Next-Generation WSN for Environmental Monitoring Employing Big Data Analytics, Machine Learning and Artificial Intelligence

Shaikh, Rumana Abdul Jalil; Naidu, Hari Kumar; Kokate, Piyush

doi:10.1007/978-981-15-5258-8_20

Cited by 17 publications

(1 citation statement)

References 43 publications

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“…Sensor networks are used to collect environmental data such as light, moisture and temperature. The cost and size of the sensor nodes allow this technology to be used in a wide range of applications, including smart agriculture, medical, smart cities, industrial and military applications [4][5][6][7]. A major problem in the development of wireless sensor networks is the energy limitation of the sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

EFTVG: An Energy Efficient Fuzzy–Timer Clustering Approach in an Adaptive Virtual Grid Cluster Based WSN

Mazinani

Alyasiri

2023

Preprint

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Clustering is an effective way that improves WSN lifetime. In this paper, unlike the other approaches, an adaptive virtual grid is conducted to form cluster boundaries during network lifetime. Using the adaptive virtual grid not only shrinks the size of clusters but also determines network configuration based on the location of the base station. In EFTVG, compared to the previous clustering methods which used a specific cluster head selection procedure for the entire network lifetime, we propose a consecutive clustering (fuzzy-timer) approach. Authors divide the network lifetime into two parts. Firstly, a fuzzy clustering is applied to address uncertainty in a semi-distributed manner. Then a timer-based clustering is used in the second part to tackle sharp energy consumption in a fully-distributed manner. A local threshold is applied in each cluster to specify whether cluster head selection is required or not. Using this novelty, the network may encounter new cluster heads in some clusters, while we trust the ex-cluster head in others. Applying this policy results in energy saving. Finally, we present a novel hybrid routing to reduce energy consumption in WSN. EFTVG is compared with MOFCA, EAFCA, EAUCF, FLECH, DECUC, FUCA, DUCF, FSVGA and FMCR-CT approaches in four different scenarios.

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Section: Introductionmentioning

confidence: 99%