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Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm

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Abstract

Agricultural applications can take advantage of improved services provided by the Internet of Things paradigms to manage data effectively. It is necessary to manage Quality of Service (QoS) characteristics to effectively monitor and measure the given services. Given how challenging it is to satisfy a user’s complicated requirements with a single service, this paper presents a QoS-aware method for sending agricultural information as a service and then combining those services, thus, known as service composition. The proposed work is divided into two phases. In the first phase, a fuzzy inference set is used to initialize the population whereas, in the second phase, the multi-objective evolutionary algorithm NSGA-II (Non-dominated sorting genetic algorithm) has been used to optimize the cost and time of services involved in apple crop production. Since evolutionary algorithms have a problem dealing with uncertainties so modification using fuzzy logic has been proposed to check its effectiveness in Service Composition Problem (SCP). In order to demonstrate the persuasiveness of our work, the proposed method is compared with the multi-objective genetic algorithm (MOGA), Gaining sharing knowledge (GSK) algorithm, and NSGA-II and it has been found that NSGA-II is giving more diversified and near to true Pareto solutions.

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References

  1. United Nations Department of Economic and Social Welfare (2017) The world population prospects: the 2017 revision. https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100. Accessed 15 Dec 2023

  2. Hunter MC, Smith RG, Schipanski ME, Atwood LW, Mortensen DA (2017) Agriculture in 2050: recalibrating targets for sustainable intensification. Bioscience 67:386–391. https://doi.org/10.1093/biosci/bix010

    Article  Google Scholar 

  3. Terence S, Purushothaman G (2020) Systematic review of internet of things in smart farming. Trans Emerg Telecommun Technol 31:3958. https://doi.org/10.1002/ett.v31.610.1002/ett.3958

    Article  Google Scholar 

  4. Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: a comprehensive review. Artif Intell Agric 4:81–95. https://doi.org/10.1016/j.aiia.2020.06.001

    Article  Google Scholar 

  5. Tao W, Zhao L, Wang G, Liang R (2021) Review of the internet of things communication technologies in smart agriculture and challenges. Comput Electron Agric 189:106352. https://doi.org/10.1016/j.compag.2021.106352

    Article  Google Scholar 

  6. Sharma S, Pathak BK, Kumar R (2023) Understanding of network resiliency in communication networks with its integration in internet of things - a survey. Electrica 23(2):318–328

    Article  Google Scholar 

  7. Chifu VR, Pop CB, Salomie I, Suia DS, Niculici AN (2011) Optimizing the semantic web service composition process using Cuckoo Search. In: Brazier FMT, Nieuwenhuis K, Pavlin G, Warnier M, Badica CE (eds) Intelligent distributed computing V. Studies in computational intelligence, vol 382. Springer, Heidelberg, pp 93–102

    Google Scholar 

  8. Kurdi H, Ezzat F, Altoaimy L, Ahmed SH, Youcef-Toumi K (2018) Multicuckoo: multi-cloud service composition using a Cuckoo-inspired algorithm for the internet of things applications. IEEE Access 6:56737–56749. https://doi.org/10.1109/ACCESS.2018.2872744

    Article  Google Scholar 

  9. Sharma V, Tripathi AK (2022) A systematic review of meta-heuristic algorithms in IoT based application. Array 14:100164. https://doi.org/10.1016/j.array.2022.100164

    Article  Google Scholar 

  10. Kapoor M, Pathak BK, Kumar R (2023) A nature-inspired meta-heuristic knowledge-based algorithm for solving multiobjective optimization problems. J Eng Math 143:5. https://doi.org/10.1007/s10665-023-10304-4

    Article  Google Scholar 

  11. Acharjya DP, Rathi R (2022) An integrated fuzzy rough set and real coded genetic algorithm approach for crop identification in smart agriculture. Multimed Tools Appl 81(24):35117–35142

