Table of Contents Author Guidelines Submit a Manuscript
Wireless Communications and Mobile Computing
Volume 2018, Article ID 1347967, 15 pages
https://doi.org/10.1155/2018/1347967
Research Article

Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation

1Department of Software Engineering, Foundation University Islamabad, Pakistan
2School of Reliability and Systems Engineering, Beihang University, Beijing, China

Correspondence should be addressed to Shunkun Yang; nc.ude.aaub@ksy

Received 14 March 2018; Revised 18 July 2018; Accepted 29 August 2018; Published 16 September 2018

Academic Editor: Mubashir H. Rehmani

Copyright © 2018 Umar Mahmud et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. A. Malik, U. Mahmud, and M. Y. Javed, “Future challenges in context aware computing,” in WWW/Internet 2007, pp. 306–310, 2007. View at Google Scholar
  2. T. Strang and C. Linnhoff-Popien, “A context modeling survey,” in Proceedings of the Workshop on Advanced Context Modelling, Reasoning and Management (UbiComp '04), pp. 34–41, 2004.
  3. P. Daponte, L. De Vito, F. Picariello, and M. Riccio, “State of the art and future developments of measurement applications on smartphones,” Measurement, vol. 46, no. 9, pp. 3291–3307, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Alaa, A. A. Zaidan, B. B. Zaidan, M. Talal, and M. L. M. Kiah, “A review of smart home applications based on Internet of Things,” Journal of Network and Computer Applications, vol. 97, pp. 48–65, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Cioara, I. Anghel, I. Salomie, G. Copil, B. Pernici, and D. Moldovan, “A context aware self-adapting algorithm for managing the energy efficiency of IT service centers,” Ubiquitous Computing and Communication Journal, vol. 6, pp. 619–630, 2011. View at Google Scholar
  6. S. L. Kiani, A. Anjum, N. Antonopoulos, and M. Knappmeyer, “Context-aware service utilisation in the clouds and energy conservation,” Journal of Ambient Intelligence and Humanized Computing, vol. 5, no. 1, pp. 111–131, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. O. Yuryur, Energy Efficient Context-Aware Framework in Mobile Sensing [Ph.D. Thesis], University of South Florida, Florida, FL, USA, 2013.
  8. K. Naik, A Survey of Software Based Energy Saving Methodologies for Handheld Wireless Communication Devices, Department of Electrical and Computer Engineering, University of Waterloo, 2010.
  9. K. Ravindranath and K. R. S. Rao, “A survey on energy aware offloading techniques for mobile cloud computing,” International Journal of Computer Trends and Technology, vol. 4, pp. 2081–2086, 2013. View at Google Scholar
  10. C. Arun and V. Jaiganesh, “Survey on minimizing energy consumption in mobile cloud computing,” International Journal of Computer Applications, vol. 150, no. 3, pp. 5–8, 2016. View at Publisher · View at Google Scholar
  11. Z. Li, S. Tesfatsion, S. Bastani et al., “A survey on modeling energy consumption of cloud applications: deconstruction, state of the art, and trade-off debates,” IEEE Transactions on Sustainable Computing, vol. 2, no. 3, pp. 255–274, 2017. View at Publisher · View at Google Scholar
  12. S. Mittal, “A survey of techniques for improving energy efficiency in embedded computing systems,” International Journal of Computer Aided Engineering and Technology, vol. 6, no. 4, pp. 440–459, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. M. H. Jofri, M. F. M. Fudzee, and M. N. Ismail, “A survey on energy-aware profiler for mobile devices,” in Advances in Intelligent Systems and Computing, vol. 331, pp. 295–305, Springer, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. J. F. M. Bernai, L. Ardito, M. Morisio, and P. Falcarin, “Towards an efficient context-aware system: Problems and suggestions to reduce energy consumption in mobile devices,” in Proceedings of the 9th International Conference on Mobile Business/9th Global Mobility Roundtable (ICMB and GMR '10), pp. 510–514, June 2010. View at Scopus
