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Data-driven peer-to-peer blockchain framework for water consumption management

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Abstract

It is widely believed that effective water resource management can optimize the scheduling of water supply plans, which is essential for sustainable development. The core of management is to accurately predict future water consumption. However, existing studies generally face two challenges. First, a reliable bottom platform for the support of online data integration is absent. In addition, multisource factors that primarily affect water consumption are neglected when modeling. To solve the above problems, this paper proposes a data-driven peer-to-peer blockchain framework to predict water consumption. Fundamentally, it utilizes a blockchain system with a peer-to-peer network to serve as the decision support platform hardware. On this basis, an intelligent prediction algorithm that combines the grey model and long short-term memory model is developed to drive the hardware infrastructure. After that, the performance of the proposed method is evaluated by carrying out experiments on a real-world dataset, and three typical approaches are selected for comparison. The experimental results show that the proposal exceeds general prediction models by approximately 8%.

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References

  1. Dadmand F, Naji-Azimi Z, Davary K (2020) Sustainable allocation of water resources in water-scarcity conditions using robust fuzzy stochastic programming. J Clean Prod 276. https://doi.org/10.1016/j.jclepro.2020.123812

  2. Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Sci Adv 2:e1500323. https://doi.org/10.1126/sciadv.1500323

    Article  Google Scholar 

  3. Swain SS, Mishra A, Chatterjee C (2020) Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach. J Hydrol 590. https://doi.org/10.1016/j.jhydrol.2020.125260

  4. Sjöstrand K, Lindhe A, Söderqvist T, Rosén L (2019) Marginal abatement cost curves for water scarcity mitigation under uncertainty. Water Resour Manag 33:4335–4349

    Article  Google Scholar 

  5. Pesantez JE, Kaza N (2020) Smart meters data for modeling and forecasting water demand at the user-level. Environ Model Softw 125. https://doi.org/10.1016/j.envsoft.2020.104633

  6. Beh EHY, Dandy GC, Paton FL (2014) Optimal sequencing of water supply options at the regional scale incorporating alternative water supply sources and multiple objectives. Environ Model Softw 53:137–153

    Article  Google Scholar 

  7. Karamaziotis PI, Raptis A, Nikolopoulos K, Assimakopoulos V (2020) An empirical investigation of water consumption forecasting methods. Int J Forecast 36:588–606

    Article  Google Scholar 

  8. Guo Z, Shen Y, Aloqaily M et al (2021) Probabilistic inferences-based modeling for sustainable environmental systems under hybrid cloud infrastructure. Simul Model Pract Theory 107:102215

    Article  Google Scholar 

  9. Zeng W, Guo Z, Shen Y, Bashir AK, Yu K, Al-Otaibi YD, Gao X (2020) Data-driven management for fuzzy sewage treatment processes using hybrid neural computing. Neural Comput & Applic. https://doi.org/10.1007/s00521-020-05655-3

  10. Guo Z, Yu K, Jolfaei A, Bashir AK, Almagrabi AO, Kumar N (2021) A fuzzy detection system for rumors through explainable adaptive learning. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2021.3052109

  11. Yu K, Lin L, Alazab M, Tan L, Gu B (2020) Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3042504

  12. Guo Z, Tang L, Guo T, Yu K, Alazab M, Shalaginov A (2021) Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Futur Gener Comput Syst 117:205–218

    Article  Google Scholar 

  13. Zhang J et al (2021) 3D reconstruction for motion blurred images using deep learning-based intelligent systems. CMC-Comput Mater Continua 66:2087–2104. https://doi.org/10.32604/cmc.2020.014220

    Article  Google Scholar 

  14. Guo G, Liu S, Wu Y, et al. (2018) Short-term water demand forecast based on deep learning method. J Water Resour Plan Manag 144. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992

  15. Zhang WP, Yang Q, Mao YH (2018) Application of improved least squares support vector machine in the forecast of daily water consumption. Wirel Pers Commun 102:3589–3602

    Article  Google Scholar 

  16. Wu H, Zhou M (2017) Forecasting the water demand in Chongqing, China using a grey prediction model and recommendations for the sustainable development of urban water consumption. Int J Environ Res Publ Health 14. https://doi.org/10.3390/ijerph14111386

  17. Rasifaghihi N, Haghighat F (2020) Forecast of urban water consumption under the impact of climate change. Sustain Cities Soc 52. https://doi.org/10.1016/j.scs.2019.101848

