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Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines


In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

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  1. Vimal S, Khari M, Dey N, Crespo RG, Robinson YH (2020) Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT, Computer Communications.

  2. Guerrero-Ibanez JA, Zeadally S, Contreras-Castillo J (2015) Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wirel Commun 22(6):122–128

    Google Scholar 

  3. Wang N, Zhang N, Wang M (2006) Wireless sensors in agriculture and food industry—Recent development and future perspective. Comput Electron Agric 50(1):1–14

    Google Scholar 

  4. Zhang Q, Reid JF, Noguchi N (1999) Agricultural vehicle navigation using multiple guidance sensors. In: Proceedings of the International Conference on Field and Service Robotics, pp 293–298. August

  5. Dey N, Mahalle PN, Shafi PM, Kimabahune VV, Hassanien AE (2020) Internet of things smart computing and technology: a roadmap ahead

  6. Relf-Eckstein JE, Ballantyne AT, Phillips PWB (2019) Farming Reimagined: A case study of autonomous farm equipment and creating an innovation opportunity space for broadacre smart farming. NJAS-Wageningen J Life Sci 90:100307

    Google Scholar 

  7. Vimal S, Khari M, Dey N, Crespo RG, Robinson YH (2020) Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Computer Communications.

  8. Khosravy M, Gupta N, Patel N, Dey N, Nitta N, Babaguchi N. (2020) Probabilistic stone’s blind source separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems, Computer Communications

  9. Vimal S, Khari M, Crespo RG, Kalaivani L, Dey N, Kaliappan M (2020) Energy enhancement using Multiobjective Ant colony optimisation with Double Q learning algorithm for IoT based cognitive radio networks. Computer Communications

  10. Sarkar M, Banerjee S, Badr Y, Sangaiah AK (2017) Configuring a trusted cloud service model for smart city exploration using hybrid intelligence. Int J Amb Comput Intell (IJACI) 8(3):1–21

    Google Scholar 

  11. Díaz M, Martín C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J Netw Comput Appl 67:99–117

    Google Scholar 

  12. Mach P, Becvar Z (2017) Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun Surv 19(3):1628–1656

    Google Scholar 

  13. Aram S, Troiano A, Pasero E (2012) Environment sensing using smartphone. In: 2012 IEEE Sensors applications symposium proceedings, pp 1–4. IEEE

  14. Gupta S, Khosravy M, Gupta N, Darbari H (2019) In-field failure assessment of tractor hydraulic system operation via pseudospectrum of acoustic measurements. Turk J Electr Eng CO 27(4):2718–2729

    Google Scholar 

  15. Gupta S, Gupta N, Tiwari BN, Khosravy M, Senzio-Savino B, Asharif F, Asharif MR (2016) Tractor oil pump fault diagnosis by pseudo-spectrum analysis of vehicle sound records. In: Proceedings of the 31st international technical conference on circuits/systems. Computers and communications

  16. Bohlin M, Forsgren M, Hoist A, Levin B, Aronsson M, Steinert R (2008) Reducing vehicle maintenance using condition monitoring and dynamic planning

  17. Gillblad D, Steinert R, Holst A (2008) Fault-tolerant incremental diagnosis with limited historical data. In: 2008 International conference on prognostics and health management. IEEE, pp 1–8

  18. Wu B, Wang H (2019) A lane identifying approach of the intelligent vehicle in complex condition: intelligent vehicle in complex condition. Int J Amb Comput Intell(IJACI) 10(4):25– 44

    Google Scholar 

  19. Ali AH, Atia A, Mostafa MSM (2017) Recognizing driving behavior and road anomaly using smartphone sensors. Int J Amb Comput Intell (IJACI) 8(3):22–37

    Google Scholar 

  20. Völgyesi P, Szilvási S, János S, Lédeczi Á (2011) External smart microphone for mobile phones. In: 2011 Fifth international conference on sensing technology. IEEE, pp 171–176

  21. Sarwar M, Soomro TR (2013) Impact of smartphone’s on society. Eur J Sci Res 98(2):216–226

    Google Scholar 

  22. Boutaba R, Salahuddin MA, Limam N, Sara S, Shahriar N, Estrada-Solano F, Caicedo OM (2018) A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Internet Serv Appl 9(1):16

