Skip to main content

Advertisement

Log in

An Energy-Efficient and Congestion Control Data-Driven Approach for Cluster-Based Sensor Network

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

In Wireless Sensor Networks (WSNs), a dense deployment of sensor nodes produce data that contain intra-temporal and inter-spatial correlation. To reduce the intensity of correlation, we propose in-node data aggregation technique that eliminates redundancy in the sensed data in an energy-efficient manner. A novel data-driven approach is adopted to perform in-node data aggregation using an underlying cluster-based hierarchical network. Our proposed approach partially processes the data at each member node and forwards a fraction of the actual data, i.e., fused data, towards the cluster head. At each member node, the raw captured data is categorized into various classes, i.e., stratum. Each member node continuously senses the environment for temperature readings and compares them with the mean values of various strata. If the temperature reading is less than or greater than the mean value, it is compared with the existing Min/Max of that particular stratum. If in case, the new reading is less than/greater than the Min/Max of a particular stratum, it replaces these values, accordingly. Our proposed approach is computationally lightweight, energy-efficient and reduces the degree of correlation among the resource-constrained sensor nodes. As as a result, communication cost, packet collision and network congestion are reduced and the network lifetime is enhanced. The analytical results prove the validity and effectiveness of our proposed approach.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Akyildiz IF, Kasimoglu IH (2004) Wireless sensor and actor networks: research challenges. Ad hoc netw 2 (4):351–367

    Article  Google Scholar 

  2. Akyildiz IF, Vuran MC (2010) Wireless sensor networks, vol 4. Wiley, New York

    Book  Google Scholar 

  3. Alam M, Albano M, Radwan A, Rodriguez J (2012) Context parameter prediction to prolong mobile terminal battery life. In: Mobile multimedia communications, pp 476–489. Springer, Berlin

  4. Alam M, Rodriguez J (2010) A dual head clustering mechanism for energy efficient WSNs. In: Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, pp 380–387. Springer, Berlin

  5. Anastasi G, Conti M, Di Francesco M, Passarella A (2009) Energy conservation in wireless sensor networks A survey. Ad hoc netw 7(3):537–568

    Article  Google Scholar 

  6. Bahi JM, Makhoul A, Medlej M (2014) A two tiers data aggregation scheme for periodic sensor networks. Ad Hoc Sens Wirel Netw 21(1-2):77–100

    Google Scholar 

  7. Berl A, Gelenbe E, Di Girolamo M, Giuliani G, De Meer H, Dang MQ, Pentikousis K (2010) Energy-efficient cloud computing. Comput J 53(7):1045–1051

    Article  Google Scholar 

  8. Biswas S, Das R, Chatterjee P (2018) Energy-efficient connected target coverage in multi-hop wireless sensor networks. In: Industry interactive innovations in science, engineering and technology, pp 411–421. Springer

  9. Cheng L, Niu J, Luo C, Shu L, Kong L, Zhao Z, Gu Y (2018) Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks. Comput Netw 134:66–77

    Article  Google Scholar 

  10. De Vries PG (1986) Stratified random sampling. In: Sampling theory for forest inventory, pp 31–55. Springer

  11. Dhand G, Tyagi SS (2016) Data aggregation techniques in wsn Survey. Procedia Comput Sci 92:378–384

    Article  Google Scholar 

  12. Du T, Qu Z, Guo Q, Qu S (2015) A high efficient and real time data aggregation scheme for wsns. Int J Distributed Sens Netw 11(6):261381

    Article  Google Scholar 

  13. Fasolo E, Rossi M, Widmer J, Zorzi M (2007) In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wirel Commun 14(2):70–87

    Article  Google Scholar 

  14. Ganjewar PD, Barani S, Wagh SJ, Sonavane SS (2018) Survey on data reduction techniques for energy conservation for prolonging life of wireless sensor network. Wirel Commun 10(2):17–25

    Google Scholar 

  15. Hansen MH, Hurwitz WN (1953) Sample survey methods and theory, vol I. Wiley, New York

    MATH  Google Scholar 

  16. Harb H, Makhoul A, Laiymani D, Jaber A, Tawil R (2014) K-means based clustering approach for data aggregation in periodic sensor networks. In: 2014 IEEE 10Th international conference on wireless and mobile computing, networking and communications (wimob), pp 434–441. IEEE

  17. Harb H, Makhoul A, Tawbi S, Couturier R (2017) Comparison of different data aggregation techniques in distributed sensor networks. IEEE Access 5:4250–4263

    Article  Google Scholar 

  18. Harb H, Makhoul A, Tawil R, Jaber A (2014) A suffix-based enhanced technique for data aggregation in periodic sensor networks. In: 2014 international wireless communications and mobile computing conference (IWCMC), pp 494–499. IEEE

