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All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications

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Abstract

Big data collection and storage have become one of the most obvious challenge in this era. Indeed, much of that data is collected thanky to a huge number of connected devices in sensing-based IoT applications. Thus, in order to deal with data growth in such applications, researchers have focused on data reduction approach as an efficient solution for minimizing the amount of data collection and saving the limited sensor energy in such networks. Mainly, data reduction approach relies on various kinds of data processing techniques such that aggregation, compression, prediction, clustering, sensing frequency adaptation and spatial-temporal correlation. However, each of those techniques has its own advantages and disadvantages regarding sensor energy saving, data reduction ratio, data accuracy, complexity, etc. In this paper, we propose a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications. The proposed mechanism takes advantages from existing data reduction techniques while optimizing various performance metrics. All-in-One relies on the cluster network architecture and works on three main phases: on-period, in-period and in-node. The first phase, e.g. on-period, allows each sensor node to search the similarity among its periodic collected data then to reduce its data transmission to the Cluster-Head (CH) by applying either data aggregation, compression or prediction technique. The second phase, e.g. in-period, allows each sensor to study the variation of the monitored condition then to reduce its data collection according to two techniques, on-off transmission or adapting sensing frequency. The last phase, e.g. in-node, is applied at the CH level and aims to remove the redundancy among data collected by neighboring nodes, based on in-network correlation or data clustering techniques, before sending the data to the sink. We conducted simulations on real sensor data in order to evaluate the efficiency of our mechanism, in terms of several performance metrics, compared to other exiting techniques.

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References

  1. Asghari P, Rahmani AM, Javadi HHS (2019) Internet of Things applications: A systematic review. Comput Netw 148:241–261

    Article  Google Scholar 

  2. Souri A, Norouzi M (2019) A state-of-the-art survey on formal verification of the internet of things applications. J Service Sci Res 11(1):47–67

    Article  Google Scholar 

  3. Khan WZ, Rehman MH, Zangoti HM, Afzal MK, Armi N, Salah K (2020) Industrial internet of things: Recent advances, enabling technologies and open challenges. Comput Electr Eng 81:106522

    Article  Google Scholar 

  4. Dehkordi SA, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Dehkordi MA (2020) A survey on data aggregation techniques in IoT sensor networks. Wireless Netw 26(2):1243–1263

    Article  Google Scholar 

  5. Pushpalatha S, Shivaprakasha KS (2020) Energy-efficient communication using data aggregation and data compression techniques in wireless sensor networks: A survey. Advances in communication, signal processing, VLSI, and embedded systems, pp 161–179

  6. Dias GM, Bellalta B, Oechsner S (2016) A survey about prediction-based data reduction in wireless sensor networks. ACM Computing Surveys (CSUR) 49(3):1–35

    Article  Google Scholar 

  7. Balakrishna S, Thirumaran M (2020) Semantics and clustering techniques for IoT sensor data analysis: A comprehensive survey. Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pp 103–125

  8. Harb H, Makhoul A (2017) Energy-efficient sensor data collection approach for industrial process monitoring. IEEE Trans Industr Inform 14(2):661–672

    Article  Google Scholar 

  9. Zeng P, Pan B, Choo Kim-Kwang R., Hong L. (2020) MMDA: Multidimensional and multidirectional data aggregation for edge computing-enhanced IoT. J Syst Architect, pp 101713

  10. Zhang J, Lin Z, Tsai Pei-Wei, Xu L (2020) Entropy-driven data aggregation method for energy-efficient wireless sensor networks. Information Fusion 56:103–113

    Article  Google Scholar 

  11. Bushnaq OM, Celik A, ElSawy H, Alouini M-S, Al-Naffouri TY (2019) Aeronautical Data Aggregation and Field Estimation in IoT Networks. Hovering and Traveling Time Dilemma of UAVs, IEEE Transactions on Wireless Communications, Vol 18, Iss 10:4620–4635

    Google Scholar 

  12. Ullah A, Said G, Sher M, Ning H (2020) Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN. Peer-to-Peer Netw Appl 13(1):163–174

    Article  Google Scholar 

  13. Soufiene BO, Bahattab AA, Trad A, Youssef H (2016) Lightweight and confidential data aggregation in healthcare wireless sensor networks. Transactions on Emerging Telecommunications Technologies 27 (4):576–588

    Article  Google Scholar 

  14. Liang Y, Li Y (2014) An efficient and robust data compression algorithm in wireless sensor networks. IEEE Commun Lett 18(3):439–442

    Article  Google Scholar 

  15. Xu Q, Akhtar R, Zhang X, Wang C (2018) Cluster-based arithmetic coding for data provenance compression in wireless sensor networks. Wireless Communications and Mobile Computing, vol 2018

  16. Deepu CJ, Heng C-H, Lian Y (2016) A hybrid data compression scheme for power reduction in wireless sensors for IoT. IEEE Trans Biomed Circuits Sys 2:245–254

