Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informe...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 80; no. 8; pp. 11149 - 11190
Main Authors: Chandrasekaran, Senthil Kumar, Rajasekaran, Vijay Anand
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2024
Springer Nature B.V
Subjects:
Agricultural commodities
Agricultural production
Agriculture
Clustering
Communication
Compilers
Computer Science
Crop growth
Crop yield
Crops
Data processing
Data transmission
Energy consumption
Energy efficiency
Farmers
Fuzzy logic
Green buildings
Internet access
Internet of Things
Interpreters
Optimization
Optimization algorithms
Parameters
Performance enhancement
Processor Architectures
Programming Languages
Real time
Sensors
Soil moisture
Fuzzy logic
Internet of things
Whale optimization algorithm
Cluster head
Enhanced crow swarm optimization
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one’s throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05780-5