Abstract
In recent times, there has been a great motivation toward research in the field of big data analysis and their incorporation into Internet of Things (IoT). The basic idea behind IoT is to ensure provision of services to clients all over the globe at any point of time from a pool of resources. With increasing volumes of data being handled, processed and stored in recent times, an efficient processing mechanism for these huge volumes of data is available in the form of big data handlers which ensure speedy provision of services without any delay overhead. Hence, big data and IoT put together prove to be the most effective tool and need of the hour for smooth handling and provision of services demanded by clients thus improving the overall quality of service. These two concepts have been effectively applied to developing an intelligent agricultural economic model which is quite heterogeneous in nature thus posing to be a great research challenge. Agro-economic models are quite essential and critical as agriculture forms the backbone of many developing nations across the globe. ANFIS model introduces the necessary intelligence for the big data analytic system to handle the heterogeneous nature of agro-economic input data and provide a suitable prediction. A sample data of five hundred details from five different subsets have been used in the experimental model and prediction of yield computed and compared against recent techniques.
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References
Aazam M, Khan I, Alsaffar AA, Huh E-N (2014) Cloud of things: integrating Internet of Things and cloud computing and the issues involved. In: Proceedings of 11th international Bhurban conference on applied science technology (IBCAST), Islamabad, Pakistan, pp 414–419
Batalla JM, Krawiec P (2014) Conception of ID layer performance at the network level for Internet of Things. Pers Ubiquit Comput 18:465–480
Cárdenas AA, Manadhata PK, Rajan SP (2013) Big data analytics for security. IEEE Secur Priv 11(6):74–76
Choi C, Esposito C, Wang H, Liu Z, Choi J (2018) Intelligent power equipment management based on distributed context-aware inference in smart cities. IEEE Commun Mag 56(7):212–217
Cozzolino G (2018) Using semantic tools to represent data extracted from mobile devices. In: 2018 IEEE international conference on information reuse and integration (IRI), IEEE, pp 530–536
Fahimifard SM, Salarpour M, Sabouhi M, Shirzady S (2009) Application of ANFIS to agricultural economic variables forecasting case study: poultry retail price. J Artif Intell 2(2):65–72
Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94
Khan Z, Anjum A, Kiani SL (2013) Cloud based big data analytics for future cities. In: Proceedings of Conference on utility and cloud computing, IEEE Computer Society, pp 381–386
Li C, Wang M (2013) Excerpts from the translation of challenges and opportunities with big data. J E-Sci Technol Appl 4(1):12–18
Lokers R, Knapen R, Janssen S, van Randen Y, Jansen J (2016) Analysis of big data technologies for use in agro-environmental. Environ Modell Softw 84:494–504
Mohamed N, Al-Jaroodi J (2014) Real-time big data analytics: applications and challenges. In: 2014 international conference on high performance computing & simulation (HPCS), vol 2, pp 305, 310
Mohammadi A, Rafiee A, Mohtasebi SS, Rafiee H (2010) Energy inputs—yield relationship and cost analysis of kiwifruit production in Iran. Renew Energy 35:1071–1075
Muhammad K, Hamza R, Ahmad J, Lloret J, Wang H, Baik SW (2018) Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Trans Ind Inf 14(8):3679–3689
Nicholls CI, Altieri MA, Dezanet A, Lana M, Feistauer D, Ouriques M (2004) A rapid, farmer-friendly agroecological method to estimate soil quality and crop health in vineyard systems. Biodynamics 8:33–39
Pacini C, Wossink A, Giesen G, Vazzana C, Huirne R (2003) Evaluation of sustainability of organic, integrated and conventional farming systems: a farm and field-scale analysis. Agr Ecosyst Environ 95(1):273–288
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 Gener Comput Syst 82:349–357
RahimiZade M, Madani H, Rezadost S, Mehraban A, Marjaniz A (2007) Energy analysis in ecological farming systems and strategies to increase energy efficiency. Sixth Natl Conf Energy 12(13):1–12
Schmolke A, Thorbek P, DeAngelis DL, Grimm V (2010) Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol Evol 25(8):479–486
Shih HS, Shyur HJ, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Modell 45(7):801–813
Song Z, Lazarescu MT, Tomasi R, Lavagno L, Spirito MA (2014) High-level Internet of Things applications development using wireless sensor networks. In: Internet of Things, Springer, Madrid, pp 75–109
Sun Y (2019) Analysis for center deviation of circular target under perspective projection. Eng Comput 36(7):2403–2413. https://doi.org/10.1108/EC-09-2018-0431
Uckelmann D, Harrison M, Michahelles F (2011) An architecture approach towards the future Internet of Things. Architecting the Internet of Things. Springer, Berlin, pp 1–24
Varatharajan R, Manogaran G, Priyan MK (2018) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 77(8):10195–10215
Villa F, Athanasiadis IN, Rizzoli AE (2009) Modelling with knowledge: a review of emerging semantic approaches to environmental modelling. Environ Model Softw 24(5):577–587
Wang Y-Z, Jin X-L, Chen X-Q (2013) Network big data: present and future. Chin J Comput 6(36):1125–1138
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Chen, X., Wang, H.H. & Tian, B. Multidimensional agro-economic model with soft-IoT framework. Soft Comput 24, 12187–12196 (2020). https://doi.org/10.1007/s00500-019-04657-1
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DOI: https://doi.org/10.1007/s00500-019-04657-1