Context reasoning using extended evidence theory in pervasive computing environments

Future Generation Computer Systems

Volumn 26, Issue 2, 2010, Pages 207-216

  Zhang, Daqiang ^a   Guo, Minyi ^a   Zhou, Jingyu ^a   Kang, Dazhou ^b   Cao, Jiannong ^c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Context reasoning; Evidence theory; Pervasive computing

Indexed keywords

CONFLICT RESOLUTION; CONTEXT REASONING; EMPIRICAL STUDIES; EVIDENCE THEORIES; EVIDENCE THEORY; FUNDAMENTAL PROBLEM; PERVASIVE APPLICATIONS; PERVASIVE COMPUTING; PERVASIVE COMPUTING ENVIRONMENT; RESOURCE CONSTRAINT; USER MOBILITY;

UBIQUITOUS COMPUTING;

COMPUTATION THEORY;

EID: 70349764321     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2009.08.005     Document Type: Article

References (42)

1. Weiser M. The computer for the twenty-first century. Scientific American 265 3 (1991) 94-104
2. Satyanarayanan M. Pervasive computing: vision and challenges, Personal Communications. IEEE 8 4 (2001) 10-17 (See also IEEE Wireless Communications)
3. C. Xu, S.C. Cheung, Inconsistency detection and resolution for context-aware middleware support, in: ESEC/FSE-13: Proceedings of the 10th European Software Engineering Conference, 2005, pp. 336-345
4. X. Chang, S.C. Cheung, W.K. Chan, Y. Chunyang, Heuristics-based strategies for resolving context inconsistencies in pervasive computing applications, in: ICDCS'08, 2008, pp. 713-721
5. S. Jeffery, M. Garofalakis, M. Franklin, Adaptive cleaning for RFID data streams, in: Proceedings of the 32nd International Conference on Very large data bases, 2006, pp. 163-174
6. E. Elnahrawy, B. Nath, Cleaning and querying noisy sensors, in: Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, 2003, pp. 78-87
7. Liu K., Chen L., Liu Y., and Li M. Robust and efficient aggregate query processing in wireless sensor networks. Mobile Networks and Applications 13 1 (2008) 212-227
8. Ranganathan A., Al-Muhtadi J., and Campbell R.H. Reasoning about uncertain contexts in pervasive computing environments. IEEE Pervasive Computing 3 2 (2004) 62-70
9. M. Mamei, R. Nagpal, Macro programming through bayesian networks: Distributed inference and anomaly detection, in: Proceedings of the 5th Annual IEEE International Conference on Pervasive Computing and Communications, 2007, 2007, pp. 87-96
10. Kofod Petersen A., and Mikalsen M. Context: Representation and reasoning: Representing and reasoning about context in a mobile environment. Revue d'intelligence artificielle 19 3 (2005) 479-498
11. Ranganathan A., and Campbell R.H. An infrastructure for context-awareness based on first order logic. Personal Ubiquitous Comput. 7 6 (2003) 353-364
12. Shen J., and Yang Y. Extending RDF in distributed knowledge-intensive applications. Future Generation Computer Systems 20 1 (2004) 27-46
13. X. Wang, D. Zhang, T. Gu, H. Pung, Ontology based context modeling and reasoning using OWL, in: Pervasive Computing and Communications Workshops. Proceedings of the 2nd IEEE Annual Conference on, 2004, pp. 18-22
14. Joung Y.J., and Chuang F.Y. OntoZilla: An ontology-based, semi-structured and evolutionary p2p network for information systems and services. Future Generation Computer Systems 25 1 (2009) 53-63
15. Bikakis A., Patkos T., Antoniou G., and Plexousakis D. A survey of semantics-based approaches for context reasoning in ambient intelligence. Proceedings of the Workshop Artificial Intelligence Methods for Ambient Intelligence (2007), Springer 15-24
16. Shafer G. A Mathematical Theory of Evidence (1976), Princeton University Press
17. Liu L., and Yager R.R. Classic works of the Dempster-Shafer theory of belief functions: An introduction. Studies in Fuzziness and Soft Computing, Vol. 219/2008 (2008), Springer 1-34
18. Orponen P. Dempster's rule of combination is # p-complete. Artificial Intelligence (1990) 243-245
