Adaptive Context-Aware Design Using Context State Information for the Internet of Things Paradigm
Keywords:Context adaptation, context event alignment, context state information, finite state machine, semantic localization
In the Internet of Things (IoT) paradigm, context state information plays a critical role in advancing the development of adaptive pervasive applications. Pervasive services and context-aware computing are emerging as the next computing paradigms in which infrastructure and services are seamlessly available anywhere, anytime, and in any format. The IoT paradigm raises new opportunities and demands on the underlying systems, in particular, the need to have systems that are adaptive and context-aware using context state information. In this paper, we introduce a new adaptive context state design technique to model context-aware applications that are sensitive to context state information changes. Each context change event is captured, interpreted and reacted to so that applications and users use only the functionality and adaptability needs that are solutions to their needs. The solution is modeled using Finite State Machine (FSM) and semantic localization so that context state information within the IoT paradigm is aligned to events. The semantic localization process precisely estimates the proximity location of the user along with the quality of context (QoC) attributes using the Bluetooth cell-based approach. This semantic information is useful in determining and inferring the user activities in a location. The QoC attributes are used to determine the confidence of the user location and range of the Bluetooth beacons within the IoT domain. This will, in turn, be used to determine whether the user is in the location or not. The alignment technique in our model represents the proper and new solution concerning functionality and adaptability needs expressed by other user applications in the IoT environment. The experimental scenario results indicate that a user can continue to enjoy their daily activities while the IoT application adapts continuously to their changing needs and notifying service providers of the changes according to the events of the user.
V. Genaro Motti, “A computational framework for multi-dimensional context-aware adaptation,” in Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems, 2011, pp. 315–318.
J. E. May, “Systems and Methods for Context Aware Adaptation of Services and Resources in a Distributed Computing System,” ed: Google Patents, 2019.
A. K. Dey and G. D. Abowd, “Cybreminder: A context-aware system for supporting reminders,” in Handheld and Ubiquitous Computing, 2000, pp. 172–186.
G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, “Towards a better understanding of context and context-awareness,” in Handheld and ubiquitous computing, 1999, pp. 304–307.
H. Chang, S. Shin, and C. Chung, “Context life cycle management scheme in ubiquitous computing environments,” in Mobile Data Management, 2007 International Conference on, 2007, pp. 315–319.
L. Pei, R. Chen, Y. Chen, H. Leppakoski, and A. Perttula, “Indoor/outdoor seamless positioning technologies integrated on smart phone,” in Advances in Satellite and Space Communications, 2009. SPACOMM 2009. First International Conference on, 2009, pp. 141–145.
Y. Wang, X. Yang, Y. Zhao, Y. Liu, and L. Cuthbert, “Bluetooth positioning using RSSI and triangulation methods,” in Consumer Communications and Networking Conference (CCNC), 2013 IEEE, 2013, pp. 837–842.
T. Nuradha, I. Gnanarathne, L. Perera, D. Denipitiyage, and D. Dias, “Beacon placement algorithm for hybrid indoor positioning with Wi-Fi and bluetooth low energy,” in 2019 Moratuwa Engineering Research Conference (MERCon), 2019, pp. 135–140.
M. Golestanian and C. Poellabauer, “Indoor localization using multi-range beaconing: poster,” in Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2016, pp. 397–398.
G. Vavoula, M.-A. Tseliou, and Z. Tsiviltidou, “Bluetooth Low Energy Beacon-based Positioning for Multimedia Guides in Heritage Buildings: a Case Study,” in World Conference on Mobile and Contextual Learning, 2019, pp. 102–109.
F. Martelli, “Bluetooth low energy,” University of Bologna, vol. 25, 2014.
T. Rattenbury, N. Good, and M. Naaman, “Towards automatic extraction of event and place semantics from flickr tags,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 103–110.
B. Lee, J. Oh, H. Yu, and J. Kim, “Protecting location privacy using location semantics,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 1289–1297.
C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context aware computing for the internet of things: A survey,” Communications Surveys & Tutorials, IEEE, vol. 16, pp. 414–454, 2014.
M. Sama, S. Elbaum, F. Raimondi, D. S. Rosenblum, and Z. Wang, “Context-aware adaptive applications: Fault patterns and their automated identification,” Software Engineering, IEEE Transactions on, vol. 36, pp. 644–661, 2010.
D. Zhao and X. Zhang, “Location Semantics in Positioning Services,” in Proc. of 2 nd International Conference on Future Computer and Communication, 2010.
T. Lemlouma and N. Layaïda, “Context-aware adaptation for mobile devices,” in Mobile Data Management, 2004. Proceedings. 2004 IEEE International Conference on, 2004, pp. 106–111.
F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi, and S. Weer-awarana, “Unraveling the Web services web: an introduction to SOAP, WSDL, and UDDI,” IEEE Internet computing, vol. 6, p. 86, 2002.
H. Wang, R. Mehta, S. Supakkul, and L. Chung, “Rule-based context-aware adaptation using a goal-oriented ontology,” in Proceedings of the 2011 international workshop on Situation activity & goal awareness, 2011, pp. 67–76.
M. Raento, A. Oulasvirta, R. Petit, and H. Toivonen, “ContextPhone: A prototyping platform for context-aware mobile applications,” IEEE Pervasive Computing, vol. 4, pp. 51–59, 2005.
Y. Wang, “An FSM model for situation-aware mobile application software systems,” in Proceedings of the 42nd annual Southeast regional conference, 2004, pp. 52–57.
H. Rudin, “An informal overview of formal protocol specification,” IEEE Communications Magazine, vol. 23, pp. 46–52, 1985.
S. Wu, Q. Liu, P. Bai, L. Wang, and T. Tan, “SAPE: A System for Situation-Aware Public Security Evaluation,” in Thirtieth AAAI Conference on Artificial Intelligence, 2016.
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, 2015, pp. 386–399.
A. Arfaoui, S. Cherkaoui, A. Kribeche, and S. M. Senouci, “Context-Aware Adaptive Remote Access For IoT Applications,” IEEE Internet of Things Journal, 2019.
X. Yan, X. He, J. Yu, and Y. Tang, “White-Box Traceable Ciphertext-Policy Attribute-Based Encryption in Multi-Domain Environment,” IEEE Access, vol. 7, pp. 128298–128312, 2019.
M. Weber and E. Lee, “A model for semantic localization,” in Proceedings of the 14th International Conference on Information Processing in Sensor Networks, 2015, pp. 350–351.
R. J. Ohira, T. Sullivan, A. J. Abotomey, and J. Jo, “A Comparative Study of Wi-Fi and Bluetooth for Signal Strength-Based Localisation,” in Robot Intelligence Technology and Applications 4, ed: Springer, 2017, pp. 589–597.
A. Manzoor, H.-L. Truong, and S. Dustdar, “On the evaluation of quality of context,” in European Conference on Smart Sensing and Context, 2008, pp. 140–153.
Z. Abid, S. Chabridon, and D. Conan, “A framework for quality of context management,” in International Workshop on Quality of Context, 2009, pp. 120–131.
D. Helms, M. Wallace, D. Young, C. Sexton, and D. Martin, “Interleaving Multiple Bluetooth Low Energy Advertisements,” ed: US Patent 20,160,105,788, 2016.
D. Deugo, “Using Beacons for Attendance Tracking,” in Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS), 2016, p. 155.
D. Priefer, P. Kneisel, and G. Taentzer, “JooMDD: A model-driven development environment for web content management system extensions,” in Proceedings of the 38th International Conference on Software Engineering Companion, 2016, pp. 633–636.