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RETRACTED ARTICLE: Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital

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This article was retracted on 06 June 2022

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Abstract

Recent innovative development in Internet of Things, usage of wearable devices in body area networks has become smarter and has reached new perception, in terms of connectivity and diagnosis. Energy consumption, latency and network coverage are some of the research issues occurred in IoT based body area network. To address latency issue, in this work, networks could adopt to the Fog architectures to perform computation, data analysis and storage near to the users. To improve battery life period of sensor nodes an intelligent proactive routing algorithms for body-fog-cloud area networks are needed. In this research a novel algorithm called as modified WORN-DEAR algorithms for BAN-IoT networks is proposed to achieve energy efficient routing and scheduling using the principle of deep learning based adaptive distance-energy features. This work is simulated on Cooja-Contiki network simulator and implemented on different test beds with ESP8266 WIFI SoC interface. Final results were compared with existing WORN-DEAR algorithm and achieved higher accuracy of 98% in LSTM compare to other machine learning algorithms such as logistic regression, naïve bias, SVM and KNN.

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References

  • Agrawal H, Dhall R, Iyer KS, Chetlapalli V (2020) An improved energy efficient system for IoT enabled precision agriculture. J Ambient Intell Humaniz Comput 11:2337–2348

    Article  Google Scholar 

  • Ahmed S, Javaid N, Yousaf S, Ahmad A, Sandhu MM, Imran M, Khan ZA, Alrajeh N (2015) Co-LAEEBA: Cooperative link aware and energy efficient protocol for wireless body area networks. Comput Hum Behav 51:1205–1215

    Article  Google Scholar 

  • Amira M, Elias J, Mehaoua A (2019) Moving towards body-to-body sensor networks for ubiquitous applications: a survey. J Sens Actuator Netw 8:27

    Article  Google Scholar 

  • Bonomi F, Milito R, Zhu J, Addepalli (2012) Fog computing and its role in the Internet of Things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, pp 13–16

  • Duan Y, Yisheng LV, Wang FY (2016) Travel time prediction with LSTM neural network. In: IEEE 19th International conference on intelligent transportation systems (ITSC), pp 1053–1058

  • Eck D, Schmidhuber J (2002) Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In: Neural networks for signal processing, 2002. In IEEE 12th IEEE workshop, pp 747–756

  • Giles CL, Kuhn GM, Williams RJ (1994) Dynamic recurrent neural networks: theory and applications. IEEE Trans Neural Netw 5:153–156

    Article  Google Scholar 

  • Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: Acoustics, speech and signal processing (ICASSP), IEEE international conference, pp 6645–6649

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  • Huang X, Shan H, Shen X (2011) On energy efficiency of cooperative communications in wireless body area network. In: Proceedings of IEEE wireless communications and networking conference, pp 1097–1101

  • Iqrar A, Karvonen H, Kumpuniemi T, Katz M (2019) Wireless communications for the hospital of the future: requirements, challenges and solutions. Int J Wirel Inf Netw. https://doi.org/10.1007/s10776-019-00468-1

    Article  Google Scholar 

  • Janakiram MSV (2018) Is fog computing the next big thing in Internet of Things? https://www.forbes.com/sites/janakirammsv/2016/04/18/is-fog-computing-the-next-big-thing-in-internet-of-things/#7180d166608d

  • Kumaresan P, Prabukumar Manoharan (2018) Design and implementation of energy efficient reconfigurable networks (WORN-DEAR) for BAN in IOT environment (BIOT). Int J Reason Based Intell Syst 10:258

    Google Scholar 

  • La QD, Ngo MV, Dinh TQ, Quek TQS, Shin H (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digit Commun Netw 5(1):3–9. https://doi.org/10.1016/j.dcan.2018.10.008

    Article  Google Scholar 

  • Qi Z, Xin Y-Y (2016) Study on WBAN-based efficient and energy saving access mechanisms. Int J Multimed Ubiquitous Eng 11:35–42

    Article  Google Scholar 

  • Samanta A, Misra S (2018) Dynamic connectivity establishment and cooperative scheduling for QoS-aware wireless body area networks. IEEE Trans Mob Comput 17:2755–2788

    Google Scholar 

  • Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681

    Article  Google Scholar 

  • Selem E, Fatehy M, Abd El-Kader SM, Nassar H (2019) THE (temperature heterogeneity energy) aware routing protocol for IoT health application. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2931868

    Article  Google Scholar 

  • Shahid MH, Hameed AR, ul Islam S, Khattak HA, Din IU, Rodrigues JJPC (2020) Energy and delay efficient fog computing using caching mechanism. Comput Commun 154:534–541. https://doi.org/10.1016/j.comcom.2020.03.001

    Article  Google Scholar 

  • Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  • Tauqir A, Javaid N, Akram S, Rao A, Mohammad SN (2013) Distance aware relaying energy-efficient: DARE to monitor patients in multi-hop body area sensor networks. In: Eighth international conference on broadband, wireless computing, communication and applications, IEEE

  • Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R (2020) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Future Gener Comput Syst 104:187–200

    Article  Google Scholar 

  • Uddin MZ, Hassan MM, Alsanad A, Savaglio C (2020) A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Inf Fusion 55:105–115. https://doi.org/10.1016/j.inffus.2019.08.004

    Article  Google Scholar 

  • Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156–3164

  • Vivekanandan K, Praveena N (2020) Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network. J Ambient Intell Humaniz Comput

  • Wu T, Wu F, Redoute JM, Yuce MR (2017) An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5:1413–11422

    Google Scholar 

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Authors and Affiliations

  1. School of Computing, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    S. Amudha & M. Murali

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  1. S. Amudha
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  2. M. Murali
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Correspondence to S. Amudha.

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Amudha S. declares that he has no conflict of interest. Murali M. declares that he has no conflict of interest.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/xxxDOIxxx

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Amudha, S., Murali, M. RETRACTED ARTICLE: Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital. J Ambient Intell Human Comput 12, 7441–7460 (2021). https://doi.org/10.1007/s12652-020-02421-0

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  • DOI: https://doi.org/10.1007/s12652-020-02421-0

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