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Green Mobile Cloud Computing forIndustry 5.0

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Green Mobile Cloud Computing

Abstract

Industry 5.0 delivers a vision of the industry that intends efficiency and productivity goals. It reinforces the contribution of industry 5.0 to sustainable smart cities and societies. The exponential increase in mobile users and their demands towards computing and communication resources have raised several challenges: mobility, limited battery, latency, energy consumption, etc. Mobile cloud computing (MCC) offers the facilities to store the data and execute applications inside the cloud by offloading. Nevertheless, the decision-making regarding offloading for energy-efficiency and low latency is significant and it needs novel research contributions. Furthermore, security, privacy, resource allocation, etc. are other significant issues that seek novel solutions. Fog, Edge cloud computing reduced delay and was used for real-time IoT application offloading. MCC is an engine of prosperity; production needs digital and green modulations.

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References

  1. De, D.: Mobile Cloud Computing: Architectures, Algorithms, and Applications. Chapman and Hall/CRC (2019)

    Google Scholar 

  2. Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R.: Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun. Surv. Tutor. 16(1), 369–392 (2013)

    Article  Google Scholar 

  3. Fernando, N., Loke, S.W., Rahayu, W.: Mobile cloud computing: a survey. Futur. Gener. Comput. Syst. 29(1), 84–106 (2013)

    Article  Google Scholar 

  4. Malik, S.U.R., Akram, H., Gill, S.S., Pervaiz, H., Malik, H.: EFFORT: energy efficient framework for offload communication in mobile cloud computing. Softw. Pract. Exp. 51(9), 1896–1909 (2021)

    Article  Google Scholar 

  5. Mukherjee, A., De, D., Roy, D.G.: A power and latency aware cloudlet selection strategy for multi-cloudlet environment. IEEE Trans. Cloud Comput. 7(1), 141–154 (2019)

    Article  Google Scholar 

  6. Mukherjee, A., Gupta, P., De, D.: Mobile cloud computing based energy efficient offloading strategies for femtocell network. In: 2014 Applications and Innovations in Mobile Computing (AIMoC), pp. 28–35. IEEE (2014)

    Chapter  Google Scholar 

  7. Mukherjee, A., Deb, P., De, D., Buyya, R.: C2OF2N: a low power cooperative code offloading method for femtolet-based fog network. J. Supercomput. 74(6), 2412–2448 (2018)

    Article  Google Scholar 

  8. Mukherjee, A., Deb, P., De, D., Obaidat, M.S.: WmA-MiFN: a weighted majority and auction game based green ultra-dense micro-femtocell network system. IEEE Syst. J. 14(1), 353–362 (2019)

    Article  Google Scholar 

  9. Deb, P., Mukherjee, A., De, D.: Design of green smart room using fifth generation network device Femtolet. Wirel. Pers. Commun. 104(3), 1037–1064 (2019)

    Article  Google Scholar 

  10. Barbarossa, S., Sardellitti, S., Di Lorenzo, P.: Joint allocation of computation and communication resources in multiuser mobile cloud computing. In: 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 26–30. IEEE (2013)

    Chapter  Google Scholar 

  11. Mukherjee, A., De, D.: Femtolet: a novel fifth generation network device for green mobile cloud computing. Simul. Model. Pract. Theory. 62, 68–87 (2016)

    Article  Google Scholar 

  12. Yu, S., Langar, R.: Collaborative computation offloading for multi-access edge computing. In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 689–694. IEEE (2019)

    Google Scholar 

  13. Mukherjee, A., De, D., Ghosh, S.K., Buyya, R.: Mobile Edge Computing. Springer (2021). https://doi.org/10.1007/978-3-030-69893-5. eBook ISBN: 978-3-030-69893-5, Hardcover ISBN: 978-3-030-69892-8

    Book  Google Scholar 

  14. Peng, K., Leung, V., Xiaolong, X., Zheng, L., Wang, J., Huang, Q.: A survey on mobile edge computing: focusing on service adoption and provision. Wirel. Commun. Mob. Comput. 2018 (2018)

    Google Scholar 

  15. Ghosh, S., Mukherjee, A., Ghosh, S.K., Buyya, R.: Mobi-iost: mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications. IEEE Trans. Netw. Sci. Eng. (2019)

    Google Scholar 

  16. Mukherjee, A., Ghosh, S., Behere, A., Ghosh, S.K., Buyya, R.: Internet of health things (IoHT) for personalized health care using integrated edge-fog-cloud network. J. Ambient. Intell. Humaniz. Comput., 1–17 (2020)

    Google Scholar 

  17. De, D., Mukherjee, A.: Femtocell based economic health monitoring scheme using mobile cloud computing. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 385–390. IEEE (2014)

    Chapter  Google Scholar 

  18. Mukherjee, A., De, D.: Femtocell based green health monitoring strategy. In: 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), pp. 1–4. IEEE (2014)

