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Personal and Ubiquitous Computing

, Volume 22, Issue 1, pp 121–134 | Cite as

Hybrid computation offloading for smart home automation in mobile cloud computing

  • Jie Zhang
  • Zhili Zhou
  • Shu Li
  • Leilei Gan
  • Xuyun Zhang
  • Lianyong Qi
  • Xiaolong Xu
  • Wanchun DouEmail author
Original Article

Abstract

Smart home automation enables the users to realize the access control of the in-home appliances by the mobile devices. With the rapid development of mobile cloud computing, offloading computation workloads of the home automation applications to nearby cloudlets has been treated as a promising approach to overcoming inherent flaws of portable devices, such as low battery capacity. The computing capacity of cloudlet is limited compared with the distant public cloud whose elastic computation resources are almost infinite. Therefore, some mobile services should wait for the occupied computation resources in the cloudlet to get released, which is less energy-efficient. In view of this challenge, we model the waiting time spending in the cloudlet as a M/M/m/ queue and propose a hybrid computation offloading algorithm for home automation applications to minimize the total energy consumption of the mobile devices within a given constant deadline. The proposed algorithm combines cloudlet with public clouds, providing a more energy-efficient offloading strategy for home automation applications. Technically, a particle swarm optimization (PSO)-based heuristic algorithm is implemented to schedule mobile services. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of our proposed algorithm.

Keywords

Home automation Mobile cloud computing Cloudlet Mobile service Energy consumption 

Notes

Funding information

This research is supported by the National Key R&D Program of China (no. 2017YFB1001800); the National Science Foundation of China under grant no. 61672276, no. 61702277, no. 61402258, and no. 61602253; and the Key Research and Development Project of Jiangsu Province under grant nos. BE2015154 and BE2016120. Besides, this work is also supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, and the open project from State Key Laboratory for Novel Software Technology, Nanjing University under grant no. KFKT2017B04.