  15. T. Rault, A. Bouabdallah, Y. Challal, and F. Marin, “A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications,” Pervasive and Mobile Computing, vol. 37, pp. 23–44, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. M. N. Ismail, R. Ibrahim, and M. F. Md Fudzee, “A survey on content adaptation systems towards energy consumption awareness,” Advances in Multimedia, vol. 2013, Article ID 871516, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Wang, L. Feng, W. Xue, and Z. Song, “A survey on energy-efficient data management,” ACM SIGMOD Record, vol. 40, no. 2, pp. 17–23, 2011. View at Publisher · View at Google Scholar
  18. S. Brienza, S. E. Cebeci, S. S. Masoumzadeh, H. Hlavacs, Ö. Özkasap, and G. Anastasi, “A survey on energy efficiency in P2P systems: File distribution, content streaming, and epidemics,” ACM Computing Surveys, vol. 48, no. 3, 2015. View at Google Scholar · View at Scopus
  19. R. Bolla, R. Bruschi, F. Davoli, and F. Cucchietti, “Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures,” IEEE Communications Surveys & Tutorials, vol. 13, no. 2, pp. 223–244, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. M. R. Celenliôglu, D. Gözüpek, and H. A. Mantar, “A survey on the energy efficiency of vertical handover mechanisms,” in Proceedings of the International Conference on Wireless and Mobile Networks (WiMoN '13), 2013.
  21. U. Mahmud, N. Iltaf, A. Rehman, and F. Kamran, “Context-Aware Paradigm for a Pervasive Computing Environment (CAPP),” in WWW/Internet 2007, pp. 337–346, Villa Real, Portugal, 2007. View at Google Scholar
  22. M. Knappmeyer, S. L. Kiani, E. S. Reetz, N. Baker, and R. Tonjes, “Survey of context provisioning middleware,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1492–1519, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Hashemi and A. Sadeghi-Niaraki, “A theoretical framework for ubiquitous computing,” International Journal of Advanced Pervasive and Ubiquitous Computing, vol. 8, no. 2, pp. 1–15, 2016. View at Publisher · View at Google Scholar
  24. U. Alegre, J. C. Augusto, and T. Clark, “Engineering context-aware systems and applications: A survey,” The Journal of Systems and Software, vol. 117, pp. 55–83, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. U. Mahmud and N. A. Malik, “Flow and threat modelling of a context aware system,” International Journal of Advanced Pervasive and Ubiquitous Computing, vol. 6, no. 2, pp. 58–70, 2014. View at Publisher · View at Google Scholar
  26. U. Mahmud, “UML based model of a context aware system,” International Journal of Advanced Pervasive and Ubiquitous Computing, vol. 7, no. 1, pp. 1–16, 2015. View at Publisher · View at Google Scholar
  27. U. Mahmud, “Organizing contextual data in context aware systems: A review,” in Handbook of Research on Human-Computer Interfaces, Developments, and Applications, P. A. Hershey, Ed., pp. 273–303, IGI Global, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. G. W. Musumba and H. O. Nyongesa, “Context awareness in mobile computing: A review,” International Journal of Machine Learning and Applications, vol. 2, no. 1, 2013. View at Publisher · View at Google Scholar
  29. J.-Y. Hong, E.-H. Suh, and S.-J. Kim, “Context-aware systems: a literature review and classification,” Expert Systems with Applications, vol. 36, no. 4, pp. 8509–8522, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. S. L. Kiani, A. Anjum, M. Knappmeyer, N. Bessis, and N. Antonopoulos, “Federated broker system for pervasive context provisioning,” The Journal of Systems and Software, vol. 86, no. 4, pp. 1107–1123, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. U. Mahmud, U. Farooq, M. Y. Javed, and N. A. Malik, “Representing and organizing contextual data in context aware environments,” Journal of Computing, vol. 4, pp. 61–67, March 2012. View at Google Scholar
  32. J. Grudin, “Desituating action: Digital representation of context,” Human-Computer Interaction, vol. 16, no. 2-4, pp. 269–286, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Feng, P. M. G. Apers, and W. Jonker, “Towards context-aware data management for ambient intelligence,” Lecture Notes in Computer Science, vol. 3180, pp. 422–431, 2004. View at Google Scholar · View at Scopus
  34. U. Mahmud, N. Iltaf, and F. Kamran, “Context congregator: gathering contextual information,” in Proceedings of the 5th Frontiers of Information Technology (FIT '07), pp. 134–141, Islamabad, Pakistan, 2007.
