Abstract
The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, including ontology-driven multi-agent rule-based reasoning. Rule-based reasoning is a relatively simple model that can be adapted to different real-world problems. It can be developed considering a set of assertions, which collectively constitute the working memory, and a set of rules that specify how to act on the assertion set. However, the size of the working memory is crucial when developing context-aware systems in resource constrained devices such as smartphones and wearables. In this paper, we discuss rule-based context-aware systems and techniques for determining the required working memory size for a fixed set of rules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ligeza, A.: Logical Foundations for Rule-Based Systems, vol. 11, no. 2. Springer, Heidelberg (2006)
Giarratano, J.C., Riley, G.: Expert systems, principles and programming, Thomson course of technology. Boston, Australia (2005)
Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. (2005)
Tai, W., Keeney, J., O’Sullivan, D.: Resource-constrained reasoning using a reasoner composition approach. Semant. Web 6, 35–59 (2015)
Rakib, A., Haque, H.M.U.: A logic for context-aware non-monotonic reasoning agents. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014. LNCS (LNAI), vol. 8856, pp. 453–471. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13647-9_41
Rakib, A., Uddin, I.: An efficient rule-based distributed reasoning framework for resource-bounded systems. Mob. Netw. Appl. 24(1), 82–99 (2019)
Sarker, I.H.: Mobile data science: towards understanding data-driven intelligent mobile applications. arXiv preprint arXiv:1811.02491 (2018)
Sarker, I.H.: BehavMiner: mining user behaviors from mobile phone data for personalized services. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 452–453. IEEE Computer Society (2018)
Chronis, I., Madan, A., Pentland, A.: SocialCircuits: the art of using mobile phones for modeling personal interactions. In: Proceedings of the ICMI-MLMI 2009 Workshop on Multimodal Sensor-Based Systems and Mobile Phones for Social Computing, pp. 1–4 (2009)
Jung, J.J.: Contextualized mobile recommendation service based on interactive social network discovered from mobile users. Expert Syst. Appl. 36(9), 11950–11956 (2009)
OlguÃn, D.O., Waber, B.N., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible organizations: technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(1), 43–55 (2008)
Aly, W.M., Eskaf, K.A., Selim, A.S.: Fuzzy mobile expert system for academic advising. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5. IEEE (2017)
Ghasempour, A.: Optimized scalable decentralized hybrid advanced metering infrastructure for smart grid. In: 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 223–228. IEEE (2015)
Ghasempour, A.: Optimum packet service and arrival rates in advanced metering infrastructure architecture of smart grid. In: 2016 IEEE Green Technologies Conference (GreenTech), pp. 1–5. IEEE (2016)
Ghasempour, A.: Optimized advanced metering infrastructure architecture of smart grid based on total cost, energy, and delay. In: 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–6. IEEE (2016)
Ghasempour, A.: Optimizing the advanced metering infrastructure architecture in smart grid. Utah State University (2016)
Sharma, V., Song, F., You, I., Atiquzzaman, M.: Energy efficient device discovery for reliable communication in 5G-based IoT and BSNs using unmanned aerial vehicles. J. Netw. Comput. Appl. 97, 79–95 (2017)
Sharma, V., You, I., Andersson, K., Palmieri, F., Rehmani, M.H., Lim, J.: Security, privacy and trust for smart mobile-internet of things (M-IoT): a survey. IEEE Access 8, 167123–167163 (2020)
Abulkhair, M.F., Ibrahim, L.F.: Using rule base system in mobile platform to build alert system for evacuation and guidance. Int. J. Adv. Comput. Sci. Appl. 7(4), 68–79 (2016)
Mukherjee, C.: Build Android-Based Smart Applications: Using Rules Engines, NLP and Automation Frameworks. Apress (2017)
Uddin, I.: A rule-based framework for developing context-aware systems for smart spaces. Ph.D. thesis, University of Nottingham (2019)
Alirezaie, M., et al.: An ontology-based context-aware system for smart homes: E-care@home. Sensors (Basel, Switzerland) 17 (2017)
Abdur, R.: Smart space system interoperability. In: Proceedings of the 3rd International Workshop on (Meta)Modelling for Healthcare Systems, Bergen, Norway, vol. 2336, pp. 16–23. CEUR Workshop Proceedings (2018)
Uddin, I., Rakib, A., Haque, H.M.U., Vinh, P.C.: Modeling and reasoning about preference-based context-aware agents over heterogeneous knowledge sources. Mob. Netw. Appl. 23, 13–26 (2018)
Streitz, N.A., Charitos, D., Kaptein, M., Böhlen, M.: Grand challenges for ambient intelligence and implications for design contexts and smart societies. J. Ambient Intell. Smart Environ. 11, 87–107 (2019)
Mahalle, P.N., Dhotre, P.S.: Context-Aware Pervasive Systems and Applications. ISRL, vol. 169. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9952-8
Cook, D., Das, S.: Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing). Wiley, Hoboken (2004)
Noy, N., McGuinness, D., Hayes, P.J.: Semantic integration & interoperability using RDF and OWL. W3C Editor’s Draft 3, November 2005
Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5, 4–7 (2001)
Uddin, I., Rakib, A.: A preference-based application framework for resource-bounded context-aware agents. In: Kim, K.J., Joukov, N. (eds.) ICMWT 2017. LNEE, vol. 425, pp. 187–196. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5281-1_20
Wang, X.H., Zhang, D.Q., Gu, T., Pung, H.K.: Ontology based context modeling and reasoning using OWL. In: IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22 (2004)
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a Semantic Web rule language combining OWL and RuleML. Acknowledged W3C submission, standards proposal research report: Version 0.6, April 2004
Grosof, B., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logics. In: The Twelfth International World Wide Web Conference, Budapest, pp. 48–57. ACM (2003)
Rakib, A., Ul Haque, H.M., Faruqui, R.U.: A temporal description logic for resource-bounded rule-based context-aware agents. In: Vinh, P.C., Alagar, V., Vassev, E., Khare, A. (eds.) ICCASA 2013. LNICST, vol. 128, pp. 3–14. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05939-6_1
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Uddin, I., Rakib, A., Ali, M., Vinh, P.C. (2021). Memory-Constrained Context-Aware Reasoning. In: Cong Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications. ICCASA 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 409. Springer, Cham. https://doi.org/10.1007/978-3-030-93179-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-93179-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93178-0
Online ISBN: 978-3-030-93179-7
eBook Packages: Computer ScienceComputer Science (R0)