    Google Scholar 

  23. Djedouboum AC, Ari A, Adamou A, Gueroui AM, Mohamadou A, Aliouat Z (2018) Big data collection in large-scale wireless sensor networks. Sensors 18(12):4474

    Google Scholar 

  24. Burnett K, Samavi S, Waslanderm S, Barfoot T, Schoellig A (2019) aUToTrack: a lightweight object detection and tracking system for the sae autodrive challenge. In: 2019 16th conference on Computer and Robot Vision (CRV). IEEE, pp 209–216



  27. Ganguly K, Gulati A, von Braun J (2017). Innovations spearheading the next transformations in India’s agriculture

  28. Lohento K, Sotannde M (2019) Business models and key success drivers of agtech start-ups CTA

  29. Coppola R, Morisio M (2016) Connected car: technologies, issues, future trends. ACM Comput Surv (CSUR) 49(3):1–36

    Google Scholar 

  30. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Information Fusion 42:146–157

    Google Scholar 

  31. Pasupa K, Sunhem W (2016) A comparison between shallow and deep architecture classifiers on small dataset. In: 2016 8Th international conference on information technology and electrical engineering (ICITEE). IEEE, pp 1–6

  32. Smiti P, Srivastava S, Rakesh N (2018) Video and audio streaming issues in multimedia application. In: 2018 8Th international conference on cloud computing, data science & engineering (confluence). IEEE, pp 360–365

  33. Leme BCC, Almeida LF, Bizarria JWP, Bizarria FCP, Soares AMS, Ramos MAC (2017) Development of a low-cost tool for semi-automatic classification and counting of particles in industrial oils. In: IEEE international conference on Industrial Engineering and Engineering Management (IEEM), p 2017

  34. Renius KT (2020) Tractor and implement. In: Fundamentals of tractor design. Springer, Cham, pp 217–260

  35. Khosravy M, Asharif MR, Yamashita K (2011) A theoretical discussion onthe foundation of stone’s blind source separation. Signal Image Video Process 5:379–388

    Google Scholar 

  36. Khosravy M, Alsharif MR, Yamashita K (2008) A probabilistic short-length linear predictability approach to blind source separation. In: ITC-CSCC:International Technical Conference on Circuits Systems, Computers and Communications, pp 381– 384

  37. Khosravy M, Alsharif MR, Yamashita K (2009) A pdf-matched modification to stone’s measure of predictability for blind source separation. In: International symposium on neural networks. Springer, pp 219–228

  38. Khosravy M (2010) Blind source separation and its application to speech, image and MIMO-OFDM communication systems, Ph.D. thesis University ofthe Ryukyus

  39. Lartillot O, Toiviainen P (2007) A Matlab toolbox for musical feature extraction from audio. In: International conference on digital audio effects, pp 237–244

  40. Giannakopoulos T, Pikrakis A (2014) Introduction to audio analysis: a MATLAB® approach. Academic, New York

    Google Scholar 

  41. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—Part i: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767

    Google Scholar 

  42. Sen PC, Hajra M, Ghosh M (2020) Supervised classification algorithms in machine learning: a survey and review. Springer, Singapore, pp 9–111

  43. Saouabi M, Ezzati A (2020) Data mining classification algorithms. Comput Sci 15(1):3893–94

    MathSciNet  MATH  Google Scholar 

  44. Gupta N, Khosravy M, Patel N, Senjyu T (2018) A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access 6:48455–48477

    Google Scholar 

  45. Singh G, Gupta N, Khosravy M (2015) New crossover operators for real coded genetic algorithm (RCGA). In: 2015 International conference on intelligent informatics and biomedical sciences (ICIIBMS). IEEE, pp 135–140

  46. Khosravy M, Gupta N, Patel N, Senjyu T (2020) Frontier applications of nature inspired computation. Springer, Cham

    Google Scholar 

  47. Gupta N, Khosravy M, Patel N, Dey N (2020) Mahela, O.P, Mendelian evolutionary theory optimization algorithm

  48. Gupta N, Khosravy M, Mahela OP, Patel N (2020) Plant biology inspired genetic algorithm: Superior efficiency to firefly optimizer. In: Applications of firefly algorithm and its variants. Springer, pp 193–219