  35. N. A. Malik, M. Y. Javed, and U. Mahmud, “Estimating user preferences by managing contextual history in context aware systems,” Journal of Software , vol. 4, no. 6, pp. 571–576, 2009. View at Google Scholar · View at Scopus
  36. U. Mahmud and M. Y. Javed, “Context inference engine (CiE): inferring context,” International Journal of Advanced Pervasive and Ubiquitous Computing, vol. 4, no. 3, pp. 13–41, 2012. View at Publisher · View at Google Scholar
  37. U. Mahmud and M. Y. Javed, “Context Inference Engine (CiE): Classifying activity of context using minkowski distance and standard deviation-based ranks,” in Systems and Software Development, Modeling, and Analysis: New Perspectives and Methodologies, pp. 65–112, IGI Global, 2014. View at Google Scholar · View at Scopus
  38. S. Hussain, Z. Wang, and I. K. Toure, “An approach for QoS measurement and web service selection sureness,” High Technology Letters, vol. 19, no. 3, pp. 283–289, 2013. View at Google Scholar · View at Scopus
  39. S. Hussain, Z. S. Wang, and I. K. Toure, “Performance analysis of web services in different types of internet technologies,” Applied Mechanics and Materials, vol. 513-517, pp. 1431–1436, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. O. Landsiedel, K. Wehrle, and S. Götz, “Accurate prediction of power consumption in sensor networks,” in Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II '05), pp. 37–44, May 2005. View at Publisher · View at Google Scholar · View at Scopus
  41. J. Flinn, Extending Mobile Computer Battery Life Through Energy-Aware Adaptation [Ph.D. Thesis], Carnegie Melon University, Pittsburgh, PA, USA, 2001.
  42. A. Kansal, F. Zhao, J. Liu, N. Kothari, and A. A. Bhattacharya, “Virtual machine power metering and provisioning,” in Proceedings of the 1st ACM Symposium on Cloud Computing (SoCC '10), pp. 39–50, Indianapolis, IN, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. A. Pathak, Y. C. Hu, and M. Zhang, “Where is the energy spent inside my app?: Fine grained energy accounting on smartphones with eprof,” in Proceedings of the 7th ACM European Conference on ComputerSystems (EuroSys '12), pp. 29–42, Bern, Switzerland, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Kulk and J. Welsh, “A low power walk for the NAO robot,” in Proceedings of the Australasian Conference on Robotics and Automation (ACRA '08), pp. 1–7, December 2008. View at Scopus
  45. C. Seo, G. Edwards, S. Malek, and N. Medvidovic, “A framework for estimating the impact of a distributed software system's architectural style on its energy consumption,” in Proceedings of the 7th IEEE/IFIP Working Conference on Software Architecture (WICSA '08), pp. 277–280, February 2008. View at Scopus
  46. N. Vallina-Rodriguez, J. Shah, A. Finamore et al., “Breaking for commercials: Characterizing mobile advertising,” in Proceedings of the ACM Internet Measurement Conference (IMC '12), pp. 343–356, November 2012. View at Scopus
  47. Y. Li, H. Chen, and W. Shi, “Power behavior analysis of mobile applications using Bugu,” Sustainable Computing: Informatics and Systems, vol. 4, no. 3, pp. 183–195, 2014. View at Publisher · View at Google Scholar · View at Scopus
  48. A. B. Abkenar, S. W. Loke, W. Rahayu, and A. Zaslavsky, “Energy considerations for continuous group activity recognition using mobile devices: The case of GroupSense,” in Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications (AINA '16), pp. 479–486, Crans-Montana, Switzerland, March 2016. View at Scopus