References

  1. 1.
    Singh N, Bharti SS, Singh R, Singh DK (2014) In: 2014 International conference on advances in engineering and technology research (ICAETR), pp 1–5Google Scholar
  2. 2.
    Wang J-C, Lin C-H, Siahaan E, Chen B-W, Chuang H-L (2014) Mixed sound event verification on wireless sensor network for home automation. IEEE Trans Indus Inf 10:803–812CrossRefGoogle Scholar
  3. 3.
    Dickey N, Banks D, Sukittanon S (2012) Home automation using Cloud Network and mobile devices, Southeastcon. Proc IEEE 1–4Google Scholar
  4. 4.
    Baraka K, Ghobril M, Malek S, Kanj R, Kayssi A (2013) Low cost arduino/android-based energy-efficient home automation system with smart task scheduling. Computational intelligence. In: 2013 Fifth International conference on communication systems and networks (CICSyN), pp 296–301Google Scholar
  5. 5.
    Korkmaz I, Metin SK, Gurek A, Gur C, Gurakin C, Akdeniz M (2015) A cloud based and Android supported scalable home automation system. Comput Electr Eng 43:112–128CrossRefGoogle Scholar
  6. 6.
    Grzonkowski S, Corcoran PM (2011) Sharing cloud services: user authentication for social enhancement of home networking. IEEE Trans Consum Electron 57:1424–1432CrossRefGoogle Scholar
  7. 7.
    Kehoe B, Patil S, Abbeel P, Goldberg K (2015) A survey of research on cloud robotics and automation. IEEE Trans Autom Sci Eng 12:398–409CrossRefGoogle Scholar
  8. 8.
    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wireless Commun Mob Comput 13:1587–1611CrossRefGoogle Scholar
  9. 9.
    Ahmed E, Gani A, Sookhak M, Hamid A, Hafizah S, Xia F (2015) Application optimization in mobile cloud computing: motivation, taxonomies, and open challenges. J Netw Comput Appl 52:52–68CrossRefGoogle Scholar
  10. 10.
    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Fut Gen Comput Syst 29:84–106CrossRefGoogle Scholar
  11. 11.
    Barbera MV, Kosta S, Mei A, Stefa J (2013) To offload or not to offload? The bandwidth and energy costs of mobile cloud computing. IEEE INFOCOM, 1285–1293Google Scholar
  12. 12.
    Kumar Kk, Lu Y-H (2010) Cloud computing for mobile users: can offloading computation save energy?. Computer 43:51–56CrossRefGoogle Scholar
  13. 13.
    Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervas Comput 8:14–23CrossRefGoogle Scholar
  14. 14.
    Chun B-G, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the sixth conference on computer systems, pp 301–314Google Scholar
  15. 15.
    Xiang H, Xu X, Zheng H, Li S, Wu T, Dou W, Yu S (2016) An adaptive cloudlet placement method for mobile applications over GPS big data. IEEE Global Commun Conf (GLOBECOM), 1–6Google Scholar
  16. 16.
    Clinch S, Harkes J, Friday A, Davies N, Satyanarayanan M (2012) How close is close enough? Understanding the role of cloudlets in supporting display appropriation by mobile users. In: IEEE International conference on pervasive computing and communications (PerCom), pp 122–127Google Scholar
  17. 17.
    Xu X, Dou W, Zhang X, Chen J (2016) EnReal: an energy-aware resource allocation method for scientific workflow executions in cloud environment, vol 4Google Scholar
  18. 18.
    Bolch G, Greiner S, de Meer H, Trivedi KS (2006) Queueing networks and Markov chains: modeling and performance evaluation with computer science applicationsGoogle Scholar
  19. 19.
    Parsopoulos KE, Vrahatis MN et al (2002) Particle swarm optimization method for constrained optimization problems, vol 76Google Scholar
  20. 20.
    Deb K, Pratap A, Agarwal S, Meyarivan (2002) TAMT, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  21. 21.
    Verma A, Kaushal S (2014) Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud. Int J Grid Utilit Comput 5:96–106CrossRefGoogle Scholar
  22. 22.
    Gary MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. WH Freemann, New York, p 70Google Scholar
  23. 23.
    Ullman JD (1975) NP-complete scheduling problems. J Comput Syst Sci 10:384–393MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Kennedy J (2011) Particle swarm optimization. Encycloped Mach Learn 760–766Google Scholar
  25. 25.
    Yu J, Buyya R (2006) A budget constrained scheduling of workflow applications on utility grids using genetic algorithms. In: IEEE Workshop on workflows in support of large-scale science, pp 1–10Google Scholar
  26. 26.
    Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14:217–230Google Scholar
  27. 27.
    Izakian H, Ladani BT, Abraham A, Snasel V et al (2010) A discrete particle swarm optimization approach for grid job scheduling. Int J Innov Comput Inf Control 6:1–15Google Scholar
  28. 28.
    Kennedy J, Eberhart R C (1997) A discrete binary version of the particle swarm algorithm. IEEE Int Conf Syst Man Cybern Comput Cybern Simul 5:4104–4108Google Scholar
  29. 29.
    Zhang W, Wen Y (2015) Energy-efficient task execution for application as a general topology in mobile cloud. Comput IEEE Trans Cloud ComputGoogle Scholar
  30. 30.
    Gamba M, Gonella A, Palazzi CE (2015) Design issues and solutions in a modern home automation system. In: IEEE International conference on computing on networking and communications (ICNC), pp 1111–1115Google Scholar
  31. 31.
    Meng Z, Lu J (2016) A rule-based service customization strategy for smart home context-aware automation. IEEE Trans Mob Comput 15:558–571CrossRefGoogle Scholar
  32. 32.
    Jacobsson A, Boldt M, Carlsson B (2016) A risk analysis of a smart home automation system. Futur Gener Comput Syst 56:719–733CrossRefGoogle Scholar
  33. 33.
    Wang J-C, Lee Y-S, Lin C-H, Siahaan E, Yang C-H (2015) Robust environmental sound recognition with fast noise suppression for home automation. IEEE Trans Autom Sci Eng 12:1235– 1242CrossRefGoogle Scholar
  34. 34.
    Gill K, Yang S-H, Yao F, Lu X (2009) A Zigbee-based home automation system. IEEE Trans Consum Electron 55:422–430CrossRefGoogle Scholar
  35. 35.
    Olteanu A-C, Oprina G-D, Tapus N, Zeisberg S (2013) Enabling mobile devices for home automation using ZigBee. In: IEEE 19th International conference on control systems and computer science (CSCS), pp 189–195Google Scholar
  36. 36.
    Mandula K, Parupalli R, Murty CHAS, Magesh E, Lunagariya R (2015) Mobile based home automation using internet of things (IoT). In: International conference on control, instrumentation, communication and computational technologies (ICCICCT), pp 340–343Google Scholar
  37. 37.
    Wang J, Cao J, Li B, Lee S, Sherratt RS (2015) Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans Consum Electron 61:438–444CrossRefGoogle Scholar
  38. 38.
    Cuervo E, Balasubramanian A, Cho D-K, Wolman A, Saroiu S, Chandra R, Bahl P (2010) MAUI: making smartphones last longer with code offload. In: Proceedings of the 8th international conference on mobile systems, applications, and services, pp 49–62Google Scholar
  39. 39.
    Ra M-R, Sheth A, Mummert L, Pillai P, Wetherall D, Govindan R (2011) Odessa: enabling interactive perception applications on mobile devices. In: Proceedings of the 9th international conference on Mobile systems, applications, and services, pp 43–56Google Scholar
  40. 40.
    Li Z, Wang C, Xu R (2001) Computation offloading to save energy on handheld devices: a partition scheme. In: Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for embedded systems, pp 238–246Google Scholar
  41. 41.
    Masdari M, Salehi F, Jalali M, Bidaki M (2017) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manag 25:122–158CrossRefGoogle Scholar
  42. 42.
    Xue S, Shi W, Xu X (2016) A Heuristic scheduling algorithm based on PSO in the cloud computing environment. International Journal of u-and e-Service. Sci Technol 9(1):349–362Google Scholar
  43. 43.
    Mao C, Lin R, Xu C, He Q (2017) Towards a trust prediction framework for cloud services based on PSO-driven neural network. IEEE Access 5:2187–2199CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Jiangsu Engineering Centre of Network Monitoring, School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Department of Electrical and Computer EngineeringThe University of AucklandAucklandNew Zealand
  4. 4.School of Information Science and Engineering, Chinese Academy of Education Big DataQufu Normal UniversityRizhaoChina

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