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Selective hypothesis testing in cognitive IoT sensor network

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Abstract

Objects with the ability to learn, reason, and observe their surroundings have recently been the focus of research in the Internet of Things (IoT). Due to this research advancement, a new field called cognitive IoT (CIoT) has emerged. The cognitive IoT combines the IoT with intelligence so that it can mimic the human brain with the help of its intelligent features. The testing of hypotheses is a key part of many CIoT inferential tasks. When dealing with large and diverse datasets, the problem becomes more complicated. Therefore, this study proposes a novel method for selective hypothesis testing that passes the sensory data to a total variation regularizer to reduce the influence of corrupted entries and employs probabilistic clustering to lessen the negative effects of missing entries. In addition, for each generated cluster, the plausibility value is calculated, and the cluster with the highest plausibility value is chosen. This is how large datasets are compressed into more manageable sets. Following this, we address data heterogeneity by designing suitable copula using the minimum Akaike information criterion (AIC). Further, it conducts selective hypothesis testing (SHT) after calculating the p value and adjusting those p values using various methods. Lastly, this study assesses how well the suggested algorithm performs on environmental data spanning 21.25 years. The results show that, even with extremely large and diverse datasets, the suggested method outperforms competing methods in terms of accuracy that lies between 75 and 93%.

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References

  1. Asthon K (2010) International Telecommunication Union. RFiD J 22:97–114

    Google Scholar 

  2. Jararweh Y, Fatima S, Jarrah M, Alzubi S (2023) Smart and sustainable agriculture: fundamentals, enabling technologies, and future directions. Comput Electr Eng 110:108799. https://doi.org/10.1016/j.compeleceng.2023.108799

    Article  Google Scholar 

  3. Andronie M, Lăzăroiu G, Karabolevski OL et al (2022) Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the internet of robotic things. Electronics 12:22. https://doi.org/10.3390/electronics12010022

    Article  Google Scholar 

  4. Tu S, Yu H, Badshah A et al (2023) Secure internet of vehicles (IoV) with decentralized consensus blockchain mechanism. IEEE Trans Veh Technol 72:11227–11236. https://doi.org/10.1109/TVT.2023.3268135

    Article  Google Scholar 

  5. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  6. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16:414–454. https://doi.org/10.1109/SURV.2013.042313.00197

    Article  Google Scholar 

  7. Palattella MR, Accettura N, Vilajosana X et al (2013) Standardized protocol stack for the internet of (important) things. IEEE Commun Surv Tutor 15:1389–1406. https://doi.org/10.1109/SURV.2012.111412.00158

    Article  Google Scholar 

  8. Vlacheas P, Giaffreda R, Stavroulaki V et al (2013) Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun Mag 51:102–111. https://doi.org/10.1109/MCOM.2013.6525602

    Article  Google Scholar 

  9. Zhang M, Zhao H, Zheng R et al (2012) Cognitive internet of things: concepts and application example. Int J Comput Sci Issues 9:151

    Google Scholar 

  10. Amzar D, Thamrin NM, Afzal S, Mohamad Z (2020) An IoT-based production monitoring system for assembly line in manufacture. Int J Integr Eng. https://doi.org/10.30880/ijie.2020.12.02.005

    Article  Google Scholar 

  11. Bui N, Castellani A, Casari P, Zorzi M (2012) The internet of energy: a web-enabled smart grid system. IEEE Netw 26:39–45. https://doi.org/10.1109/MNET.2012.6246751

    Article  Google Scholar 

  12. Tan S, De D, Song W-Z et al (2017) Survey of security advances in smart grid: a data driven approach. IEEE Commun Surv Tutor 19:397–422. https://doi.org/10.1109/COMST.2016.2616442

    Article  Google Scholar 

  13. Franchetti F et al (2017) High-Assurance SPIRAL: end-to-end guarantees for robot and car control. IEEE Control Syst 37:82–103. https://doi.org/10.1109/MCS.2016.2643244

    Article  MathSciNet  Google Scholar 

  14. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  MathSciNet  Google Scholar 

  15. Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc Ser B Stat Methodol. https://doi.org/10.1111/1467-9868.00346

    Article  MathSciNet  Google Scholar 

  16. Efron B (2004) Large-scale simultaneous hypothesis testing. J Am Stat Assoc 99:96–104. https://doi.org/10.1198/016214504000000089

    Article  Google Scholar 

  17. Li S, Wang X (2018) Fully distributed sequential hypothesis testing: algorithms and asymptotic analyses. IEEE Trans Inf Theory 64:2742–2758. https://doi.org/10.1109/TIT.2018.2806961

