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Title Environmental monitoring smart system with selfsustaining wireless sensor network using data validation algorithms
Authors Kanwal, T.
Altaf, S.
Javed, M.K.
Keywords wireless sensor network
data validation
feature extraction
feature identification
algorithm
Type Article
Date of Issue 2020
URI https://essuir.sumdu.edu.ua/handle/123456789/77634
Publisher Sumy State University
License
Citation Kanwal, T., Altaf, S., Javed, M. K. (2020). Environmental monitoring smart system with selfsustaining wireless sensor network using data validation algorithms. Journal of Engineering Sciences, Vol. 7(1), pp. E10–E19, doi: 10.21272/jes.2020.7(1).e3
Abstract Study in Wireless Sensor Network (WSN) has been becoming an emerging and promising research topic aiming for the advancement in the Internet of Things (IoT) for a reliable connection. The capability of the wireless sensor to be used in a complex environment can become hard to reach areas and also be able to communicate in an ad-hoc manner, attracted researchers in recent times. Development in wireless sensor network producing a lot of new applications to sense environment remotely are facing challenges restricting it to perform up to its potential. Data validation and data reliability are such existing problems in this domain that needed to be addressed. Because sensed data cannot be blindly trusted upon, as it may have faults and errors occurred with-in the sensing environment. Besides, to guarantee the active state of the sensing system in a remote area is also essential in terms of power usage and management. The focus of the paper is data validation acquired from sensors deployed in remote areas. Although, lots of data validation algorithms have been proposed by researchers to identify single data fault. However, our research identifies multiple faults, namely spike fault, out of range fault, outliers, and stuck at fault using a hybrid form of an algorithm. A comparison with the existing algorithm shows that the proposed algorithm improved data validation by 97 % in detecting multiple data faults using Artificial Intelligence techniques.
Appears in Collections: Journal of Engineering Sciences / Журнал інженерних наук

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United States United States
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Vietnam Vietnam
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