19. Voorbraak F. A computationally efficient approximation of Dempster-Shafer theory. International Journal of Man-Machine Studies 30 5 (1989) 525-536
20. Zadeh L. Review of a mathematical theory of evidence. AI Magazine 5 3 (1984) 81
21. J. Cassens, A. Kofod-Petersen, Designing explanation aware systems: The quest for explanation patterns, in: Proceedings of the 22nd International Conference on Artificial Intelligence Workshop, 2007, pp. 303-316
22. J.A.F. Nieto, M.E.B. Gutierrez, B.P. Lancho, Developing home care intelligent environments: From theory to practice, in: Proceedings of the 7th International Conference on Practical Applications of Agents and Multi-Agent Systems, 2009, pp. 2-12
23. Hedges M., Blanke T., and Hasan A. Rule-based curation and preservation of data: A data grid approach using iRODS. Future Generation Computer Systems 25 4 (2009) 446-452
24. Gu T., Pung H.K., and Zhang D.Q. Toward an OSGi-based infrastructure for context-aware applications. IEEE Pervasive Computing 3 4 (2004) 66-74
25. A.D. Sarma, O. Benjelloun, A. Halevy, J. Widom, Working models for uncertain data, in: Proceedings of the 22nd International Conference on Data Engineering, ICDE'06, 2006, pp. 162-174
26. Korpipaa P., Mantyjarvi J., Kela J., Keranen H., and Malm E. Managing context information in mobile devices. IEEE Pervasive Computing 2 3 (2003) 42-51
27. G. Biegel, V. Cahill, A framework for developing mobile, context-aware applications, in: Pervasive Computing and Communications, Proceedings of the 2nd IEEE Annual Conference on, 2004, 361-365
28. Ma Y., Kalashnikov D.V., and Mehrotra S. Toward managing uncertain spatial information for situational awareness applications. IEEE Transactions on Knowledge and Data Engineering 20 10 (2008) 1408-1423
29. Cooper G.F. The computational complexity of probabilistic inference using bayesian belief networks (research note). Artificial Intelligence 42 2-3 (1990) 393-405
30. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Machine Learning 29 2-3 (1997) 131-163
31. McClean S., Scotney B., and Shapcott M. Aggregation of imprecise and uncertain information in databases. IEEE Transactions on Knowledge and Data Engineering 13 6 (2001) 902-912
32. Murphy R. Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Transactions on Robotics and Automation 14 2 (1998) 197-206
33. Hong X., Nugent C., Mulvenna M., McClean S., Scotney B., and Devlin S. Evidential fusion of sensor data for activity recognition in smart homes. Pervasive and Mobile Computing 5 3 (2009) 236-252
34. H. Wu, Sensor data fusion for context-aware computing using DS theory, Ph.D. thesis, Carnegie Mellon University, 2003
35. Dubois D., and Prade H. Consonant approximation of belief functions. International Journal of Approximate Reasoning 4 5-6 (1990) 419-449
36. Yager R. On the Dempster-Shafer framework and new combination rules. Information Sciences: an International Journal 41 2 (1987) 93-137
37. Smets P. The combination of evidence in the transferable belief model. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 5 (1990) 447-458
38. Dempster A.P. A generalization of bayesian inference. Journal of the Royal Statistical Society, Series B 30 (1968) 205-247
39. D.Q. Zhang, J.N. Cao, J.Y. Zhou, M.Y. Guo, Extended Dempster-Shafer theory in context reasoning for ubiquitous computing environments, in: Proceedings of the 7th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2009
40. Huang K., Wang L., Zhang D., and Liu Y. Optimizing the BitTorrent performance using an adaptive peer selection strategy. Future Generation Computer Systems 24 7 (2008) 621-630
41. Department of Architecture, Massachusetts Institute of Technology, House_n project. http://architecture.mit.edu/house_n/intro.html
42. Zhang N.L., and Poole D. Exploiting causal independencies in Bayesian network inference. Journal of Artifical Intelligence Research (1996) 301-328