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research-article

Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home

Authors:

Pradip Kumar Sharma,

Alireza Jolfaei,

Dhananjay SinghAuthors Info & Claims

The Journal of Supercomputing, Volume 76, Issue 4

Pages 2479 - 2502

https://doi.org/10.1007/s11227-019-02998-0

Published: 01 April 2020 Publication History

Abstract

Human activity recognition has been a topic of attraction among researchers and developers because of its enormous usage in widespread region of human life. The varied human activities and the way they are executed at individual level are the main challenges to be recognized in human behavior modeling. This paper proposes a novel methodology that recognizes human activities from the behavior of individuals in a smart home environment. The dataset considered in this work is captured using Bluetooth low energy, a popular technology for indoor localization. The proposed framework is a binary cuckoo search-based stacking model that collectively exploits multiple base learners for human activities recognition from the gathered accelerometer sensors data mounted on wearable and mobile devices. The work is tested on the newly developed SPHERE dataset to recognize user activities in smart home environment. The experimental results confirm the effectiveness of the proposed approach, which outperforms MLP, DT, KNN, SGD, NB, RF, LR and SVM classifiers on the dataset and gives a high predictive accuracy value of 93.77% via a tenfold cross-validation. The proposed approach gives a better performance at the expense of more computation time, that is, due to the integration of cuckoo search metaheuristic algorithm.

References

[1]

Atzori L, Iera A, and Morabito G The internet of things: a survey Comput Netw 2010 54 15 2787-2805

[2]

Nanda A, Puthal D, Rodrigues JJPC, and Kozlov SA Internet of autonomous vehicles communications security: overview, issues, and directions IEEE Wirel Commun 2019 26 4 60-65

[3]

Baccarelli E, Scarpiniti M, Naranjo PGV, and Vaca-Cardenas L Fog of social IoT: when the fog becomes social IEEE Netw 2018 32 4 68-80

[4]

Shojafar M, Pooranian Z, Naranjo PGV, and Baccarelli E FLAPS: bandwidth and delay-efficient distributed data searching in fog-supported P2P content delivery networks J Supercomput 2017 73 12 5239-5260

[5]

Naranjo PGV, Baccarelli E, and Scarpiniti M Design and energy-efficient resource management of virtualized networked fog architectures for the real-time support of IoT applications J Supercomput 2018 74 6 2470-2507

[6]

El-Sayed H, Sankar S, Prasad M, Puthal D, Gupta A, Mohanty M, and Lin C-T Edge of things: the big picture on the integration of edge, iot and the cloud in a distributed computing environment IEEE Access 2017 6 1706-1717

[7]

Guerrero-Ibáñez J, Zeadally S, and Contreras-Castillo J Sensor technologies for intelligent transportation systems Sensors 2018 18 4 1212

[8]

Koydemir HC and Ozcan A Wearable and implantable sensors for biomedical applications Annu Rev Anal Chem 2018 11 127-146

[9]

Montori F, Bedogni L, and Bononi L A collaborative internet of things architecture for smart cities and environmental monitoring IEEE Internet Things J 2017 5 2 592-605

[10]

Basiri A, Lohan ES, Moore T, Winstanley A, Peltola P, Hill C, Amirian P, and e Silva PF Indoor location based services challenges, requirements and usability of current solutions. Comput Sci Rev 2017 24 1-12

[11]

Shit RC, Sharma S, Puthal D, and Zomaya AY Location of things (lot): a review and taxonomy of sensors localization in iot infrastructure IEEE Commun Surv Tutor 2018 20 3 2028-2061

[12]

Shit RC, Sharma S, Puthal D, James P, Pradhan B, van Moorsel A, Zomaya AY, and Ranjan R Ubiquitous localization (UbiLoc): a survey and taxonomy on device free localization for smart world IEEE Commun Surv Tutor 2019

[13]

He S and Chan S-HG Wi-fi fingerprint-based indoor positioning: recent advances and comparisons IEEE Commun Surv Tutor 2015 18 1 466-490

[14]

Bahl P, Padmanabhan VN, Bahl V, Padmanabhan V (2000) RADAR: an in-building RF-based user location and tracking system

[15]

Mohammadi SA, Amoozegar S, Jolfaei A, Mirghadri A (2011) Enhanced adaptive bandwidth tracking using mean shift algorithm. In: 2011 IEEE 3rd International Conference on Communication Software and Networks. IEEE, pp 494–498

[16]

Liu S, Jiang Y, and Striegel A Face-to-face proximity estimationusing bluetooth on smartphones IEEE Trans Mob Comput 2013 13 4 811-823

