Survey on Prediction Algorithms in Smart Homes

IEEE Internet of Things Journal

Volumn 4, Issue 3, 2017, Pages 636-644

  Wu, Shaoen ^a,b   Rendall, Jacob B ^b   Smith, Matthew J ^b   Zhu, Shangyu ^b   Xu, Junhong ^b   Wang, Honggang ^c   Yang, Qing ^d   Qin, Pinle ^e  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b BALL STATE UNIVERSITY   (United States)

^c University of Massachusetts Dartmouth   (United States)

^d Montana State University   (United States)

^e NORTH UNIVERSITY OF CHINA   (China)

Author keywords

Intelligent systems; Internet of Things; prediction methods; smart homes

Indexed keywords

AUTOMATION; INTELLIGENT BUILDINGS; INTELLIGENT SYSTEMS; INTERNET OF THINGS;

DAILY LIVES; PREDICTION ALGORITHMS; PREDICTION METHODS; SMART ENVIRONMENT; SMART HOMES;

FORECASTING;

EID: 85028393581     PISSN: None     EISSN: 23274662     Source Type: Journal    
DOI: 10.1109/JIOT.2017.2668061     Document Type: Article

References (62)

1. D. J. Cook et al., "MavHome: An agent-based smart home," in Proc. PerCom, vol. 3. Fort Worth, TX, USA, 2003, pp. 521-524.
2. M. R. Alam, M. B. I. Reaz, and M. A. M. Ali, "A review of smart homes-Past, present, and future," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 42, no. 6, pp. 1190-1203, Nov. 2012.
3. M. B. I. Reaz, "Artificial intelligence techniques for advanced smart home implementation," Acta Tech. Corviniensis Bull. Eng., vol. 6, no. 2, p. 51, 2013.
4. S. K. Das, D. J. Cook, A. Battacharya, E. O. Heierman, and T.-Y. Lin, "The role of prediction algorithms in the MavHome smart home architecture," IEEE Wireless Commun., vol. 9, no. 6, pp. 77-84, Dec. 2002.
5. E. M. Tapia, S. S. Intille, and K. Larson, "Activity recognition in the home using simple and ubiquitous sensors," in Proc. Int. Conf. Pervasive Comput., Vienna, Austria, 2004, pp. 158-175.
6. D. J. Cook and S. K. Das, "How smart are our environments? An updated look at the state of the art," Pervasive Mobile Comput., vol. 3, no. 2, pp. 53-73, 2007.
7. A. Roy, S. K. Das, and K. Basu, "A predictive framework for locationaware resource management in smart homes," IEEE Trans. Mobile Comput., vol. 6, no. 11, pp. 1270-1283, Nov. 2007.
8. S. K. Das and D. J. Cook, "Health monitoring in an agent-based smart home by activity prediction," in Proc. Int. Conf. Smart Homes Health Telematics, vol. 14. Singapore, 2004, pp. 3-14.
9. A. Lotfi, C. Langensiepen, S. M. Mahmoud, and M. J. Akhlaghinia, "Smart homes for the elderly dementia sufferers: Identification and prediction of abnormal behaviour," J. Ambient Intell. Humanized Comput., vol. 3, no. 3, pp. 205-218, 2012.
10. P. C. Roy et al., "A possibilistic approach for activity recognition in smart homes for cognitive assistance to Alzheimer's patients," in Proc. Activity Recognit. Pervasive Intell. Environ., 2011, pp. 33-58.
11. R. Li, B. Lu, and K. D. McDonald-Maier, "Cognitive assisted living ambient system: A survey," Digit. Commun. Netw., vol. 1, no. 4, pp. 229-252, 2015.
12. S. Wang and G. Zhou, "A review on radio based activity recognition," Digit. Commun. Netw., vol. 1, no. 1, pp. 20-29, 2015.
13. L. Yang, Y. Ren, and W. Zhang, "3D depth image analysis for indoor fall detection of elderly people," Digit. Commun. Netw., vol. 2, no. 1, pp. 24-34, 2016.
14. E. J. Rechy-Ramirez and H. Hu, "Bio-signal based control in assistive robots: A survey," Digit. Commun. Netw., vol. 1, no. 2, pp. 85-101, 2015.
15. R. Aipperspach, E. Cohen, and J. Canny, "Modeling human behavior from simple sensors in the home," in Proc. Int. Conf. Pervasive Comput., Dublin, Ireland, 2006, pp. 337-348.
16. S. Mahmoud, A. Lotfi, and C. Langensiepen, "Behavioural pattern identification and prediction in intelligent environments," Appl. Soft Comput., vol. 13, no. 4, pp. 1813-1822, 2013.
17. Z. Ghahramani, "An introduction to hidden Markov models and Bayesian networks," Int. J. Pattern Recognit. Artif. Intell., vol. 15, no. 1, pp. 9-42, 2001.