    Article  Google Scholar 

  12. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  13. Qazi S, Khawaja BA, Farooq QU (2022) IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends. IEEE Access 10:21219–21235. https://doi.org/10.1109/ACCESS.2022.3152544

    Article  Google Scholar 

  14. Akhter R, Sofi SA (2022) Precision agriculture using IoT data analytics and machine learning. J King Saud Univ Comput Inf Sci 34(8):5602–5618

    Google Scholar 

  15. Ojha V, Abraham A, Snasel V (2019) Heuristic design of fuzzy inference systems: a review of three decades of research. Eng Appl Artif Intell 85:845–864. https://doi.org/10.1016/j.engappai.2019.08.010

    Article  Google Scholar 

  16. Valdez F, Melin P, Castillo O (2014) A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst Appl 41(14):6459–6466

    Article  Google Scholar 

  17. Guerrero M, Castillo O, Garcia M (2015) Fuzzy dynamic parameters adaptation in the Cuckoo Search Algorithm using fuzzy logic. Paper presented at IEEE Congress on Evolutionary Computation, Sendai, Japan, 2015, pp 441–448. https://doi.org/10.1109/CEC.2015.7256923

  18. Caraveo C, Valdez F, Castillo O (2017) A new meta-heuristic of optimization with dynamic adaptation of parameters using type-2 fuzzy logic for trajectory control of a mobile robot. Algorithms. 10(3):1–16

    Article  Google Scholar 

  19. Castillo O, Amador-Angulo L (2018) A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Inf Sci 460–461:476–496. https://doi.org/10.1016/j.ins.2017.10.032

    Article  Google Scholar 

  20. Olivas F, Valdez F, Melin P, Sombra A, Castillo O (2019) Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. Inf Sci 476:159–175. https://doi.org/10.1016/j.ins.2018.10.025

    Article  Google Scholar 

  21. Dela Cruz JR, Baldovino RG, Culibrina FB, Bandala AA, Dadios EP (2017) Fuzzy-based decision support system for smart farm water tank monitoring and control. Paper presented at 5th International Conference on Information and Communication Technology (ICoIC7), Melaka, Malaysia, 2017, pp 1–4. https://doi.org/10.1109/ICoICT.2017.8074669

  22. Lavanya G, Rani C, GaneshKumar P (2020) An automated low cost IoT based fertilizer intimation system for smart agriculture. Sustain Comput Inform Syst 28:100300. https://doi.org/10.1016/j.suscom.2019.01.002

    Article  Google Scholar 

  23. Benyezza H, Bouhedda M, Rebouh S (2021) Zoning irrigation smart system based on fuzzy control technology and IoT for water and energy saving. J Clean Prod 302:127001. https://doi.org/10.1016/j.jclepro.2021.127001

    Article  Google Scholar 

  24. Sharma RP, Ramesh D, Pal P, Tripathi S, Kumar C (2022) IoT-enabled IEEE 802.15. 4 WSN monitoring infrastructure-driven fuzzy-logic-based crop pest prediction. IEEE Internet Things J 9:3037–3045. https://doi.org/10.1109/JIOT.2021.3094198

    Article  Google Scholar 

  25. Kropp I, Nejadhashemi AP, Deb K, Abouali M, Roy PC, Adhikari U, Hoogenboom G (2019) A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agric Syst 173:289–302. https://doi.org/10.1016/j.agsy.2019.03.014

    Article  Google Scholar 

  26. Priya R, Ramesh D, Udutalapally V (2021) NSGA-2 optimized fuzzy inference system for crop plantation correctness index identification. IEEE Trans Sustain Comput 7(1):172–188

    Article  Google Scholar 

  27. Sharma RP, Dharavath R, Edla DR (2023) IOFT-FIS: internet of farm things based prediction for crop pest infestation using optimized fuzzy inference system. Internet of Things 21:100658. https://doi.org/10.1016/j.iot.2022.100658