  49. M. Marcu, “Powerthermal profiling of software applications,” Microelectronics Journal, vol. 42, no. 4, pp. 601–608, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. L.-T. Duan, B. Guo, Y. Shen, Y. Wang, and W.-L. Zhang, “Energy analysis and prediction for applications on smartphones,” Journal of Systems Architecture, vol. 59, no. 10, pp. 1375–1382, 2013. View at Publisher · View at Google Scholar · View at Scopus
  51. Y.-F. Chung, C.-Y. Lin, and C.-T. King, “ANEPROF: Energy profiling for android java virtual machine and applications,” in Proceedings of the 17th IEEE International Conference on Parallel and Distributed Systems (ICPADS '11), pp. 372–379, Tainan, Taiwan, December 2011. View at Scopus
  52. M. Tanabian, “Mobile device/application power profiling—testing and design considerations in mobile devices & apps for power performance and battery life,” Intuigence Group, White Paper, 2011. View at Google Scholar
  53. Y.-D. Lin, E. Rattagan, Y.-C. Lai et al., “Calibrating parameters and formulas for process-level energy consumption profiling in smartphones,” Journal of Network and Computer Applications, vol. 44, pp. 106–119, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. M. Lee, D.-K. Kim, and J.-W. Lee, “Analysis of characteristics of power consumption for context-aware mobile applications,” Information, vol. 5, no. 4, pp. 612–621, 2014. View at Google Scholar · View at Scopus
  55. T. Mitchell, Machine Learning, McGraw Hill, 1997.
  56. S. Elmalaki, M. Gottscho, P. Gupta, and M. Srivastava, “A case for battery charging-aware power management and deferrable task scheduling in smartphones,” in Proceedings of the 6th USENIX Conference on Power-Aware Computing and Systems (HotPower '14), pp. 1–4, Broomfield, CO, USA, 2014.
  57. G. Anastasi, S. Brienza, G. L. Re, and M. Ortolani, “Energy-efficient protocol design,” in Green Communications: Principles, Concepts and Practice, K. Samdanis, P. Rost, A. Maeder, M. Meo, and C. Verikoukis, Eds., pp. 339–360, John Wiley & Sons, Ltd, Chichester, UK, 2015. View at Google Scholar
  58. J. Flinn and M. Satyanarayanan, “Energy-aware adaptation for mobile applications,” in Proceedings of the Seventeenth ACM Symposium on Operating Systems Principles, pp. 48–63, Charleston, SC, USA, December 1999. View at Publisher · View at Google Scholar
  59. S. Elmalaki, L. Wanner, and M. Srivastava, “CAreDroid: Adaptation framework for android context-aware applications,” in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom '15), pp. 386–399, Paris, France, September 2015. View at Scopus
  60. M. Moghimi, J. Venkatesh, P. Zappi, and T. Rosing, “Context-aware mobile power management using fuzzy inference as a service,” in Proceedings of the International Conference on Mobile Computing, Applications, and Services (MobiCASE '12), vol. 110, pp. 314–327, Seattle, WA, USA, 2012. View at Publisher · View at Google Scholar
  61. Y. Fei, Z. Lin, and N. K. Jha, “An energy-aware framework for coordinated dynamic software management in mobile computers,” in Proceedings of the IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS '04), pp. 306–317, Volendam, Netherlands, October 2004. View at Scopus
  62. J. Chen and G. Venkataramani, “EnDebug: A hardware-software framework for automated energy debugging,” Journal of Parallel and Distributed Computing, vol. 96, pp. 121–133, 2016. View at Publisher · View at Google Scholar · View at Scopus