[17]

Zhao X, Xiao Z, Markham A Trigoni N, Ren Y (2014) Does BTLE measure up against WiFi? A comparison of indoor location performance. In: European Wireless 2014; 20th European Wireless Conference. VDE, pp 1–6

[18]

Chen Y, Lymberopoulos D, Liu J, Priyantha B (2012) Fm-based indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM, pp 169–182

[19]

Yoon S, Lee K, Rhee I (2013) Fm-based indoor localization via automatic fingerprint DB construction and matching. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services. ACM, pp 207–220

[20]

Ni LM, Liu Y, Lau YC, Patil AP (2003) LANDMARC: indoor location sensing using active RFID. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003 (PerCom 2003). IEEE, pp 407–415

[21]

Zhuo W, Zhang B, Chan SHG, Chang EY (2012) Error modeling and estimation fusion for indoor localization. In: 2012 IEEE International Conference on Multimedia and Expo. IEEE, pp. 741–746

[22]

Sun Z, Purohit A, Chen K, Pan S, Pering T, Zhang P (2011) PANDAA: physical arrangement detection of networked devices through ambient-sound awareness. In: Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, pp 425–434

[23]

Huang W, Xiong Y, Li X-Y, Lin H, Mao X, Yang P, Liu Y (2014) Shake and walk: acoustic direction finding and fine-grained indoor localization using smartphones. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, pp 370–378

[24]

Kuo Y-S, Pannuto P, Hsiao K-J, Dutta P (2014) Luxapose: indoor positioning with mobile phones and visible light. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. ACM, pp 447–458

[25]

Yang Z, Wang Z, Zhang J, Huang C, Zhang Q (2015) Wearables can afford: Light-weight indoor positioning with visible light. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services. ACM, pp 317–330

[26]

Chung J, Donahoe M, Schmandt C, Kim I-J, Razavai P, Wiseman M (2011) Indoor location sensing using geo-magnetism. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services. ACM, pp 141–154

[27]

Xie H, Gu T, Tao X, Ye H, Lv J (2014) MaLoc: A practical magnetic fingerprinting approach to indoor localization using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, pp 243–253

[28]

Cedillo P, Sanchez C, Campos K, Bermeo A (2018) A systematic literature review on devices and systems for ambient assisted living: solutions and trends from different user perspectives. In: 2018 International Conference on eDemocracy & eGovernment (ICEDEG). IEEE, pp 59–66

[29]

Arifoglu D and Bouchachia A Activity recognition and abnormal behaviour detection with recurrent neural networks Procedia Comput Sci 2017 110 86-93

[30]

Chen Y and Shen C Performance analysis of smartphone-sensor behavior for human activity recognition IEEE Access 2017 5 3095-3110

[31]

Yang C, Puthal D, Mohanty SP, and Kougianos E Big-sensing-data curation for the cloud is coming: a promise of scalable cloud-data-center mitigation for next-generation iot and wireless sensor networks IEEE Consum Electron Mag 2017 6 4 48-56

[32]

Yang X-S and Deb S Cuckoo search: recent advances and applications Neural Comput Appl 2014 24 1 169-174

[33]

Wang F, He XS, Wang Y, and Yang SM Markov model and convergence analysis based on cuckoo search algorithm Comput Eng 2012 38 11 180-185

[34]

Naik BB, Singh D, Samaddar AB, Jung S (2018) Developing a cloud computing data center virtual machine consolidation based on multi-objective hybrid fruit-fly cuckoo search algorithm. In: 2018 IEEE 5G World Forum (5GWF), Santa Clara, California, USA, 9–11 July 2018

[35]

Clerc M and Kennedy J The particle swarm-explosion, stability, and convergence in a multidimensional complex space IEEE Trans Evolut Comput 2002 6 1 58-73

[36]

Gandomi AH, Yang X-S, and Alavi AH Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems Eng Comput 2013 29 1 17-35

[37]

Mishra SK, Puthal D, Rodrigues JJPC, Sahoo B, and Dutkiewicz E Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications IEEE Trans Ind Inform 2018 14 10 4497-4506

[38]

Sahoo KS, Puthal D, Obaidat MS, Sarkar A, Mishra SK, and Sahoo B On the placement of controllers in software-defined-WAN using meta-heuristic approach J Syst Softw 2018 145 180-194

[39]

Sadiq FI, Selamat A, Ibrahim R, et al. Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing J Teknol 2015 77 13 11-19

[40]

