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