    Google Scholar 

  19. Banerjee, P.S., Karmakar, A., Dhara, M., Ganguly, K., Sarkar, S.: A novel method for predicting bradycardia and atrial fibrillation using fuzzy logic and arduino supported IoT sensors. Med. Novel Technol. Devices. 10, 100058 (2021)

    Article  Google Scholar 

  20. De, D., Mukherjee, A.: Femto-cloud based secure and economic distributed diagnosis and home health care system. J. Med. Imaging Health Inform. 5(3), 435–447 (2015)

    Article  Google Scholar 

  21. Mukherjee, A., De, D., Ghosh, S.K.: FogIoHT: a weighted majority game theory based energy-efficient delay-sensitive fog network for internet of health things. Internet of Things. 11, 100181 (2020)

    Article  Google Scholar 

  22. Butt, S.M.: Cloud centric real time mobile learning system for computer science. GRIN Verlag. (2014)

    Google Scholar 

  23. De, D., Mukherjee, A., Roy, D.G.: Power and delay efficient multilevel offloading strategies for mobile cloud computing. Wirel. Pers. Commun., 1–28 (2020)

    Google Scholar 

  24. Cuervo, E., Balasubramanian, A., Cho, D.-k., Wolman, A., Saroiu, S., Chandra, R., Bahl, P.: Maui: making smartphones last longer with code offload. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 49–62 (2010)

    Chapter  Google Scholar 

  25. Wang, S., Dey, S.: Rendering adaptation to address communication and computation constraints in cloud mobile gaming. In: 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1–6. IEEE (2010)

    Google Scholar 

  26. Constandache, I., Bao, X., Azizyan, M., Choudhury, R.R.: Did you see Bob? Human localization using mobile phones. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, pp. 149–160 (2010)

    Chapter  Google Scholar 

  27. Banerjee, N., Agarwal, S., Bahl, P., Chandra, R., Wolman, A., Corner, M.: Virtual compass: relative positioning to sense mobile social interactions. In: International Conference on Pervasive Computing, pp. 1–21. Springer, Berlin/Heidelberg (2010)

    Google Scholar 

  28. Sacramento, V., Endler, M., Rubinsztejn, H.K., Lima, L.S., Gonçalves, K., Nascimento, F.N., Bueno, G.A.: MoCA: a middleware for developing collaborative applications for mobile users. IEEE Distrib. Syst. Online. 5(10), 2–2 (2004)

    Article  Google Scholar 

  29. Huerta-Canepa, G., Lee, D.: A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond, pp. 1–5 (2010)

    Google Scholar 

  30. Kemp, R., Palmer, N., Kielmann, T., Bal, H.: Cuckoo: a computation offloading framework for smartphones. In: International Conference on Mobile Computing, Applications, and Services, pp. 59–79. Springer, Berlin/Heidelberg (2010)

    Google Scholar 

  31. Qi, H., Gani, A.: Research on mobile cloud computing: review, trend and perspectives. In: 2012 Second International Conference on Digital Information and Communication Technology and It’s Applications (DICTAP), pp. 195–202. IEEE (2012)

    Google Scholar 

  32. Huang, D., Zhang, X., Kang, M., Luo, J.: MobiCloud: building secure cloud framework for mobile computing and communication. In: 2010 Fifth IEEE International Symposium on Service Oriented System Engineering, pp. 27–34. IEEE (2010)

    Chapter  Google Scholar 

  33. Bhowmik, A., De, D.: mTrust: call behavioral trust predictive analytics using unsupervised learning in Mobile cloud computing. Wirel. Pers. Commun. 117(2), 483–501 (2021)

    Article  Google Scholar 

  34. Lu, Y., Zhao, D.: Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service. Comput. Commun. 182, 22–30 (2022)

    Article  Google Scholar 

  35. Razaque, A., Jararweh, Y., Alotaibi, B., Alotaibi, M., Hariri, S., Almiani, M.: Energy-efficient and secure mobile fog-based cloud for the Internet of Things. Futur. Gener. Comput. Syst. 127, 1–13 (2022)

    Article  Google Scholar 

  36. Hati, S., De, D., Mukherjee, A.: DewBCity: blockchain network-based dew-cloud modeling for distributed and decentralized smart cities. J. Supercomput., 1–21 (2022)

    Google Scholar 

  37. De, D.: FedLens: federated learning-based privacy-preserving mobile crowdsensing for virtual tourism. Innov. Syst. Softw. Eng., 1–14 (2022)

    Google Scholar 

  38. Roy, D.G., De, D., Mukherjee, A., Buyya, R.: Application-aware cloudlet selection for computation offloading in multi-cloudlet environment. J. Supercomput. 73(4), 1672–1690 (2017)

    Article  Google Scholar 

  39. Deb, P., Mukherjee, A., De, D.: A study of densification management using energy efficient femto-cloud based 5G mobile network. Wirel. Pers. Commun. 101(4), 2173–2191 (2018)

    Article  Google Scholar 

  40. Flinn, J., Satyanarayanan, M.: Powerscope: a tool for profiling the energy usage of mobile applications. In: Proceedings WMCSA’99. Second IEEE Workshop on Mobile Computing Systems and Applications, pp. 2–10. IEEE (1999)