    Article  MathSciNet  Google Scholar 

  18. Veeravalli VV, Basar T, Poor HV (1993) Decentralized sequential detection with a fusion center performing the sequential test. IEEE Trans Inf Theory 39:433–442. https://doi.org/10.1109/18.212274

    Article  Google Scholar 

  19. Tsitsiklis JN (1993) Decentralized detection in advances in statistical signal processing. In: Poor HV, Thomas JB (eds) JAI Press. Greenwich

    Google Scholar 

  20. Fellouris G, Moustakides GV (2011) Decentralized sequential hypothesis testing using asynchronous communication. IEEE Trans Inf Theory 57:534–548. https://doi.org/10.1109/TIT.2010.2090249

    Article  MathSciNet  Google Scholar 

  21. Li S, Li X, Wang X, Liu J (2017) Decentralized sequential composite hypothesis test based on one-bit communication. IEEE Trans Inf Theory 63:3405–3424. https://doi.org/10.1109/TIT.2017.2693156

    Article  MathSciNet  Google Scholar 

  22. Fisher RA (1992) Statistical methods for research workers. In: breakthroughs in statistics. Springer, New York

    Book  Google Scholar 

  23. Trust B (2016) Biometrika trust on the use and interpretation of certain test criteria for purposes of statistical inference : part I Neyman J and Pearson ES (ed.) Published by : Oxford University Press on behalf of Biometrika Trust Stable. 20:175–240

  24. Jeffreys H (1998) The theory of probability. OUP Oxford, Oxford

    Book  Google Scholar 

  25. Allakany A, Yadav G, Paul K, Okamura K (2020) Detection and mitigation of lfa attack in sdn-iot network. In: Workshops of the International Conference on Advanced Information Networking and Applications. Springer, pp 1087–1096

  26. Wu J, Wang C, Yu Y et al (2020) Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things. ETRI J 42:976–986. https://doi.org/10.4218/etrij.2019-0388

    Article  Google Scholar 

  27. Li F, Xie R, Wang Z et al (2020) Online distributed IoT security monitoring with multidimensional streaming big data. IEEE Internet Things J 7:4387–4394. https://doi.org/10.1109/JIOT.2019.2962788

    Article  Google Scholar 

  28. Kassab R, Simeone O, Popovski P (2020) Fog-based detection for random-access IoT networks with per-measurement preambles. In: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, pp 1–5

  29. Walshe M, Epiphaniou G, Al-Khateeb H et al (2019) Non-interactive zero knowledge proofs for the authentication of IoT devices in reduced connectivity environments. Ad Hoc Netw 95:101988. https://doi.org/10.1016/j.adhoc.2019.101988

    Article  Google Scholar 

  30. Tarighati A, Gross J, Jalden J (2017) Decentralized hypothesis testing in energy harvesting wireless sensor networks. IEEE Trans Signal Process 65:4862–4873. https://doi.org/10.1109/TSP.2017.2716909

    Article  MathSciNet  Google Scholar 

  31. Siegmund DO, Zhang NR, Yakir B (2011) False discovery rate for scanning statistics. Biometrika 98:979–985. https://doi.org/10.1093/biomet/asr057

    Article  MathSciNet  Google Scholar 

  32. Sun W, Reich BJ, Tony Cai T et al (2015) False discovery control in large-scale spatial multiple testing. J R Stat Soc Ser B Stat Methodol 77:59–83. https://doi.org/10.1111/rssb.12064

    Article  MathSciNet  Google Scholar 

  33. Lánczky A, Győrffy B (2021) Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J Med Internet Res 23:e27633. https://doi.org/10.2196/27633

    Article  Google Scholar 

  34. Golz M, Zoubir AM, Koivunen V (2022) Multiple hypothesis testing framework for spatial signals. IEEE Trans Signal Inf Process over Networks 8:771–787. https://doi.org/10.1109/TSIPN.2022.3190735

    Article  MathSciNet  Google Scholar 

  35. Gilani A, Belhadj Amor S, Salehkalaibar S, Tan VYF (2019) Distributed hypothesis testing with privacy constraints. Entropy 21:478. https://doi.org/10.3390/e21050478

    Article  MathSciNet  Google Scholar 

  36. Negm E (2023) Internet of Things (IoT) acceptance model—assessing consumers’ behavior toward the adoption intention of IoT. Arab Gulf J Sci Res. https://doi.org/10.1108/AGJSR-09-2022-0183

    Article  Google Scholar 

  37. Khan S, Thapa C, Durrani S, Camtepe S (2023) Access-based lightweight physical layer authentication for the internet of things devices. IEEE Internet Things J 11:1–13