  63. P. Simoens, C. Mahieu, F. Ongenae et al., “Internet of robotic things: context-aware and personalized interventions of assistive social robots,” in Proceedings of the 5th IEEE International Conference on Cloud Networking (CloudNet '16), pp. 204–207, Pisa, Italy, October 2016. View at Scopus
  64. Q. Zhang, G. Metri, S. Raghavan, and W. Shi, “RESCUE: An energy-aware scheduler for cloud environments,” Sustainable Computing, vol. 4, no. 4, pp. 215–224, 2014. View at Publisher · View at Google Scholar · View at Scopus
  65. N. Akhter and M. Othman, “Energy aware resource allocation of cloud data center: review and open issues,” Cluster Computing, vol. 19, no. 3, pp. 1163–1182, 2016. View at Publisher · View at Google Scholar · View at Scopus
  66. S. Kang, J. Lee, H. Jang, Y. Lee, S. Park, and J. Song, “A scalable and energy-efficient context monitoring framework for mobile personal sensor networks,” IEEE Transactions on Mobile Computing, vol. 9, no. 5, pp. 686–702, 2010. View at Publisher · View at Google Scholar · View at Scopus
  67. D. Sathan, A. Meetoo, and R. K. Subramaniam, “Context aware lightweight energy efficient framework,” World Academy of Science Engineering and Technology, vol. 52, pp. 64–70, 2009. View at Google Scholar
  68. R. Hermann, P. Zappi, and T. S. Rosing, “Context aware power management of mobile systems for sensing applications,” in Information Processing in Sensor Networks, pp. 1–5, 2012. View at Google Scholar
  69. T. Simunic, L. Benini, and G. De Micheli, “Energy-efficient design of battery-powered embedded systems,” in Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED '99), pp. 212–217, San Diego, Calif, USA, August 1999. View at Publisher · View at Google Scholar
  70. T. Ŝimunić, L. Benini, and G. De Micheli, “Energy-efficient design of battery-powered embedded systems,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 9, no. 1, pp. 15–28, 2001. View at Publisher · View at Google Scholar · View at Scopus
  71. V. Tiwari, S. Malik, A. Wolfe, and M. T.-C. Lee, “Instruction level power analysis and optimization of software,” in Technologies for Wireless Computing, pp. 139–154, Springer, 1996. View at Google Scholar
  72. V. Tiwari, S. Malik, and A. Wolfe, “Power analysis of embedded software: a first step towards software power minimization,” in Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, pp. 437–445, San Jose, Calif, USA, 1994. View at Publisher · View at Google Scholar
  73. H. Mehta, R. M. Owens, M. J. Irwin, R. Chen, and D. Ghosh, “Techniques for low energy software,” in Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED '97), pp. 72–75, Monterey, Calif, USA, August 1997. View at Publisher · View at Google Scholar
  74. E. Capra, C. Francalanci, and S. A. Slaughter, “Is software 'green'? Application development environments and energy efficiency in open source applications,” Information and Software Technology, vol. 54, no. 1, pp. 60–71, 2012. View at Publisher · View at Google Scholar · View at Scopus
  75. S. Hao, D. Li, W. G. J. Halfond, and R. Govindan, “Estimating mobile application energy consumption using program analysis,” in Proceedings of the 35th International Conference on Software Engineering (ICSE '13), pp. 92–101, San Francisco, Calif, USA, May 2013. View at Scopus
  76. S.-L. Tsao, C.-K. Yu, and Y.-H. Chang, “Profiling energy consumption of I/O functions in embedded applications,” in Proceedings of the International Conference on Architecture of Computing Systems (ARCS '13), vol. 7767, pp. 195–206, Prague, Czech, 2013. View at Publisher · View at Google Scholar