18. J. E. Cook and A. L. Wolf, "Discovering models of software processes from event-based data," ACM Trans. Softw. Eng. Methodol., vol. 7, no. 3, pp. 215-249, 1998.
19. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
20. H. Si, Y. Kawahara, H. Morikawa, and T. Aoyama, "A stochastic approach for creating context-aware services based on context histories in smart home," in Cognitive Sci. Res. Paper, Univ. Sussex, Sussex, U.K., 2005, pp. 37-42.
21. S. P. Rao and D. J. Cook, "Predicting inhabitant action using action and task models with application to smart homes," Int. J. Artif. Intell. Tools, vol. 13, no. 1, pp. 81-99, 2004.
22. E. Nazerfard and D. J. Cook, "Bayesian networks structure learning for activity prediction in smart homes," in Proc. 8th Int. Conf. Intell. Environ. (IE), Guanajuato, Mexico, 2012, pp. 50-56.
23. C. Debes et al., "Monitoring activities of daily living in smart homes: Understanding human behavior," IEEE Signal Process. Mag., vol. 33, no. 2, pp. 81-94, Mar. 2016.
24. L. Chen, C. D. Nugent, and H. Wang, "A knowledge-driven approach to activity recognition in smart homes," IEEE Trans. Knowl. Data Eng., vol. 24, no. 6, pp. 961-974, Jun. 2012.
25. M. Adipradhana, I. G. B. B. Nugraha, and S. H. Supangkat, "Intervention of non-inhabitant activities detection in smart home environment," in Proc. Int. Conf. ICT Smart Soc. (ICISS), Jakarta, Indonesia, 2013, pp. 1-5.
26. S. Zhang, S. McClean, B. Scotney, P. Chaurasia, and C. Nugent, "Using duration to learn activities of daily living in a smart home environment," in Proc. 4th Int. Conf. Pervasive Comput. Technol. Healthcare, Munich, Germany, 2010, pp. 1-8.
27. P. Zhuang and H. Liang, "Energy storage management in smart homes based on resident activity of daily life recognition," in Proc. IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), Miami, FL, USA, 2015, pp. 641-646.
28. J. Petzold, F. Bagci, W. Trumler, and T. Ungerer, "Next location prediction within a smart office building," in Cognitive Sci. Res. Paper, Univ. Sussex, Sussex, U.K., 2005, pp. 69-72.
29. M. Weiser, "The computer for the 21st century," Sci. Amer., vol. 265, no. 3, pp. 94-104, 1991.
30. A. Roy et al., "Location aware resource management in smart homes," in Proc. 1st IEEE Int. Conf. Pervasive Comput. Commun. (PerCom), Fort Worth, TX, USA, 2003, pp. 481-488.
31. A. Dixit and A. Naik, "Use of prediction algorithms in smart homes," Int. J. Mach. Learn. Comput., vol. 4, no. 2, pp. 157-162, 2014.
32. D. J. Cook, M. Huber, K. Gopalratnam, and M. Youngblood, "Learning to control a smart home environment," in Proc. Innov. Appl. Artif. Intell., Acapulco, Mexico, 2003.
33. J. Ziv and A. Lempel, "Compression of individual sequences via variable-rate coding," IEEE Trans. Inf. Theory, vol. IT-24, no. 5, pp. 530-536, Sep. 1978.
34. C. Zeeh. (2003). The Lempel Ziv Algorithm. Accessed on Nov. 3, 2003. [Online]. Available: http://w3studi.informatik.unistuttgart. de/-zeehca/Seminar/LempelZivReport.pdf
35. K. Gopalratnam and D. J. Cook, "Active LeZi: An incremental parsing algorithm for sequential prediction," Int. J. Artif. Intell. Tools, vol. 13, no. 4, pp. 917-929, 2004.
36. A. Bhattacharya and S. K. Das, "LeZi-update: An information-theoretic approach to track mobile users in PCS networks," in Proc. 5th Annu. ACM/IEEE Int. Conf. Mobile Comput. Netw., Seattle, WA, USA, 1999, pp. 1-12.
37. H. Fang and J. Ruan, "An improved position prediction algorithm based on active LeZi in smart home," in Proc. Int. Conf. Comput. Sci. Amp Service Syst. (CSSS), Nanjing, China, 2012, pp. 1733-1736.
38. A. Shukla, G. Ojha, S. Acharya, and S. Jain, "A customized flocking algorithm for swarms of sensors tracking a swarm of targets," arXiv preprint arXiv:1311.6981, 2013.
39. C. Moeslinger, T. Schmickl, and K. Crailsheim, "A minimalist flocking algorithm for swarm robots," in Proc. Eur. Conf. Artif. Life, Budapest, Hungary, 2009, pp. 375-382.
40. J. Lapalu, K. Bouchard, A. Bouzouane, B. Bouchard, and S. Giroux, "Unsupervised mining of activities for smart home prediction," Proc. Comput. Sci., vol. 19, pp. 503-510, Jun. 2013.
