):37Google Scholar
  41. 41.
    Govind P (2018) Gupta improved cuckoo search-based clustering protocol for wireless sensor networks. Proc Comput Sci 125:234–240Google Scholar
  42. 42.
    Rais A, Bouragba K (2019) Mohammed Ouzzif routing and clustering of sensor nodes in the honeycomb architecture. Journal of Computer Networks and Communications 2019:Google Scholar
  43. 43.
    Plageras AP, Psannis KE, Stergiou C, Wang H, Gupta BB (2018) Efficient IoT-based sensor BIG data collection–processing and analysis in smart buildings. Future Generation Comput Syst J 82:349–357Google Scholar
  44. 44.
    Stergiou C, Psannis KE, Kim B-G, Gupta B (2018) Secure integration of IoT and cloud computing. Future Generation Comput Syst J 78:964–975Google Scholar
  45. 45.
    Stergiou C, Psannis KE (2017) Recent advances delivered by mobile cloud computing and internet of things for big data applications: A survey. Int J Netw Manag 27(3):e1930Google Scholar
  46. 46.
    Psannis KE, Stergiou C, Gupta BB (2019) Advanced media-based smart big data on intelligent cloud systems. IEEE Trans Sustainable Comput 4(1):77–87Google Scholar
  47. 47.
    Stergiou C, Psannis KE, Gupta BB, Ishibashi Y (2018) Security, privacy & efficiency of sustainable cloud computing for big data & IoT. Sustainable Comput Inf Syst 19:174–184Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.TICKET Laboratory, Faculty of EngineeringAntonine UniversityBaabdaLebanon
  2. 2.FEMTO-ST Institute/CNRS, The DISC DepartmentUniversity Bourgogne Franche-ComtéBelfortFrance

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