University of Oulu

Behzad M., Abdullah M., Hassan M.T., Ge Y., Khan M.A. (2019) Performance Optimization in IoT-Based Next-Generation Wireless Sensor Networks. In: Nguyen N., Kowalczyk R., Xhafa F. (eds) Transactions on Computational Collective Intelligence XXXIII. Lecture Notes in Computer Science, vol 11610. Springer, Berlin, Heidelberg

Performance optimization in IoT-based next-generation wireless sensor networks

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Author: Behzad, Muzammil1,2; Abdullah, Manal3; Hassan, Muhammad Talal2;
Organizations: 1University of Oulu, Oulu 90014, Finland
2COMSATS University Islamabad, Islamabad 44000, Pakistan
3King Abdulaziz University, Jeddah 21589, Kingdom of Saudi Arabia
4The Chinese University of Hong Kong, Shatin 999077, Hong Kong
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2020062245103
Language: English
Published: Springer Nature, 2019
Publish Date: 2021-06-21
Description:

Abstract

In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is the data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-662-59540-4
ISBN Print: 978-3-662-59539-8
Pages: 1 - 31
DOI: 10.1007/978-3-662-59540-4_1
OADOI: https://oadoi.org/10.1007/978-3-662-59540-4_1
Host publication: Transactions on computational collective intelligence XXXIII
Host publication editor: Nguyen, Ngoc Thanh
Kowalczyk, Ryszard
Xhafa, Fatos
Type of Publication: A3 Book chapter
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © Springer-Verlag GmbH Germany, part of Springer Nature 2019. This is a post-peer-review, pre-copyedit version of an article published in Transactions on Computational Collective Intelligence XXXIII. The final authenticated version is available online at: https://doi.org/10.1007/978-3-662-59540-4_1.