Fuzzy Reinforcement Learning Model for Resource Adaptation in IoT
Daneshwari I. Hatti1, Ashok V. Sutagundar2

1Daneshwari I. Hatti*, Assistant Professor, