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A decision support system for surveillance of smart cities via a novel aggregation operator on intuitionistic fuzzy sets

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
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Abstract

In recent times, terror attacks are becoming one of the most important issues of defense section for almost all the countries, especially for smart cities. Sometimes countries have to spend a lot of money and man power to protect and servile the cities, which is a challenging task for the smart cities to rely on technologies rather than man power for surveillance and protection. In this paper, a fuzzy multi criteria decision support system is utilized to prioritize the parts of a smart city which may lie under potential threat of terror attacks. For this purpose, a new aggregation operation on Intuitionistic fuzzy sets has been proposed. In addition, a case study on a smart city has been carried out which showcase the applicability of the proposed methodology.

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References

  1. Abbate T, Cesaroni F, Cinici MC, Villari M (2019) Business models for developing smart cities. A fuzzy set qualitative comparative analysis of an IoT platform. Technol Forecast Soc Change 142:183–193

    Article  Google Scholar 

  2. Alamaniotis M, Tsoukalas L (2017) Fuzzy multi-kernel approach in intelligent control of energy consumption in smart cities. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 1021–1028

  3. Awasthi A, Chauhan SS (2012) A hybrid approach integrating affinity diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Appl Math Model 36(2):573–584

    Article  Google Scholar 

  4. Bellman R, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17B:141–164

    Article  MathSciNet  Google Scholar 

  5. Bezdek JC, Tsao ECK, Pal NR (1992) Fuzzy Kohonen clustering networks. In: Proceedings of IEEE international conferences on terms, fuzzy systems, pp 1035–1046

  6. Bhunia SS, Dhar SK, Mukherjee N (2014) iHealth: a fuzzy approach for provisioning intelligent health-care system in smart city. In: 2014 IEEE 10th international conference on wireless and mobile computing, networking and communications (WiMob), pp 187–193

  7. Calvo T, Mayor G, Mesiar R (2002) Aggregation operators: new trends and applications. Physica-Verlag, Heidelberg

    Book  Google Scholar 

  8. Chen JH, Chen SM (2006) A new method for ranking Intuitionistic fuzzy sets for handling fuzzy risk analysis problems. In: Proceedings of the Ninth conference on information sciences, pp 1196–1199

  9. Chen SH (1985) Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets Syst 17:13–129

    MathSciNet  MATH  Google Scholar 

  10. Convertini N, Logrillo N, Manca F, Palmisano T (2018). ecommendation system using hybrid fuzzy association rules for human smart cities. In: 2018 AEIT international annual conference, pp 1–5

  11. Costa DG, Collotta M, Pau G, Duran-Faundez C (2017) A fuzzy-based approach for sensing, coding and transmission configuration of visual sensors in smart city applications. Sensors 17(1):93

    Article  Google Scholar 

  12. Cui L, Xie G, Qu Y, Gao L, Yang Y (2018) Security and privacy in smart cities: challenges and opportunities. Special section on challenges and opportunities of big data against cyber crime (IEEE)

  13. D'Aniello G, Gaeta A, Gaeta M, Loia V, Reformat MZ (2016). ollective awareness in smart city with fuzzy cognitive maps and fuzzy sets. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1554–1561

  14. De Maio C, Fenza G, Loia V, Orciuoli F (2017) Distributed online temporal fuzzy concept analysis for stream processing in smart cities. J Parallel Distrib Comput 110:31–41

    Article  Google Scholar 

  15. Deveci M, Pekaslan D, Canıtez F (2020) The assessment of smart city projects using zSlice type-2 fuzzy sets based Interval Agreement Method. Sustain Cities Soc 53:101889

    Article  Google Scholar 

  16. Dutta P (2016) Comparison of arithmetic operations of intuitionistic fuzzy sets: case study in risk assessment. Cybern Syst Int J 47(4):290–320

    Article  Google Scholar 

  17. Firmansyah HS, Supangkat SH, Arman AA, Giabbanelli PJ (2019) Identifying the components and interrelationships of smart cities in Indonesia: supporting policymaking via fuzzy cognitive systems. IEEE Access 7:46136–46151

