IoE-Enabled Smart Embedded System: An Innovative Way of Learning

Rathod, Akshada; Ayare, Prachi; Bobhate, Ramchandra; Sachdeo, Rajneeshkaur; Sarode, Sambhaji; Malhotra, Jyoti

doi:10.1007/978-981-13-7166-0_66

Cited by 3 publications

(2 citation statements)

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“…More benefits can be expected while executing a cross‐layer design along with an optimization framework 19,20 . Particularly, it helps in improving the efficiency of protocol operations and it also aids in improving the network lifetime with minimal data losses 21–26 …”

Section: Introductionmentioning

confidence: 99%

“…19,20 Particularly, it helps in improving the efficiency of protocol operations and it also aids in improving the network lifetime with minimal data losses. [21][22][23][24][25][26] Still, the cross-layer framework across varied layers of protocol stack suffers from several challenging and difficult problems. 27,28 The major problems related to the exploitation of cross-layer signaling with the network comprise of problems with overhead, processing efficiency, nonconformant routers, delay in data delivery, and security issues.…”

Section: Introductionmentioning

confidence: 99%

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Secured cross‐layer cross‐domain routing in dense wireless sensor network: A new hybrid based clustering approach

Lahane

Jariwala

2021

Int J Intell Syst

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In wireless sensor network (WSN), increasing the network life span remains as a crucial challenge yet to be resolved. The modeling of effectual methods is necessary for conserving the scarce energy resources in WSN. To overcome such issues, cross‐layer protocols are exploited, which concerns routing the messages with increased lifetime. This study introduces a new cross‐layer design routing model under a clustering‐based approach. More importantly, the cluster head is optimally selected by a new hybrid algorithm termed as moth flame integrated dragonfly algorithm. Moreover, the optimal selection of cluster head is carried out based on parameters such as energy consumption, delay, distance, throughput, security, and overhead. Finally, the supremacy of the presented model is proved over existing models in terms of alive node analysis and network lifetime analysis. The experimental outcomes show that the proposed algorithm for test case 3 has accomplished a higher value of 66.229, which is 29.07%, 13.33%, 26.36%, and 9.67% better than conventional ant lion optimisation approach, grouped grey wolf search optimisation, firefly replaced position update in da, and alpha wolf‐assisted whale optimization algorithm, respectively, for median case scenario.

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Section: Introductionmentioning

confidence: 99%