Nurhanim K, Elamvazuthi I, Izhar LI, Ganesan T (2017) Classification of human activity based on smartphone inertial sensor using support vector machine. In: 2017 IEEE 3rd International Symposium in Robotics and Manufacturing Automation (ROMA). IEEE, pp 1–5

[41]

Chen Z, Zhu Q, Soh YC, and Zhang L Robust human activity recognition using smartphone sensors via CT-PCA and online SVM IEEE Trans Ind Inform 2017 13 6 3070-3080

[42]

Tran DN, Phan DD (2016) Human activities recognition in android smartphone using support vector machine. In: 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). IEEE, pp 64–68

[43]

Münzner S, Schmidt P, Reiss A, Hanselmann M, Stiefelhagen R, ürichen RD (2017) CNN-based sensor fusion techniques for multimodal human activity recognition. In: Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, pp 158–165

[44]

Kanjo E, Younis EMG, and Sherkat N Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach Inf Fusion 2018 40 18-31

[45]

Zhou B, Yang J, and Li Q Smartphone-based activity recognition for indoor localization using a convolutional neural network Sensors 2019 19 3 621

[46]

Markopoulos P, Zlotnikov S, and Ahmad F Adaptive radar-based human activity recognition with L1-norm linear discriminant analysis IEEE J Electromagn RF Microw Med Biol 2019 3 120-126

[47]

Tegou T, Kalamaras I, Tsipouras M, Giannakeas N, Votis K, and Tzovaras D A low-cost indoor activity monitoring system for detecting frailty in older adults Sensors 2019 19 3 452

[48]

Wang W, Liu AX, Shahzad M, Ling K, Lu S (2015) Understanding and modeling of wifi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, pp 65–76

[49]

Barsocchi P, Crivello A, La Rosa D, Palumbo F (2016) A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, pp 1–8

[50]

Ahmad U, Song H, Bilal A, Alazab M, Jolfaei A (2018) Secure passive keyless entry and start system using machine learning. In: International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage. Springer, pp 304–313

[51]

Puthal D, Mohanty SP, Bhavake SA, Morgan G, and Ranjan R Fog computing security challenges and future directions [energy and security] IEEE Consum Electron Mag 2019 8 3 92-96

[52]

https://github.com/rymc/a-dataset-for-indoor-localization-using-a-smart-home-in-a-box

[53]

Mohri M (2018) Computational complexity of machine learning algorithms. https://thekerneltrip.com/machine/learning/computational-complexity-learning-algorithms/

[54]

Williamson DF, Parker RA, and Kendrick JS The box plot: a simple visual method to interpret data Ann Intern Med 1989 110 11 916-921

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Zheng YWei NLiu Y(2022)Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge ComputingMobile Information Systems10.1155/2022/48408012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4840801
Toshpulatov MLee WLee SHaghighian Roudsari A(2022)Human pose, hand and mesh estimation using deep learning: a surveyThe Journal of Supercomputing10.1007/s11227-021-04184-778:6(7616-7654)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04184-7
Liao ZSamuel RKrishnamoorthy S(2022)RETRACTED ARTICLE: Computer vision for facial analysis using human–computer interaction modelsInternational Journal of Speech Technology10.1007/s10772-021-09953-625:2(379-389)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10772-021-09953-6
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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 76, Issue 4

Apr 2020

917 pages

ISSN:0920-8542

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© Springer Science+Business Media, LLC, part of Springer Nature 2019.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2020

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Cited By

Zheng YWei NLiu Y(2022)Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge ComputingMobile Information Systems10.1155/2022/48408012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4840801
Toshpulatov MLee WLee SHaghighian Roudsari A(2022)Human pose, hand and mesh estimation using deep learning: a surveyThe Journal of Supercomputing10.1007/s11227-021-04184-778:6(7616-7654)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04184-7
Liao ZSamuel RKrishnamoorthy S(2022)RETRACTED ARTICLE: Computer vision for facial analysis using human–computer interaction modelsInternational Journal of Speech Technology10.1007/s10772-021-09953-625:2(379-389)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10772-021-09953-6
Zhang BVelmayil VSivakumar V(2021)A deep learning model for innovative evaluation of ideological and political learningProgress in Artificial Intelligence10.1007/s13748-021-00253-312:2(119-131)Online publication date: 25-Aug-2021
https://dl.acm.org/doi/10.1007/s13748-021-00253-3
Ghosh SSaha CMolakathala NGhosh SSingh D(2021)reSenseNet: Ensemble Early Fusion Deep Learning Architecture for Multimodal Sentiment AnalysisIntelligent Human Computer Interaction10.1007/978-3-030-98404-5_62(689-702)Online publication date: 20-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-98404-5_62

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