Building Robust Context Aware IoT Applications: Methods and Strategies for Detecting and Resolving Context Inconsistencies

Asia Khatoon Soomro; Yasir Arfat Malkani; Lachhman Das Dhomeja; Iqra Kanwal Lakho

Authors

Asia Khatoon Soomro Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Pakistan
Yasir Arfat Malkani Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Pakistan
Lachhman Das Dhomeja Department of IT, Faculty of Engineering and Technology, University of Sindh, Jamshoro, Pakistan
Iqra Kanwal Lakho Department of IT, Government College University, Hyderabad, Pakistan

Keywords:

Context Awareness, Context Inconsistency Detection, Context Inconsistency Resolution, Internet of Things

Abstract

The Internet of Things (IoT) has revolutionized connectivity, creating a vast network of interconnected devices that seamlessly exchange and analyze data. Within this dynamic IoT ecosystem, context-aware applications have emerged, enabling autonomous responses to events triggered by contextual information, thereby enhancing user experiences and facilitating intelligent decision-making. However, the utilization of contextual data in IoT applications has introduced a key challenge: context inconsistencies. Context inconsistency is defined as the condition in which contextual data collected from multiple sources is inaccurate, incomplete, or conflicting, leading to incorrect processing that may disrupt the behavior of context-aware applications. Context inconsistencies arise from various factors, including sensor noise, communication errors, and contradictory data sources (e.g., two motion detection sensors located in the same area may report different readings, where one sensor detects one person, and the other sensor detects three people). These inconsistencies can significantly impact the reliability and precision of IoT applications, potentially resulting in erroneous decisions and degraded user experiences. To address this critical concern, this research paper undertakes a comprehensive review of contemporary methodologies developed for detecting and resolving context inconsistencies in IoT environments. This study explores various strategies, discusses their features in detail, and contributes by classifying them into different categories for better understanding. Through a detailed examination of the effectiveness, strengths, and limitations of each classified method, the paper aims to offer valuable insights into managing context inconsistencies in IoT applications. More precisely, this paper serves as a valuable resource for researchers, practitioners, and industry professionals in the IoT domain, providing them with a comprehensive understanding of context inconsistency detection and resolution methods.

References

Malkani Y. A., P. Bai, L.D Dhomeja, M.K Abbasi Policy-Based Contextual Access Control Mechanism for Smart IoT Devices. The Asian Bulletin of Big Data Management, 2024, Vol. 04, No. 02.

Aljawarneh, M., L. D. Dhomeja and Y. A. Malkani. Context-aware service composition of heterogeneous services in pervasive computing environments: A review. In proceedings of 19th IEEE International Multi Topic Conference Pakistan, December 2016.

Chen, C., Ye, C., & Jacobsen, H. A. Hybrid context inconsistency resolution for context-aware services. In 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom) IEEE, 2011 (pp. 10-19).

Xu, C., Cheung, S. C., Chan, W. K., & Ye, C. Heuristics-based strategies for resolving context inconsistencies in pervasive computing applications. In 2008 The 28th International Conference on Distributed Computing Systems IEEE, 2008, (pp. 713-721).

Bu, Y., Gu, T., Tao, X., Li, J., Chen, S., & Lu, J. Managing quality of context in pervasive computing. In 2006, the Sixth International Conference on Quality Software (QSIC'06). IEEE, 2006, (pp. 193-200.

Chomicki, J., Lobo, J., & Naqvi, S. Conflict resolution using logic programming. IEEE Transactions on knowledge and data engineering, 2003,15(1), 244-249.

Manzoor A, Truong HL, Dustdar S. On the evaluation of quality of context. InEuropean conference on smart sensing and context 2008 Oct 29 (pp. 140-153). Berlin, Heidelberg: Springer Berlin Heidelberg.

McAllister DF, Sun CE, Vouk MA. Reliability of voting in fault-tolerant software systems for small output-spaces. IEEE Transactions on Reliability. 1990 Dec;39(5):524-34.

Lee, B. H., & Kim, D. H. Efficient context-aware selection based on user feedback. IEEE Transactions on Consumer Electronics, 2012, 58(3), 978-984.

