Computing Quality of Information for Wireless Sensor Networks


Quality of information, Multi-sensor systems, QoI structural, Accuracy, Reward-and-punishment mechanism, Structural and computer related design, Sensor belief.

How to Cite

Jiajun Jiang, Hyeonin Shin, Chung-Hao Chen, Lijia Chen, Wen-Chao Yang. Computing Quality of Information for Wireless Sensor Networks. Int. J. Archit. Eng. Technol. [Internet]. 2018 Dec. 31 [cited 2022 Aug. 12];5(1):52-6. Available from:


Wireless sensor networking research is a structural and computer related design that mainly focused on internal wireless sensor network issues such as MAC and routing protocols, energy saving, hard ware design and to some extent on the architecture of gateways that connect a wireless sensor network with the rest of the world. They offer a low-cost solution that provides a high data density. Information obtained from such systems are imprecise in nature but is used for important decision making tasks. This precipitates the need to dynamically compute the quality of information (QoI) based on sensor observations. However, the sensors deployed in an environment do not have the same belief level due to their differences in capabilities and imprecision in sensing and processing. The belief in a sensor represents the level of accuracy in accomplishing a task that can be computed either by comparing the current observation with a reference data set or by performing a physical investigation. It is essential to understand how the sensors are performing with respect to the objective tasks. In this paper we propose a modified Information-driven sensor query (IDSQ) algorithm using reward-and-punishment mechanism to dynamically compute the belief in sensors by leveraging the differences of the individual sensor’s opinion. In this structural, results show the suitability of utilizing the dynamically computed belief as an alternative to the accuracy of the sensors. The structural can then be used to distribute the model processing into the wireless sensor network.


Hunkeler U, Scotton P, "A quality-of-information-aware framework for data models in wireless sensor networks," Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE International Conference on, vol., no., pp.742-747, Sept. 29 2008-Oct. 2 2008

Hossain MA, Ahmed DT, Parra J, "A framework for computing quality of information in multi-sensor systems," Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, vol., no., pp.1842-1846, 13-16 May 2012

Bisdikian C, "On Sensor Sampling and Quality of Information: A Starting Point," Pervasive Computing and Communications Workshops, 2007. PerCom Workshops '07. Fifth Annual IEEE International Conference on, vol., no., pp.279-284, 19- 23 March 2007

Abid Z, Chabridon S, "A fine-grain approach for evaluating the quality of context," Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, 2011; 21(25): pp.444-449,

Atrey PK, El Saddik A, "Confidence Evolution in Multimedia Systems," Multimedia, IEEE Transactions on, 2008; 10(7): 1288-1298.

Rolik J, Abdelrahman M, Kandasamy P, "A confidence-based approach to the self-validation, fusion and reconstruction of quasi-redundant sensor data," Instrumentation and Measurement, IEEE Transactions on, vol.50, no.6, pp.1761- 1769, Dec 2001

Hughes K, Ranganathan N, "A model for determining sensor confidence," Robotics and Automation, 1993. Proceedings, 1993 IEEE International Conference on, 1993; 2, 2-6: 136-141.

Hossain MA, Atrey PK, El Saddik A, "Learning Multisensor Confidence Using a Reward-and-Punishment Mechanism," Instrumentation and Measurement, IEEE Transactions on, 2009; 58(5): 1525-1534.

Guestrin C, Bodik P, Thibaux R, Paskin M, Madden S, "Distributed regression: an efficient framework for modeling sensor network data," Information Processing in Sensor Networks, 2004. IPSN 2004. Third International Symposium on, 2004; 1- 10: 26-27.

Deshpande A, Guestrin C, Madden S, Hellerstein JM and Hong W. “Model-driven data acquisition in sensor networks,” in Proceedings of the Thirtieth International Conference on Very Large Data Base(VLDB’ 04), 2004; pp.588-599

GJ. Pottie and WJ. Kaiser, “Wireless integrated network sensors.” Comm. ACM, 2000; 43(5): 51-58.

Frolik J, Abdelrahman, M, Kandasamy P, "A confidencebased approach to the self-validation, fusion and reconstruction of quasi-edundant sensor data," Instrumentation and Measurement, IEEE Transactions on , 2001; 50(6): 1761-1769.

M. Chu, Haussecker, and F. Zhao, “Scalable Informationdriven sensor qurying and routing for ad hoc heterogeneous sensor networks,” Int. J. H High-Performance Compu. Applicat, 2001

Feng Zhao, Jaewon Shin, Reich J. "Information-driven dynamic sensor collaboration," Signal Processing Magazine, IEEE, 2002; 19(2): pp.61-72.

Hossain MA, Atrey PK, El Saddik A. "Learning Multi-Sensor Confidence using Difference of Opinions," Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE, 2008; 12-15: pp.809-813.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2018 Jiajun Jiang, Hyeonin Shin; Chung-Hao Chen; Lijia Chen, Wen-Chao Yang