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Performance Optimization in IoT-Based Next-Generation Wireless Sensor Networks

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Transactions on Computational Collective Intelligence XXXIII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 11610))

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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.

This research work was funded in part by the Higher Education Commission of Pakistan under the research grant number 288.67/TG/R&D/HEC/2018/25181.

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Notes

  1. 1.

    It is worth noting that over larger distances, such loss factors demand a higher amount of energy yielding sudden death of the network. This is often missed by traditional protocols assuming lossless channel. Therefore, avoiding these power-hungry transmissions significantly optimize resources.

  2. 2.

    Here, we presented calculations for \(\varvec{A = \pi 150^2}\text { m}^{\varvec{2}} \text { and } \varvec{L = 100}\) merely for the ease of understanding. However, for any other small or large scale network configuration, the computations can be done in a similar fashion using the proposed expressions.

  3. 3.

    Due to space limitations, a detailed version of these results along with their pictorial representations [48, 49] are available at: https://arxiv.org/abs/1806.09980.

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Authors and Affiliations

  1. University of Oulu, 90014, Oulu, Finland

    Muzammil Behzad

  2. COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Muzammil Behzad, Muhammad Talal Hassan & Mahmood Ashraf Khan

  3. King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia

    Manal Abdullah

  4. The Chinese University of Hong Kong, Shatin, 999077, Hong Kong

    Yao Ge

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  1. Muzammil Behzad
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  4. Yao Ge
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  5. Mahmood Ashraf Khan
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Correspondence to Muzammil Behzad .

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  1. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Ryszard Kowalczyk

  3. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

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

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