    Article  Google Scholar 

  18. Flauzino R, da Silva IN, Spatti D, Silva JFR, Lourenço MA, Dantas IR (2015) Fuzzy-based orthogonal decomposition approach for fault diagnoses in distribution feeders of Smart Cities. In: 2015 IEEE PES innovative smart grid technologies Latin America (ISGT LATAM), pp 204–207

  19. Goala S, Dutta P (2018) Detection of area under potential threat via an advanced aggregation operator on generalized triangular fuzzy number. J Tibah Univ Sci. https://doi.org/10.1080/16583655.2018.1499172

    Article  Google Scholar 

  20. Goala S, Dutta P (2019) Intuitionistic fuzzy multi criteria decision making approach to crime linkage using resemblance function. Int J Appl Comput Math 5:112

    Article  Google Scholar 

  21. Grubesic TH (2006) On the application of fuzzy clustering for crime hot spot detection. J Quant Criminol 22(1):77–105

    Article  Google Scholar 

  22. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and application. Springer, New York

    Book  Google Scholar 

  23. Iqbal K, Adnan M, Abbas S, Hasan Z, Fatima A (2018) Intelligent transportation system (ITS) for smart-cities using mamdani fuzzy inference system. Int J Adv Comput Sci Appl 9(2):94–105

    Google Scholar 

  24. Kumar H, Singh MK, Gupta MP (2019) A policy framework for city eligibility analysis: TISM and fuzzy MICMAC-weighted approach to select a city for smart city transformation in India. Land Use Policy 82:375–390

    Article  Google Scholar 

  25. Lajmi H, Kammoun HM, Zouari M, Alimi AM, Rodriguez JM (2017). ype-2-fuzzy rule base system based on ECUs communication in a smart city vehicular environment. In: 2017 International conference on advanced systems and electric technologies (IC_ASET), pp 460–466

  26. Lakhno V, Matus Y, Malyukov V, Desyatko A, Hnatchenko T (2019) Smart city cybersecurity projects financing model in case of description of investors’ resources with fuzzy sets. In: 2019 IEEE international conference on advanced trends in information theory (ATIT), pp 249–252

  27. Li ST, Kuo SC, Tsai FC (2010) An intelligent decision-support model using FSOM and rule extraction for crime prevention. Expert Syst Appl 37:7108–7119

    Article  Google Scholar 

  28. Li X, Li H, Sun B, Wang F (2018) Assessing information security risk for an evolving smart city based on fuzzy and grey FMEA. J Intell Fuzzy Syst 34(4):2491–2501

    Article  Google Scholar 

  29. Liu P, Jin F (2012) A multi-attribute group decision-making method based on weighted geometric aggregation operators of interval-valued Intuitionistic fuzzy sets. Appl Math Model 36:2498–2509

    Article  MathSciNet  Google Scholar 

  30. Liu PD (2011) A weighted aggregation operator’s multi-attribute group decision-making method based on interval-valued Intuitionistic fuzzy sets. Expert Syst Appl 38:1053–1060

    Article  Google Scholar 

  31. Melo FS, Silva JLM, Macedo HT (2016). lood monitoring in smart cities based on fuzzy logic about urban open data. In: 2016 8th Euro American conference on telematics and information systems (EATIS), pp 1–5

  32. Mohamed B, Abdelhadi F, Adil B, Haytam H (2019) Smart city services monitoring framework using fuzzy logic based sentiment analysis and apache spark. In: 2019 1st International conference on smart systems and data science (ICSSD), pp 1–6

  33. Nadeem MW, Hussain M, Khan MA, Munir MU, Mehrban S (2019) Fuzzy-based model to evaluate city centric parameters for smart city. In: 2019 International conference on innovative computing (ICIC), pp 1–7

  34. Olszewski R, Turek A (2018) Using fuzzy geoparticipation methods to optimize the spatial development process in a smart city. In: 2018 IEEE 4th International Conference On Collaboration And Internet Computing (CIC), pp 430–437

  35. Olszewski R, Pałka P, Turek A, Kietlińska B, Płatkowski T, Borkowski M (2019) Spatiotemporal modeling of the smart city residents’ activity with multi-agent systems. Appl Sci 9(10):2059

