Abstract
Wireless Body Area Networks (WBANs) are emerging in the livestock industry for remote monitoring of cattle using wireless body sensors (WBS). The random mobility of animals acting as nodes causes the network’s topology to change rapidly, originating from scalability and reliability issues. Stable transmission of acquired data to the base station requires an intelligent clustering mechanism that reduces the energy consumption and fulfills the network’s constraints. Several clustering techniques are available as a solution, but these techniques yield numerous cluster heads, resulting in more energy utilization. Higher energy utilization lessens the effective life of WBSs and increases monitoring costs. This paper presents a metaheuristic approach for selecting optimal clusters in WBANs to realize an energy-efficient routing protocol for livestock health and behavior monitoring. The proposed approach employs Ant Lion Optimizer (ALO) to select the optimal clusters for different pasturage sizes using sensors of different transmission ranges considering user’s preferences about cluster density. The proposed technique with ALO is compared with other recent techniques such as Ant Colony Optimization, Grasshopper Optimization, and Moth Flame Optimization. The comparison results show the proposed technique’s effectiveness in realizing energy-efficient protocols of WBANs for remote monitoring applications.
Highlights
T HE livestock industry plays a significant contribution to the economic development of a country, and the profit gains from this industry mainly depend upon the wellbeing and good health of the animals
The communication link between various animals and Remote Base Station (RBS) originates from an Ad-hoc Network (ANET) in which the animals act as nodes of the ANET, which is termed as Wireless Body Area Networks (WBANs)
The experiments are performed on MATLAB for various grid sizes, and results are compared with other approaches like ant colony optimization (ACO), grasshopper optimization (GHO), and moth flame optimization (MFO)
Summary
T HE livestock industry plays a significant contribution to the economic development of a country, and the profit gains from this industry mainly depend upon the wellbeing and good health of the animals. Though monitoring is manually possible, data collection and processing are challenging and applicable to small-sized farms only. To meet larger farms’ requirements with hundreds to thousands of animals, WBS is used for remote monitoring. In such remote schemes, animals are monitored with internal and external body sensors that communicate wirelessly to provide real-time data to a Remote Base Station (RBS). The communication link between various animals and RBS originates from an Ad-hoc Network (ANET) in which the animals act as nodes of the ANET, which is termed as WBANs. Integrated systems are developed in which the collected data are stored in databases to construct mathematical models and knowledge bases after data processing [1]
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