41. M. R. Alam, M. B. I. Reaz, and M. A. M. Ali, "SPEED: An inhabitant activity prediction algorithm for smart homes," IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 42, no. 4, pp. 985-990, Jul. 2012.
42. V. R. Jakkula and D. J. Cook, "Using temporal relations in smart environment data for activity prediction," in Proc. 24th Int. Conf. Mach. Learn., Corvallis, OR, USA, 2007, pp. 20-24.
43. J. Hu and M. P. Wellman, "Nash Q-learning for general-sum stochastic games," J. Mach. Learn. Res., vol. 4, pp. 1039-1069, Nov. 2003.
44. N. Roy, A. Roy, and S. K. Das, "Context-aware resource management in multi-inhabitant smart homes: A Nash H-learning based approach," in Proc. PerCom, Pisa, Italy, 2006, pp. 148-158.
45. D. Schweizer and H. Wache, "Learning frequent and periodic usage patterns in smart homes," Ph.D. dissertation, School Bus., Univ. Appl. Sci. Arts Northwestern Switzerland (FHNW), Muttenz, Switzerland, Feb. 2014.
46. Y. Zhang and T. Qi, "Research on mining association behavior of smart home users based on apriori algorithm," in Proc. Int. Conf. Mater. Eng. Inf. Technol. Appl., 2015, pp. 817-821.
47. P. Rashidi and D. J. Cook, "Keeping the resident in the loop: Adapting the smart home to the user," IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 39, no. 5, pp. 949-959, Sep. 2009.
48. A. Achar, S. Laxman, and P. S. Sastry, "A unified view of the aprioribased algorithms for frequent episode discovery," Knowl. Inf. Syst., vol. 31, no. 2, pp. 223-250, 2012.
49. E. O. Heierman and D. J. Cook, "Improving home automation by discovering regularly occurring device usage patterns," in Proc. 3rd IEEE Int. Conf. Data Min. (ICDM), Melbourne, FL, USA, 2003, pp. 537-540.
50. M. R. Alam, M. B. I. Reaz, M. A. M. Ali, and S. A. Samad, "Temporal modeling of human activity in smart homes," Informacije MIDEM, vol. 41, no. 2, pp. 118-121, 2011.
51. V. R. Jakkula and D. J. Cook, "Enhancing smart home algorithms using temporal relations," Technol. Aging, vol. 21, pp. 3-10, Jan. 2008.
52. V. Jakkula and D. J. Cook, "Learning temporal relations in smart home data," in Proc. 2nd Int. Conf. Technol. Aging Canada, vol. 33. 2007.
53. B. Gu et al., "Incremental learning for ν-support vector regression," Neural Netw., vol. 67, pp. 140-150, Jul. 2015.
54. B. Gu, X. Sun, and V. S. Sheng, "Structural minimax probability machine," IEEE Trans. Neural Netw. Learn. Syst., to be published.
55. B. Gu and V. S. Sheng, "A robust regularization path algorithm for ν- support vector classification," IEEE Trans. Neural Netw. Learn. Syst., to be published.
56. Z. Pan, Y. Zhang, and S. Kwong, "Efficient motion and disparity estimation optimization for low complexity multiview video coding," IEEE Trans. Broadcast., vol. 61, no. 2, pp. 166-176, Jun. 2015.
57. Z. Fu, X. Wu, C. Guan, X. Sun, and K. Ren, "Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement," IEEE Trans. Inf. Forens. Security, vol. 11, no. 12, pp. 2706-2716, Dec. 2016.
58. M. Marufuzzaman, M. B. I. Reaz, M. A. M. Ali, and L. F. Rahman, "A time series based sequence prediction algorithm to detect activities of daily living in smart home," Methods Inf. Med., vol. 54, no. 3, pp. 262-270, 2015.
59. M. T. Moutacalli, A. Bouzouane, and B. Bouchard, "Sensors activation time predictions in smart home," in Proc. 8th ACM Int. Conf. Pervasive Technol. Related to Assistive Environ., 2015, Art. no. 37.
60. S. Choi, E. Kim, and S. Oh, "Human behavior prediction for smart homes using deep learning," in Proc. IEEE RO-MAN, Gyeongju, South Korea, 2013, pp. 173-179.
61. S. T. M. Bourobou and Y. Yoo, "User activity recognition in smart homes using pattern clustering applied to temporal ANN algorithm," Sensors, vol. 15, no. 5, pp. 11953-11971, 2015.
62. J. Huang, Y. Meng, X. Gong, Y. Liu, and Q. Duan, "A novel deployment scheme for green Internet of Things," IEEE Internet Things J., vol. 1, no. 2, pp. 196-205, Apr. 2014.