    Article  Google Scholar 

  36. Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

    Article  Google Scholar 

  37. Riyaz R, Pushpa PV (2018) Air quality prediction in smart cities: a fuzzy-logic based approach. In: 2018 International conference on computational techniques, electronics and mechanical systems (CTEMS), pp 172–178

  38. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  39. Shamsuddin NH, Othman S, Selamat H (2012) Identification of potential crime area using analytical hierarchy process (AHP) and geographical information system (GIS). Int J Innov Comput 01(1):15–22

    Google Scholar 

  40. Sharma S, Dua A, Singh M, Kumar N, Prakash S (2018) Fuzzy rough set based energy management system for self-sustainable smart city. Renew Sustain Energy Rev 82:3633–3644

    Article  Google Scholar 

  41. Shrivastav AK, Ekata D (2012) Applicability of soft computing technique for crime forecasting: a preliminary investigation. Int J Comput Sci Eng Technol (IJCSET) 3(9):415–421

    Google Scholar 

  42. Sinha D (2018) The counterterror dimension to the planning of smart cities. Observer Research Foundation (ORF), p 241

  43. Szabó AB, Soproni PB (2017) Fuzzy-voting systems in smart cities. In: 2017 IEEE 15th international symposium on intelligent systems and informatics (SISY). 000297-000302. IEEE

  44. Topaloglu M, Yarkin F, Kaya T (2018) Solid waste collection system selection for smart cities based on a type-2 fuzzy multi-criteria decision technique. Soft Comput 22(15):4879–4890

    Article  Google Scholar 

  45. Torra V (2003) Information fusion in data mining. Springer, New York

    Book  Google Scholar 

  46. Tran Thi Hoang G, Dupont L, Camargo M (2019) Application of decision-making methods in smart city projects: a systematic literature review. Smart Cities 2(3):433–452

    Article  Google Scholar 

  47. Wang G, Li X (1998) The applications of interval-valued fuzzy numbers and interval-distribution numbers. Fuzzy Sets Syst 98:331–335

    Article  MathSciNet  Google Scholar 

  48. Xia X, Li T (2019) A fuzzy control model based on BP neural network arithmetic for optimal control of smart city facilities. Pers Ubiquit Comput 23(3–4):453–463

    Article  Google Scholar 

  49. Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187

    Article  Google Scholar 

  50. Xu ZS, Da QL (2003) An overview of operators for aggregating information”. Int J Intell Syst 18:953–969

    Article  Google Scholar 

  51. Xu ZS (2004) Uncertain multiple attribute decision making: methods and applications. Tsinghua University Press, Beijing

    Google Scholar 

  52. Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35:417–433

    Article  MathSciNet  Google Scholar 

  53. Yager RR, Kacprzyk J (1997) The ordered weighted averaging operator: theory and application. Kluwer, Norwell

    Book  Google Scholar 

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

    Article  Google Scholar 

  55. Zhao H, Xu Z, Ni M, Liu S (2010) Generalized aggregation operators for intuitionistic fuzzy sets. Int J Intell Syst 25:1–30

    Article  Google Scholar 

  56. Zimmermann HJ (1985) Fuzzy set theory and its applications. Springer, Dordrecht

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India

    Soumendra Goala, Palash Dutta & Pranjal Talukdar

  2. School of Computer Science and Engineering, Faculty of Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, J&K, 182320, India

    Deo Prakash

  3. Department of Physics, Shri Varshney (P.G.) College, Aligarh, U.P., 202001, India

    K. D. Verma

  4. Department of Electronics and Communication Engineering, Gandhi Institute for Technological Advancement, Bhubaneswar, India

    G. Palai

Authors
  1. Soumendra Goala
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  2. Deo Prakash
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  3. Palash Dutta
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  4. Pranjal Talukdar
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  5. K. D. Verma
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  6. G. Palai
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Correspondence to Deo Prakash.

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Goala, S., Prakash, D., Dutta, P. et al. A decision support system for surveillance of smart cities via a novel aggregation operator on intuitionistic fuzzy sets. Multimed Tools Appl 81, 22587–22608 (2022). https://doi.org/10.1007/s11042-021-11522-7

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  • DOI: https://doi.org/10.1007/s11042-021-11522-7

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