  49. Gupta N, Khosravy M, Patel N, Sethi I (2018) Evolutionary optimization based on biological evolution in plants. Procedia Comput Sci 126:146–155

    Google Scholar 

  50. Gupta N, Khosravy M, Patel N, Gupta S, Varshney G (2020) Artificial neural network trained by plant genetics-inspired optimizer. In: Frontier applications of nature inspired computation, Springer

  51. Khosravy M, Gupta N, Patel N, Mahela OP, Varshney G (2020) Tracing the points in search space in plant biology genetics algorithm optimization. In: Frontier applications of nature inspired computation. Springer, Singapore, pp 180–195

  52. Gupta N, Khosravy M, Patel N, Mahela OP, Varshney G (2020) Plant Genetics-Inspired evolutionary optimization: a descriptive tutorial. In: Frontier applications of nature inspired computation. Springer, Singapore, pp 53–77

  53. Gupta N, Khosravy M, Patel N, Gupta S, Varshney G (2020) Evolutionary artificial neural networks: Comparative study on state of the art optimizers. In: Frontier applications of nature inspired computation, Springer

  54. Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Process 28(4):357–366

    Google Scholar 

  55. Schaffer J, Whitley D, Eshelman LJ (1992) David Combinations of genetic algorithms and neural networks: A survey of the state of the art. In: [Proceedings] COGANN-92: international workshop on combinations of genetic algorithms and neural networks. IEEE, pp 1–37

  56. Gupta N, Patel N, Tiwari BN, Khosravy M (2018) Genetic algorithm based on enhanced selection and log-scaled mutation technique. In: Proceedings of the Future Technologies Conference, Springer, Cham, pp. 730–748, November

  57. Tenenev VA, Shaura AS (2020) Solving general nonlinear programming problems with a genetic algorithm. Intellekt Sist Proizv 17(4):137–142

    Google Scholar 

  58. Yin C, Luo Z, Ni M, Cen K (1998) Predicting coal ash fusion temperature with a back-propagation neural network model, vol 77

  59. Johansson EM, Dowla FU, Goodman DM (1991) Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method. Int J Neural Sys 2(04):291–301

    Google Scholar 

  60. Kalathingal MSH, Basak S, Mitra J (2020). Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. J Food Process Eng e13128

  61. Samanta B, Al-Balushi KR, Al-Araimi SA (2001) Use of genetic algorithm and artificial neural network for gear condition diagnostics, Elsevier Science Ltd

  62. Also Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

  63. Author links open overlay panelMartin FodsletteMoller (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Google Scholar 

  64. Kaur H, Kaur M (2020) Fault classification in a transmission line using levenberg–marquardt algorithm based artificial neural network, Springer, Singapore

  65. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M, Siegert S (2018). pROC: display and analyze ROC curves. R Package Version 1

  66. Choi K, Fazekas G, Sandler M, Cho K (2018) A comparison of audio signal preprocessing methods for deep neural networks on music tagging. In: 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, pp 1870–1874

  67. Kumar S, Solanki VK, Choudhary KC, Selamat A, Crespo RG (2020) Comparative study on Ant Colony Optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT). IJIMAI 6(1):1–10

    Google Scholar 

  68. Agrawal P, Jayaswal P (2020) Diagnosis and classifications of bearing faults using artificial neural network and support vector machine. J Inst Eng (India) C 101(1):61–72

    Google Scholar 

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Correspondence to Mahdi Khosravy.

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CRediT authorship contribution statement

Neeraj Gupta: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing-original draft, Visualization, Validation. Mahdi Khosravy: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Validation. Nilesh Patel: Formal Analysis, Resources, Project administration, Writing - reviews & editing, Nilanjan Dey: Formal Analysis, Investigation, Data curation, Reviews & editing, Saurabh: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing, Visualization, Validation. Hemant Darbari: Supervision, Formal Analysis, Resources, Project administration, Rubén González Crespo: Supervision, Formal Analysis, Investigation, Data curation, Reviews & editing.

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Gupta, N., Khosravy, M., Patel, N. et al. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Appl Intell 50, 3990–4016 (2020).

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  • Green IoT
  • Agricultural machine
  • Artificial neural network
  • Evolutionary algorithm
  • Edge computation
  • Health-monitoring