Abstract:
IoT enabled devices are becoming highly popular in various domains of industrial and domestic usages. The gain in the popularity is primarily due to the higher adoptability to the situations, effective mobility, availability of the applications for managing data collected from the IoT devices. Also, few of the IoT implementations have demonstrated significant improvements for open IoT stack deployment using Wireless Sensor Networks, which is again accelerated the growth of adaptations. Nonetheless, the IoT enabled device implementations comes with fundamental challenges due to group or cluster-based aggregations. The primary challenge is low battery life due to the higher computational loads and due to the proximity-based load distribution during cluster head-oriented computations. This issue is more prominent for the IoT enabled device deployments for agricultural purposes due to the huge size of the deployment site. Number of research attempts aimed to solve this problem in the recent times. Nevertheless, the existing solutions are highly criticized for higher complexity for deployment and adaptations. Hence, this work proposes a novel solution to balanced workload distribution using probabilistic and regression-based analysis, which results into a highly energy efficient and time efficient cluster head node selection strategy. The probabilistic selection strategy is integrated and optimized with the regression driven analysis to ensure highly random selection and at the same time highly effective selection based on various parameters such as mobility, computational capacity, proximity, and memory consistency. As a result, this strategy demonstrates nearly 92% improvements for time efficiency and nearly 94% improvements for energy efficiency compared with the parallel research outcomes.
Page(s):
5632-5640
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 17, Year: 2022
Keywords:
probability distribution
,
AgroIoT
,
Probabilistic Selection
,
Equivalency Coefficient
,
energy efficient