    Chapter  Google Scholar 

  41. Banerjee, K.S., Agu, E.: PowerSpy: fine-grained software energy profiling for mobile devices. In: 2005 International Conference on Wireless Networks, Communications and Mobile Computing, vol. 2, pp. 1136–1141. IEEE (2005)

    Chapter  Google Scholar 

  42. Seo, C., Malek, S., Medvidovic, N.: An energy consumption framework for distributed java-based systems. In: Proceedings of the Twenty-Second IEEE/ACM International Conference on Automated Software Engineering, pp. 421–424 (2007)

    Chapter  Google Scholar 

  43. Zhao, Y., Leung, V.C.M., Zhu, C., Gao, H., Chen, Z., Ji, H.: Energy-efficient sub-carrier and power allocation in cloud-based cellular network with ambient RF energy harvesting. IEEE Access. 5, 1340–1352 (2017)

    Article  Google Scholar 

  44. Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34(12), 3590–3605 (2016)

    Article  Google Scholar 

  45. Liu, Z., Jingqi, F.: Resource pricing and offloading decisions in mobile edge computing based on the Stackelberg game. J. Supercomput., 1–20 (2022)

    Google Scholar 

  46. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  47. Mukherjee, A., Deb, P., De, D., Buyya, R.: IoT-F2N: an energy-efficient architectural model for IoT using Femtolet-based fog network. J. Supercomput. 75(11), 7125–7146 (2019)

    Article  Google Scholar 

  48. Karmakar, A., Ganguly, K., Banerjee, P.S.: HeartHealth: an intelligent model for multi-attribute based heart condition monitoring using fuzzy-TOPSIS method. In: 2021 Devices for Integrated Circuit (DevIC), pp. 1–5. IEEE (2021)

    Google Scholar 

  49. Gupta, A.K., Bhattacharya, I., Banerjee, P.S., Mandal, J.K., Mukherjee, A.: DirMove: direction of movement based routing in DTN architecture for post-disaster scenario. Wirel. Netw. 22(3), 723–740 (2016)

    Article  Google Scholar 

  50. Mukherjee, A., Bhattacharjee, S., Pal, S., De, D.: Femtocell based green power consumption methods for mobile network. Comput. Netw. 57(1), 162–178 (2013)

    Article  Google Scholar 

  51. Sayed, S.G., Said, S.A., Salem, S.A.: Energy aware mobile cloud computing using femtocells technology. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 90–95. IEEE (2021)

    Chapter  Google Scholar 

  52. Heller, B., Seetharaman, S., Mahadevan, P., Yiakoumis, Y., Sharma, P., Banerjee, S., McKeown, N.: Elastictree: saving energy in data center networks. Nsdi. 10, 249–264 (2010)

    Google Scholar 

  53. Marsan, M.A., Chiaraviglio, L., Ciullo, D., Meo, M.: Optimal energy savings in cellular access networks. In: 2009 IEEE International Conference on Communications Workshops, pp. 1–5. IEEE (2009)

    Google Scholar 

  54. Zhou, S., Gong, J., Yang, Z., Niu, Z., Yang, P.: Green mobile access network with dynamic base station energy saving. ACM MobiCom. 9(262), 10–12 (2009)

    Google Scholar 

  55. Vallina-Rodriguez, N., Hui, P., Crowcroft, J., Rice, A.: Exhausting battery statistics: understanding the energy demands on mobile handsets. In: Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds, pp. 9–14 (2010)

    Chapter  Google Scholar 

  56. Dogar, F.R., Steenkiste, P., Papagiannaki, K.: Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 107–122 (2010)

    Chapter  Google Scholar 

  57. Lu, X., ElzaErkip, Y.W., Goodman, D.: Power efficient multimedia communication over wireless channels. IEEE J. Sel. Areas Commun. 21(10), 1738–1751 (2003)

    Article  Google Scholar 

  58. Nandyala, C.S., Kim, H.-K.: Green IoT agriculture and healthcare application (GAHA). Int. J. Smart Home. 10(4), 289–300 (2016)

    Article  Google Scholar 

  59. Solanki, A., Nayyar, A.: Green internet of things (G-IoT): ICT technologies, principles, applications, projects, and challenges. In: Handbook of Research on Big Data and the IoT, pp. 379–405. IGI Global (2019)

    Chapter  Google Scholar 

  60. Arthi, B., Aruna, M., Ananda Kumar, S.: A study on energy-efficient and green IoT for healthcare applications. In: Green Computing and Predictive Analytics for Healthcare, pp. 95–114. Chapman and Hall/CRC (2020)

    Chapter  Google Scholar 

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Mukherjee, A., De, D., Buyya, R. (2022). Green Mobile Cloud Computing forIndustry 5.0. In: De, D., Mukherjee, A., Buyya, R. (eds) Green Mobile Cloud Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-08038-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-08038-8_1

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