Abstract
Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.
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References
Abououf M, Singh S, Otrok H, Mizouni R, Ouali A (2018) Gale-shapley matching game selection—A framework for user satisfaction. IEEE Access 7:3694–3703
Allahbakhsh M, Arbabi S, Galavii M, Daniel F, Benatallah B (2019) Crowdsourcing planar facility location allocation problems. Computing 101(3):237–261
Amagata D, Hara T, Sasaki Y, Nishio S (2017) Efficient cluster-based top-k query routing with data replication in MANETs. Soft Comput 21(15):4161–4178
Arsel Z (2017) Asking questions with reflexive focus: a tutorial on designing and conducting interviews. J Consum Res 44(4):939–948
Bai F, Krishnamachari B (2010) Exploiting the wisdom of the crowd: localized, distributed information-centric VANETs [Topics in automotive networking]. IEEE Commun Mag 48(5):138–146
De Mulder W, Bethard S, Moens MF (2015) A survey on the application of recurrent neural networks to statistical language modeling. Comput Speech Lang 30(1):61–98
Dissing AS, Lakon CM, Gerds TA, Rod NH, Lund R (2018) Measuring social integration and tie strength with smart phone and survey data. PLOS One 13(8):e0200678
Doan A, Ramakrishnan R, Halevy AY (2011) Crowdsourcing systems on the world-wide web. Commun ACM 54(4):86–96
Fan J, Zhang M, Kok S, Lu M, Ooi BC (2015) Crowdop: query optimization for declarative crowdsourcing systems. IEEE Trans Knowl Data Eng 27(8):2078–2092
Fleuret F, Berclaz J, Lengagne R, Fua P (2017) Multicamera people tracking with a probabilistic occupancy map. IEEE Trans Pattern Anal Mach Intell 30(2):267–282
Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39
Hashem T, Ali ME, Kulik L, Tanin E, Quattrone A (2013) Protecting privacy for group nearest neighbor queries with crowdsourced data and computing. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, 8 Sep 2013, pp 559–562
Hashem T, Hasan R, Salim F, Mahin MT (2018) Crowd-enabled processing of trustworthy, privacy-enhanced and personalised location based services with quality guarantee. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(4):167
Jaeger MD, Dunn Cavelty M (2019) From madness to wisdom: intelligence and the digital crowd. Intell Natl Secur 34(3):329–343
Kim J, Nam B (2018) Co-processing heterogeneous parallel index for multi-dimensional datasets. J Parallel Distrib Comput 113:195–203
Koçanaoğulları A, Marghi YM, Akçakaya M, Erdoğmuş D (2018) Optimal query selection using multi-armed bandits. IEEE Signal Process Lett 25(12):1870–1874
Kumar D, Mehrotra D, Bansal R (2019) Query optimization in crowd-sourcing using multi-objective ant lion optimizer. Int J Inf Technol Web Eng (IJITWE) 14(4):50–63
Li C, Zhao C, Zhu L, Lin H, Li J (2014) Geographic routing protocol for vehicular ad hoc networks in city scenarios: a proposal and analysis. Int J Commun Syst 27(12):4126–4143
Nir G, Hor S, Karimi D, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Wilson RS, Iczkowski KA (2018) Automatic grading of prostate cancer in digitized histopathology images: learning from multiple experts. Med Image Anal 50:167–180
Park CS, Lim S (2015) Efficient processing of keyword queries over graph databases for finding effective answers. Inf Process Manag 51(1):42–57
Rahman H, Roy SB, Thirumuruganathan S, Amer-Yahia S, Das G (2019) Optimized group formation for solving collaborative tasks. VLDB J 28(1):1–23
Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710
Sundararaj Vinu (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126
Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325
Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288
Szwajlik A (2018) Characteristic and typology of crowd motivators to crowsourcing platform contribution. Eur J Serv Manag 27(3/2):445–451
Venetis P, Garcia-Molina H, Huang K, Polyzotis N (2012) Max algorithms in crowdsourcing environments. In: Proceedings of the 21st international conference on World Wide Web, ACM, 16 Apr 2012, pp 989–998
Viappiani P, Boutilier C (2010) Optimal bayesian recommendation sets and myopically optimal choice query sets. In: Advances in neural information processing systems 2010, pp 2352–2360
Vinu S (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197
Wang T, Cao Y, Zhou Y, Li P (2016) A survey on geographic routing protocols in delay/disruption tolerant networks. Int J Distrib Sens Netw 12(2):3174670
Wang X, Huang C, Yao L, Benatallah B, Dong M (2018) A survey on expert recommendation in community question answering. J Comput Sci Technol 33(4):625–653
Xi Y, Wang N, Wu X, Bao Y, Zhou W (2017) CrowdIQ: a declarative crowdsourcing platform for improving the quality of web tables. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) joint conference on web and big data. Springer, Cham, pp 324–328
Xintong G, Hongzhi W, Song Y, Hong G (2014) Brief survey of crowdsourcing for data mining. Expert Syst Appl 41(17):7987–7994
Yan Y, Rosales R, Fung G, Subramanian R, Dy J (2014) Learning from multiple annotators with varying expertise. Mach Learn 95(3):291–327
Zhang D, Li Y, Cao X, Shao J, Shen HT (2018) Augmented keyword search on spatial entity databases. VLDB J 27(2):225–244
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Bhaskar, N., Kumar, P.M. Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm. Soft Comput 24, 13037–13050 (2020). https://doi.org/10.1007/s00500-020-04722-0
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DOI: https://doi.org/10.1007/s00500-020-04722-0