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

Behzad, Muzammil; Abdullah, Manal; Hassan, Muhammad Talal; Ge, Yao; Khan, Mahmood Ashraf (2019-06-21)

 
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https://doi.org/10.1007/978-3-662-59540-4_1

Behzad, Muzammil
Abdullah, Manal
Hassan, Muhammad Talal
Ge, Yao
Khan, Mahmood Ashraf
Springer Nature
21.06.2019

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

https://rightsstatements.org/vocab/InC/1.0/
© 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.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1007/978-3-662-59540-4_1
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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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