Enhancing resource utilization and privacy in IoT data placement through fuzzy logic and PSO optimization

The exponential growth of Internet of Things (IoT) devices has ushered in an era of vast data generation, necessitating abundant resources for data storage and processing. Cloud environment forms a notorious paradigm for such data accommodation. Meanwhile, the privacy issues assimilated in IoT data provoke huge complications in data placement. In addition, it is significant to consider factors such as energy efficiency, energy utility of cloud and data access time of IoT applications while allotting resources for IoT data. In light of this circumstance, this research proposes a Fuzzy- Particle Swarm Optimization (PSO) framework to optimize IoT-oriented data placement in cloud data centers. The fuzzy Logic is adept at handling the uncertainty inherent in parameters such as resource availability and privacy sensitivity. Through membership functions and a Fuzzy Inference System, imprecise attributes are quantified, enabling smarter decision-making. Using its intelligence, it prioritizes the task with high sensitivity and resource availability to perform ideal allocation preferring best suitable resource feature unit. The integration of improved PSO leverages its capability to explore complex solution spaces and converge on optimal solutions. The greedy strategy in improved PSO assists in exploring most-optimal virtual machine instance in cloud to improve its resource efficacy. These facets culminate in a framework that holistically manages IoT-generated data, optimizing energy consumption, resource utilization, and data access time, while simultaneously upholding privacy constraints. The results underscore the potency of this approach in offering optimal data management in cloud environments, achieving better resource utilization of 89%, privacy sensitivity of 98.5%, and less energy consumption of 0.7 kWh.