    Google Scholar 

  17. Xu X, Zhang G (2017) A hybrid model for data prediction in real-world wireless sensor networks. IEEE Commun Lett

  18. Liazid H, Lehsaini M, Liazid A (2019) An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks. Wireless Netw 25(6):3545–3555

    Article  Google Scholar 

  19. Russo A, Verdier F, Miramond B (2018) Energy saving in a wireless sensor network by data prediction by using self-organized maps. Procedia computer science 130:1090–1095

    Article  Google Scholar 

  20. Guzel M, Kok Ibrahim , Akay D, Ozdemir S (2020) ANFIS and Deep Learning based missing sensor data prediction in IoT. Concurrency and Computation: Practice and Experience 32(2):e5400

    Article  Google Scholar 

  21. Chen S, Zhang S, Zheng X, Ruan X (2019) Layered adaptive compression design for efficient data collection in industrial wireless sensor networks. J Netw Comput Appl 129:37–45

    Article  Google Scholar 

  22. Agbulu GP, Kumar GJR, Juliet AV (2020) A lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks. Int J Distrib Sensor Netw 16(2):1550147719900111

    Article  Google Scholar 

  23. Qureshi KN, Bashir MUm, Lloret J, Leon A (2020) Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision. Journal of Sensors 2020

  24. Kotary DK, Nanda SJ (2020) Distributed robust data clustering in wireless sensor networks using diffusion moth flame optimization. Eng Appl Artif Intell 87:103342

    Article  Google Scholar 

  25. Habib C, Makhoul A, Darazi R, Salim C (2016) Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Trans Industr Info 12(6):2342–2352

    Article  Google Scholar 

  26. Başaran M, Schlupkothen S, Ascheid G (2019) Adaptive sampling techniques for autonomous agents in wireless sensor networks. 2019 IEEE 30th annual international symposium on personal, indoor and mobile radio communications (PIMRC), pp 1–6

  27. Abdul-Wahab S, Charabi Y, Osman S, Yetilmezsoy K, Osman II (2019) Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system. Atmospheric Pollution Research 10(6):1931–1943

    Article  Google Scholar 

  28. Rao Y, Zhao G, Wang W, Zhang J, Jiang Z, Wang R (2019) Adaptive data acquisition with energy efficiency and critical-sensing guarantee for wireless sensor networks. Sensors 19(12):2654

    Article  Google Scholar 

  29. Bahi J, Makhoul A, Medlej M (2014) A two tiers data aggregation scheme for periodic sensor networks. Ad Hoc & Sensor Wireless Networks 21(1-2):77–100

    Google Scholar 

  30. Wu M, Tan L, Xiong N (2015) A structure fidelity approach for big data collection in wireless sensor networks. Sensors 15(1):248–273

    Article  Google Scholar 

  31. 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 Journal 15(10):5483–5493

    Article  Google Scholar 

  32. Yin Y, Xu B, Cai H, Yu H (2020) A novel temporal and spatial panorama stream processing engine on IOT applications. J Industr Inf Integr, pp 100143

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

  34. Snedecor GW, Cochran WG (1989) Statistical methods, eight. Iowa State University Press, Ames

    MATH  Google Scholar 

  35. Makhoul A, Harb H, Laiymani D (2015) Residual energy-based adaptive data collection approach for periodic sensor networks. Ad Hoc Netw 35:149–160

    Article  Google Scholar 

  36. MacQueen J, et al. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 1(14):281–297

    MathSciNet  MATH  Google Scholar 

  37. Govender P, Sivakumar V (2020) Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research 11(1):40–56

    Article  Google Scholar 

  38. Lavanya K, Kashyap R, Anjana S, Thasneen S (2020) An enhanced K-Means MSOINN based clustering over neo4j with an application to weather analysis. International Conference on Intelligent Computing and Smart Communication 2019:451–461

    Google Scholar 

  39. Zhang G, Li Y, Deng X (2020) K-Means clustering-based electrical equipment identification for smart building application. Information 11(1):27

    Article  Google Scholar 

  40. Madden S (2004) Intel lab data, http://db.csail.mit.edu/labdata/labdata.html

  41. Heinzelman WB (2000) Application-specific protocol architectures for wireless networks, Thesis at Massachusetts Institute of Technology

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Authors and Affiliations

  1. Lab-STICC, CNRS UMR 6285, ENSTA-Bretagne, Brest, France

    Marwa Ibrahim, Hassan Harb, Ali Mansour, Abbass Nasser & Christophe Osswald

  2. ICCS-Lab, American University of Culture and Education (AUCE), Beirut, Lebanon

    Marwa Ibrahim, Hassan Harb & Abbass Nasser

Authors
  1. Marwa Ibrahim
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  2. Hassan Harb
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  3. Ali Mansour
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  4. Abbass Nasser
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  5. Christophe Osswald
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Correspondence to Marwa Ibrahim.

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Ibrahim, M., Harb, H., Mansour, A. et al. All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications. Peer-to-Peer Netw. Appl. 14, 1154–1173 (2021). https://doi.org/10.1007/s12083-021-01095-5

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