    Article  Google Scholar 

  28. Kashyap N, Kumari AC, Chhikara R (2020) Service composition in IoT using genetic algorithm and particle swarm optimization. Open Comput Sci 10(1):56–64

    Article  Google Scholar 

  29. Kumar P, Shetty S, Janardhana DR, Manu AP (2022) QOS aware service composition in IoT using heuristic structure and genetic algorithm. Mathematical Statistician and Engineering Applications 71:750–766. https://doi.org/10.17762/msea.v71i3.215

    Article  Google Scholar 

  30. Danish E, Onder M (2020) Application of fuzzy logic for predicting of mine fire in underground coal mine. Saf Health Work 11:322–334. https://doi.org/10.1016/j.shaw.2020.06.005

    Article  Google Scholar 

  31. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic. Prentice hall, New Jersey

    Google Scholar 

  32. Pathak BK, Srivastava S (2014) Integrated fuzzy–HMH for project uncertainties in time–cost tradeoff problem. Appl Soft Comput 21:320–329. https://doi.org/10.1016/j.asoc.2014.03.035

    Article  Google Scholar 

  33. Caiado RGG, Scavarda LF, Gaviao LO, Ivson P, Mattos Nascimento DL, Garza-Reyes JA (2021) A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. Int J Prod Econ 231:107883. https://doi.org/10.1016/j.ijpe.2020.107883

    Article  Google Scholar 

  34. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC–15:116–132. https://doi.org/10.1109/TSMC.1985.6313399

    Article  Google Scholar 

  35. Ross TJ (2009) Fuzzy logic with engineering applications, 3rd edn. John Wiley and Sons, Chichester, United Kingdom

    Google Scholar 

  36. Kashyap N, Kumari AC, Chhikara R (2020) Multi-objective optimization using NSGA II for service composition in IoT. Procedia Comput Sci 167:1928–1933. https://doi.org/10.1016/j.procs.2020.03.214

    Article  Google Scholar 

  37. Ghiasi H, Pasini D, Lessard L (2011) A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems. Eng Optim 43:39–59. https://doi.org/10.1080/03052151003739598

    Article  Google Scholar 

  38. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  39. Dubey S, Pandey R, Gautam S (2013) Literature review on fuzzy expert system in agriculture. Int J Soft Comput 2(6):289–291

    Google Scholar 

  40. Mohamed AW, Hadi AA, Mohamed AK (2020) Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int J Mach Learn Cybern 11:1501–1529. https://doi.org/10.1007/s13042-019-01053-x

    Article  Google Scholar 

  41. Agrawal P, Ganesh T, Mohamed AW (2021) A novel binary gaining - sharing knowledge-based optimization algorithm for feature selection. Neural Comput Appl 33:5989–6008. https://doi.org/10.1007/s00521-020-05375-8

    Article  Google Scholar 

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Funding

No funding was received for conducting this study.

Author information

Author notes

  1. Bhupendra Kumar Pathak and Rajiv Kumar contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Wakhnaghat, Solan, 173234, Himachal Prdaesh, India

    Shalini Sharma & Rajiv Kumar

  2. Department of Mathematics, Jaypee University of Information Technology, Wakhnaghat, Solan, 173234, Himachal Prdaesh, India

    Bhupendra Kumar Pathak

Authors
  1. Shalini Sharma
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Contributions

Shalini Sharma has done MATLAB coding, document authoring, and manuscript formatting. Bhupendra Kumar Pathak has done the formal analysis, conceptualization, writing review, and editing as well. Rajiv Kumar has provided guidance and supervision.

Corresponding author

Correspondence to Bhupendra Kumar Pathak.

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Sharma, S., Pathak, B.K. & Kumar, R. Multi-objective Service Composition Optimization in Smart Agriculture Using Fuzzy-Evolutionary Algorithm. Oper. Res. Forum 5, 43 (2024). https://doi.org/10.1007/s43069-024-00319-7

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