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NACO predicated hybrid model of internet of things and cloud computing to manage immensely colossal data in health accommodations applications

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Abstract

In current trend, there has been a consistent expanding big data analysis in the field of healthcare applications. Multi-criteria optimization problems are one of the challenging tasks when one or more essential criteria are interlinked together with tangible or intangible relations. It causes a total chaos when improving or optimizing a factor it may impacts positively or negatively. In multi criteria optimization problems, inspired algorithms can provide a solution naturally. There is an inflated requirement for the evaluation of existing optimization algorithms and provision of a customized naturally inspired algorithm for ‘IoT-Cloud healthcare monitoring system’. In this propound system, a novel swarm multi-initialization process and multi-swarm optimization algorithm is used to acquire the optimized result and also to overcome the high demand. Data-driven swarm selection procedure enables dynamic multi-criteria optimization. There are three phases exist in the proposed method, they are Nodal Ant Colony Optimization (NACO) IoT-Cloud Swarm Initialization, NACO IoT- Cloud Swarm Optimization and Data-driven IoT-Cloud Swarm Selection. These phases are built with the intension to improve the efficiency and resource utilization by minimizing waiting and execution times. The user interface is designed to monitor the network performance in terms of efficiency, execution time, turn-around time, waiting time and resource utilization and compared with three special optimizers such as Particle swarm optimizer (PSO), Parallel Particle swarm optimization (PPSO) and Genetic Algorithm (GA), Average efficiency of proposed method is increased by 5.78% than the average of existing algorithms. The maximum and minimum resource utilization of the proposed algorithm is 99.99% and 38.84% respectively. This method also gives the better percentage of resource utilization when compared with the existing algorithm. The execution time, turnaround time and waiting time is also very less when compared with above mentioned GA, PSO and PPSO algorithms.

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Authors and Affiliations

  1. Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    Kumar Parasuraman, Silambarasan Karunagaran & Raghavendran Srinivasan

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  1. Kumar Parasuraman
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  2. Silambarasan Karunagaran
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  3. Raghavendran Srinivasan
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Correspondence to Silambarasan Karunagaran.

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Parasuraman, K., Karunagaran, S. & Srinivasan, R. NACO predicated hybrid model of internet of things and cloud computing to manage immensely colossal data in health accommodations applications. J Ambient Intell Human Comput 13, 5477–5490 (2022). https://doi.org/10.1007/s12652-021-03180-2

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