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Using advanced distributed energy efficient clustering increasing the network lifetime in wireless sensor networks

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Abstract

There seem to be numerous items which have been developed using IoT devices and are network overhead. We must consider factors like reliability, minimal energy consumption, etc. To advance IoT, solutions must be found for issues with scalability, dependability, network optimization, and quality of service (QoS). The proposed approach considered a heterogeneous network with a long lifespan and high throughput while using less electricity. Specifications such as Area, Nodes, Sink location, and Data Aggregation must be given. Throughput, data communication rate, analysis of live nodes, and a reduction in node energy consumption are all factors in this method’s cluster head selection. But creating an embedded IoT system is complicated. We considered the ADEEC approach for autonomous cellular networks, which improves network performance and durability. It is possible to send messages in heterogeneous contexts more successfully than with current techniques. The experimental results of the proposed ADEEC outperform the throughput with 19% when compared to LEACH, 16.5% when compared to MODLEACH and 9.6% when compared to DEEC. The network life span of ADEEC outperforms 18% when compared to LEACH, 17% when compared to MODLEACH and 13% when compared to DEEC methods.

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Acknowledgements

This project is utilized the resources and lab equipment under the FIST grant Govt. of India Ref. No. SR/FST/ETI-410/2016. Also, this work is carried out as part of the sponsored project grant by DST-ICPS Govt. of India.

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KRR did literature work and proposed heterogeneous network with a long lifespan and high throughput while using less electricity. BNKR and ASK did literature work and simulated ADEEC method.

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Correspondence to B. Naresh Kumar Reddy.

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Raghava Rao, K., Naresh Kumar Reddy, B. & Kumar, A.S. Using advanced distributed energy efficient clustering increasing the network lifetime in wireless sensor networks. Soft Comput 27, 15269–15280 (2023). https://doi.org/10.1007/s00500-023-07940-4

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  • DOI: https://doi.org/10.1007/s00500-023-07940-4

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