  18. Guo ZW et al (2020) Robust spammer detection using collaborative neural network in internet of thing applications. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3003802

  19. Yu KP, Tan L, Shang XL, Huang JJ, Srivastava G, Chatterjee P (2020) Efficient and privacy-preserving medical research support platform against COVID-19: a Blockchain-based approach. IEEE Consum Electron Mag 10:111–120. https://doi.org/10.1109/MCE.2020.3035520

    Article  Google Scholar 

  20. Liu JG, Savenije HHG, Xu JX (2003) Forecast of water demand in Weinan City in China using WDF-ANN model. Phys Chem Earth, Parts A/B/C 28:219–224

    Article  Google Scholar 

  21. Sanchez GM, Terando A, Smith JW, et al. (2020) Forecasting water demand across a rapidly urbanizing region. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139050

  22. Firat M, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241

    Article  Google Scholar 

  23. Xiao Y, Li C, Song L et al (2021) A multidimensional information fusion-based matching decision method for manufacturing service resource. IEEE Access 9:39839–39851 

  24. Su J, Yang Y, Yang T (2018) Measuring knowledge diffusion efficiency in R&D network. Knowledge Management Research & Practice 16:208–219

    Article  Google Scholar 

  25. Guo Z, Yu K, Li Y, Srivastava G, Lin JCW (2021) Deep learning-embedded social internet of things for ambiguity-aware social recommendations. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2021.3049262

  26. Yu K, Tan L, Aloqaily M, Yang H, Jararweh Y (2021) Blockchain-Enhanced Data Sharing with Traceable and Direct Revocation in IIoT. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2021.3049141

  27. Guo ZW, Wang H (2020) A deep graph neural network-based mechanism for social recommendations. IEEE Trans Ind Inf 17:2776–2783. https://doi.org/10.1109/TII.2020.2986316

    Article  Google Scholar 

  28. Alazaba, M., Hudab, S., Abawajyc, J., et.al.: A hybrid wrapper-filter approach for malware detection. J Netw 9, 2878–2891(2014)

  29. Zhang X, Yang L, Ding Z, Song J, Zhai Y, Zhang D (2020) Sparse vector coding-based multi-carrier NOMA for in-home health networks. IEEE J Sel Areas Commun 39:325–337. https://doi.org/10.1109/JSAC.2020.3020679

    Article  Google Scholar 

  30. Venkatraman S, Alazab M, Yang Q (2018) Use of data visualisation for zero-day malware detection. Secur Commun Netw 2018:1–13

    Article  Google Scholar 

  31. Feng C et al (2021) Efficient and secure data sharing for 5G flying drones: a Blockchain-enabled approach. IEEE Netw. https://doi.org/10.1109/MNET.011.2000223

  32. Uzair YM, Ibrahim Al-B, Ebubekir A (2021) A modified GM(1,1) model to accurately predict wind speed. Sustain Energy Technol Assess 43. https://doi.org/10.1016/j.seta.2020.100905

  33. Liu SF, Forrest J, Yang YJ (2012) A brief introduction to grey systems theory. Grey Syst Theory Appl 2:89–104

    Article  Google Scholar 

  34. Hochreiter S, Schmidhuber R (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  35. Alazab M, Khan S, Krishnan SSR, Pham Q, Reddy MPK, Gadekallu TR (2020) A multidirectional LSTM model for predicting the stability of a smart grid. IEEE Access 8:85454–85463

    Article  Google Scholar 

  36. Zhou X, Hu Y, Liang W, Ma J, Jin Q (2020) Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans Ind Inf 17:3469–3477. https://doi.org/10.1109/TII.2020.3022432

    Article  Google Scholar 

  37. Hussein AF, ArunKumar N, Ramirez-Gonzalez G, Abdulhay E, Tavares JMRS, de Albuquerque VHC (2018) A medical records managing and securing blockchain based system supported by a genetic algorithm and discrete wavelet transform. Cogn Syst Res 52:1–11

    Article  Google Scholar 

  38. Singh SK, Park JH (2020) BlockIoTIntelligence: a Blockchain-enabled intelligent IoT architecture with artificial intelligence. Futur Gener Comput Syst 110:721–743

    Article  Google Scholar 

  39. Tan L, Xiao H et al (2021) A Blockchain-empowered crowdsourcing system for 5G-enabled smart cities. Comput Stand Interfaces. https://doi.org/10.1016/j.csi.2021.103517