  19. Haupt J, Bajwa WU, Rabbat M, Nowak R (2008) Compressed sensing for networked data. IEEE Signal Proc Mag 25(2):92–101

    Article  Google Scholar 

  20. Huy DV, Viet ND (2015) Df-ams: Proposed solutions for multi-sensor data fusion in wireless sensor networks. In: 2015 Seventh international conference on knowledge and systems engineering (KSE), pp 1–6. IEEE

  21. Jan MA, Jan SRU, Alam M, Akhunzada A, Rahman IU (2018) A comprehensive analysis of congestion control protocols in wireless sensor networks. Mob Netw Appl 23:1–13

    Article  Google Scholar 

  22. Jan MA, Nanda P, He X, Liu RP (2014) Pasccc: Priority-based application-specific congestion control clustering protocol. Comput Netw 74:92–102

    Article  Google Scholar 

  23. Jan MA, Usman M, He X, Rehman AU (2018) Sams: A seamless and authorized multimedia streaming framework for wmsn-based iomt. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2848284

  24. Kafi MA, Djenouri D, Ben-Othman J, Badache N (2014) Congestion control protocols in wireless sensor networks A survey. IEEE Commun Surv Tutorials 16(3):1369–1390

    Article  Google Scholar 

  25. Khan R, Ali I, Suryani AM, Ahmad M, Zakarya M (2013) Wireless sensor network based irrigation management system for container grown crops in pakistan. World Appl Sci J 24(8):1111–1118

    Google Scholar 

  26. Laughlin SB, Sejnowski TJ (2003) Communication in neuronal networks. Science 301(5641):1870–1874

    Article  Google Scholar 

  27. Liu H, Ma J, Huang W (2018) Sensor-based complete coverage path planning in dynamic environment for cleaning robot. CAAI Trans Intell Technol 3(1):65–72

    Article  Google Scholar 

  28. Liu Z, Tsuda T, Watanabe H (2015) Traffic deduction exploring sensor data’s intra-correlations in train track monitoring wsn. In: 2015 IEEE SENSORS, pp 1–4. IEEE

  29. Ma J, Jiang X, Gong M (2018) Two-phase clustering algorithm with density exploring distance measure. CAAI Trans Intell Technol 3(1):59–64

    Article  Google Scholar 

  30. Msechu EJ, Giannakis GB (2012) Sensor-centric data reduction for estimation with wsns via censoring and quantization. IEEE Trans Signal Process 60(1):400–414

    Article  MathSciNet  MATH  Google Scholar 

  31. Omona J (2013) Sampling in qualitative research: Improving the quality of research outcomes in higher education. Makerere J High Educ 4(2):169–185

    Google Scholar 

  32. Pattem S, Krishnamachari B, Govindan R (2008) The impact of spatial correlation on routing with compression in wireless sensor networks. ACM Trans Sens Netw (TOSN) 4(4):24

    Google Scholar 

  33. Rahman H, Ahmed N, Hussain I (2016) Comparison of data aggregation techniques in internet of things (iot). In: International conference on wireless communications, signal processing and networking (wiSPNET), pp 1296–1300. IEEE

  34. Rout RR, Ghosh SK (2013) Enhancement of lifetime using duty cycle and network coding in wireless sensor networks. IEEE Trans Wirel Commun 12(2):656–667

    Article  Google Scholar 

  35. Sayood K (2005) Introduction to data compression. Elsevier, Amsterdam

    MATH  Google Scholar 

  36. Singh VK, Kumar M (2018) In-network data processing in wireless sensor networks using compressed sensing. Int J Sens Netw 26(3):174–189

    Article  Google Scholar 

  37. Uthayakumar J, Vengattaraman T, Dhavachelvan P (2018) A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. Journal of King Saud University-Computer and Information Sciences

  38. Xiang L, Luo J, Vasilakos A (2011) Compressed data aggregation for energy efficient wireless sensor networks. In: 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON), pp 46–54. IEEE

  39. Xu X, Ansari R, Khokhar A, Vasilakos AV (2015) Hierarchical data aggregation using compressive sensing (hdacs) in wsns. ACM Trans Sens Netw (TOSN) 11(3):45

    Google Scholar 

  40. Yang H, Yu L (2017) Feature extraction of wood-hole defects using wavelet-based ultrasonic testing. J For Res 28(2):395–402

    Article  Google Scholar 

  41. Yetgin H, Cheung KTK, El-Hajjar M, Hanzo LH (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutorials 19(2):828–854

    Article  Google Scholar 

  42. Zhu C, Wu S, Han G, Shu L, Wu H (2015) A tree-cluster-based data-gathering algorithm for industrial wsns with a mobile sink. IEEE Access 3(1):381–96

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mian Ahmad Jan or Rahim Khan.

Additional information

Strata is the plural of stratum

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jan, S.R.U., Jan, M.A., Khan, R. et al. An Energy-Efficient and Congestion Control Data-Driven Approach for Cluster-Based Sensor Network. Mobile Netw Appl 24, 1295–1305 (2019). https://doi.org/10.1007/s11036-018-1169-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1169-x

Keywords

Navigation