    Google Scholar 

  38. Jha V, Tripathi P (2024) Ad Hoc networks multiple hypothesis testing in cognitive IoT sensor network. Ad Hoc Netw 162:103559. https://doi.org/10.1016/j.adhoc.2024.103559

    Article  Google Scholar 

  39. Jha V, Tripathi P (2024) Decentralized multiple hypothesis testing in Cognitive IOT using massive heterogeneous data. Springer, New York

    Book  Google Scholar 

  40. Hussien M, Nguyen KK, Cheriet M (2023) A learning framework for bandwidth-efficient distributed inference in wireless IoT. IEEE Sens J 23:17656–17666. https://doi.org/10.1109/JSEN.2023.3283923

    Article  Google Scholar 

  41. Gölz M, Zoubir AM, Koivunen V (2023) Spatial inference using censored multiple testing with Fdr control. In: ICASSP 2023—2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1–5

  42. Liu X, Zhang Z, Wang L (2022) Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behav Res Methods. https://doi.org/10.3758/s13428-022-01860-1

    Article  Google Scholar 

  43. Chen L, Zhou J, Lin L (2023) Hypothesis testing for populations of networks. Commun Stat Theory Methods 52:3661–3684. https://doi.org/10.1080/03610926.2021.1977961

    Article  MathSciNet  Google Scholar 

  44. Vieira F, Leenders R, McFarland D, Mulder J (2023) A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures. Springer, Tokyo

    Google Scholar 

  45. Wu Y, Jing T, Gao Q et al (2023) Game-theoretic physical layer authentication for spoofing detection in internet of things. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2022.12.016

    Article  Google Scholar 

  46. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate : a practical and powerful approach to multiple testing Author (s): Yoav Benjamini and Yosef Hochberg Source. J R Stat Soc Ser B Methodol 57:289–300

    Article  Google Scholar 

  47. Dunnett CW (1955) A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50:1096–1121

    Article  Google Scholar 

  48. deRidder S, Shahraeeni M, Gerea C (2019) The alternating direction method of multipliers for total variation regularisation in joint time-lapse full waveform inversion. In: SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists, pp 1375–1379

  49. Wang Y, Yang J, Yin W, Zhang Y (2008) A new alternating minimization algorithm for total variation image reconstruction. SIAM J Imaging Sci 1:248–272. https://doi.org/10.1137/080724265

    Article  MathSciNet  Google Scholar 

  50. Goldstein T, Osher S (2009) The split Bregman method for L1-regularized problems. SIAM J Imaging Sci 2:323–343. https://doi.org/10.1137/080725891

    Article  MathSciNet  Google Scholar 

  51. Boyd S (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends® Mach Learn 3:1–122. https://doi.org/10.1561/2200000016

    Article  Google Scholar 

  52. Yin W, Osher S, Goldfarb D, Darbon J (2008) Bregman iterative algorithms for $\ell_1$-minimization with applications to compressed sensing. SIAM J Imaging Sci 1:143–168. https://doi.org/10.1137/070703983

    Article  MathSciNet  Google Scholar 

  53. McLachlan GJ, Lee SX, Rathnayake SI (2019) Finite mixture models. Annu Rev Stat Appl 6:355–378. https://doi.org/10.1146/annurev-statistics-031017-100325

    Article  MathSciNet  Google Scholar 

  54. Jaynes ET (1988) How does the brain do plausible reasoning? Springer, Dordrecht

    Book  Google Scholar 

  55. Armstrong RA (2014) When to use the Bonferroni corrrection. Ophthalmic Phys Opt 34:502–508

    Article  Google Scholar 

  56. Streiner DL, Norman GR (2011) Correction for multiple testing. Chest 140:16–18. https://doi.org/10.1378/chest.11-0523

    Article  Google Scholar 

  57. Sarkar SK, Chang CK, Chang CK (1997) The simes method for multiple hypothesis testing with positively dependent test statistics. J Am Stat Assoc 92:1601–1608. https://doi.org/10.1080/01621459.1997.10473682

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Raipur, Chhattisgarh, India

    Vidyapati Jha & Priyanka Tripathi

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  1. Vidyapati Jha
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  2. Priyanka Tripathi
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Contributions

Vidyapati Jha contributed to methodology, investigation, formal analysis, conceptualization, resources, visualization, validation, writing—original draft preparation, and writing—review and editing; Priyanka Tripathi supervised the study. All authors reviewed the manuscript.

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Correspondence to Vidyapati Jha.

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Jha, V., Tripathi, P. Selective hypothesis testing in cognitive IoT sensor network. J Supercomput 81, 133 (2025). https://doi.org/10.1007/s11227-024-06515-w

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