Xu, H., Wang, L., Xiong, H., Du, Z., & Xie, Z. Effective context inconsistency elimination algorithm based on feedback and reliability distribution for IOV. China Communications, 2014, 11(10), 16-28.

Wang, L., Xu, H., Sun, G., Du, Z., Xie, Z., & Zheng, L. Context Inconsistency Elimination Based on User Feedback and Modified Evidence Theory. International Journal of Machine Learning and Computing, 2013, 3(6), 473.

Ji, M., Xu, H., Wang, L., Dang, J., Xu, Z., & Fang, H. Approach of measuring PoC of context using limited self‐feedback in context‐aware systems. IET Wireless Sensor Systems, 2016, 6(5), 158-165.

Zhang, D., Chen, M., Huang, H., & Guo, M. Decentralized checking of context inconsistency in pervasive computing environments. The Journal of Supercomputing, 2013, 64, 256-273.

Hamed, M., Abdelkader, H., & Abdelatey, A. Resolving Context Inconsistency Approach Based on Random Forest Tree. In International Conference on Advanced Machine Learning Technologies and Applications, 2022, (pp. 192-199). Cham: Springer International Publishing.

Lee, Y. J., Lim, J., Hyun, S. J., & Lee, D. Rule-based approach for context inconsistency management scheme in ubiquitous computing. In 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing IEEE, 2010 (pp. 120-126).

Wang, H., Xu, C., Guo, B., Ma, X., & Lu, J. Generic adaptive scheduling for efficient context inconsistency detection. IEEE Transactions on Software Engineering, 2019, 47(3), 464-497.

Xu, C., Cheung, S. C., Chan, W. K., & Ye, C. Partial constraint checking for context consistency in pervasive computing. ACM Transactions on Software Engineering and Methodology (TOSEM), 2010, 19(3), 1-61.

Buchholz, T., Küpper, A., & Schiffers, M.Quality of context: What it is and why we need it. In Workshop of the HP OpenView University Association,2003.

Manzoor, A., Truong, H. L., & Dustdar, S. Using quality of context to resolve conflicts in context-aware systems. In International Workshop on Quality of Context, 2009 , (pp. 144-155). Berlin, Heidelberg: Springer Berlin Heidelberg.

Fan, S., Xu, H., Xiong, H., Chen, M., Liu, Q., Xing, Q., & Li, T. A new QoC parameter and corresponding context inconsistency elimination algorithms for sensed contexts and non-sensed contexts. Applied Intelligence, 2022, 52(1), 681-698.

Chen, M., Xu, H., Xiong, H., Pan, L., Du, B., & Li, F. A new overall quality indicator OQoC and the corresponding context inconsistency elimination algorithm based on OQoC and Dempster–Shafer theory. Soft Computing, 2020, 24, 10829-10841.

Dempster, A. P. Upper and lower probabilities induced by a multivalued mapping. In Classic works of the Dempster-Shafer theory of belief functions, 2008, (pp. 57-72). Berlin, Heidelberg: Springer Berlin Heidelberg.

Liu, Q., Xu, H., He, B., Yuan, H., Liu, Z., Fan, S., ... & Li, S. A novel context inconsistency elimination algorithm based on the optimized Dempster-Shafer evidence theory for context-awareness systems. Applied Intelligence, 2023, 53(12), 15261-15277.

Ben‐Gal, I. (2008). Bayesian networks. Encyclopedia of statistics in quality and reliability.

Ko, K. E., & Sim, K. B. Context aware system based on Bayesian network driven context reasoning and ontology context modeling. International Journal of Fuzzy Logic and Intelligent Systems, 2008, 8(4), 254-259.

Zadeh, L. A. Fuzzy logic. In Granular, Fuzzy, and Soft Computing, 2023, (pp. 19-49). New York, NY: Springer US.

Hellmann, M. Fuzzy logic introduction. Université de Rennes, 2001, 1(1).

Pérez-Gaspar, M., Gomez, J., Bárcenas, E., & Garcia, F. A fuzzy description logic based IoT framework: Formal verification and end user programming. Plos one, 2024, 19(3).:e0296655.