  40. Shi N, Tan L, Li W, Qi X, Yu K (2020) A Blockchain-empowered AAA scheme in the large-scale HetNet. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2020.10.002

  41. Liang LY, Cai XY (2020) Forecasting peer-to-peer platform default rate with LSTM neural network. Electron Commer Res Appl 43. https://doi.org/10.1016/j.elerap.2020.100997

  42. Li Z, Huang GQ (2018) Toward a blockchain cloud manufacturing system as a peer to peer distributed network platform. Robot Comput Integr Manuf 54:133–144

    Article  Google Scholar 

  43. Danish V, Mamoun A, Sobia W, et al. (2019) IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171. https://doi.org/10.1016/j.comnet.2020.107138

  44. Tang M, Alazab M, Luo Y (2019) Big data for Cybersecurity: vulnerability disclosure trends and dependencies. IEEE Trans Big Data 5:317–329

    Article  Google Scholar 

  45. Alazab M, Alazab M, Shalaginov A, Mesleh A, Awajan A (2020) Intelligent mobile malware detection using permission requests and API calls. Futur Gener Comput Syst 107:509–521

    Article  Google Scholar 

  46. Hu YC (2020) Energy demand forecasting using a novel remnant GM(1,1) model. Soft Comput 1–10. https://doi.org/10.1007/s00500-020-04765-3

  47. Guo AP, Jiang AJ, Li XX (2020) Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches. J Supercomput 76:932–947

    Article  Google Scholar 

  48. Yuan YB, Li Q, Yuan XH, Liu SX (2020) A SAFSA- and metabolism-based nonlinear grey bernoulli model for annual water consumption prediction. Iran J Sci Technol Trans Civil Eng 1–11. https://doi.org/10.1007/s40996-020-00366-0

  49. Peng XG, Jin YC (2016) A dynamic optimization approach to the design of cooperative co-evolutionary algorithms. Knowl-Based Syst 109:174–186

    Article  Google Scholar 

  50. Reia SM, Fontanari JF (2020) The surprising little effectiveness of cooperative algorithms in parallel problem solving. Euro Phys JB 93. https://doi.org/10.1140/epjb/e2020-10199-9

  51. Zhou X, Li Y, Liang W (2020) CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans Comput Biol Bioinforma. https://doi.org/10.1109/TCBB.2020.2994780

  52. An QL, Tao ZR, Xu XW, Chen M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement 154. https://doi.org/10.1016/j.measurement.2019.107461

  53. Kaboli SHA, Rahim NA (2016) Long-term electric energy consumption forecasting via artificial cooperative search algorithm. Energy 115:857–871

    Article  Google Scholar 

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Acknowledgments

This research was supported by the National Key Research and Development Program of China (2016YFE0205600), State Language Commission Program of China (YB135-121), Science and Technology Research Project of Chongqing Municipal Education Commission (KJZD-M202000801), Natural Science Foundation of Chongqing Science & Technology Commission (cstc2020jcyj-msxmX0721), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202000810), and Project of Chongqing Technology and Business University (ZDPTTD201917, KFJJ2019071).

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Authors and Affiliations

  1. National Research Base of Intelligent Manufacturing Service, School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Huimin Li, Yu Shen & Xu Gao

  2. School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, 400067, China

    Xinyao Chen & Zhiwei Guo

  3. University College Dublin, Belfield, Dublin 4, Ireland

    Junli Xu

  4. Chongqing South-to-Thais Environmental Protection Technology Research Institute Co. Ltd, Chongqing, 400069, China

    Yu Shen

  5. Chongqing Sino French Environmental Excellence Research & Development Center Co. Ltd, Chongqing, 400042, China

    Xu Gao

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  1. Huimin Li
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  5. Yu Shen
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Correspondence to Yu Shen or Xu Gao.

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This article is part of the Topical Collection: Special Issue on Blockchain for Peer-to-Peer Computing

Guest Editors: Keping Yu, Chunming Rong, Yang Cao, and Wenjuan Li

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Li, H., Chen, X., Guo, Z. et al. Data-driven peer-to-peer blockchain framework for water consumption management. Peer-to-Peer Netw. Appl. 14, 2887–2900 (2021). https://doi.org/10.1007/s12083-021-01121-6

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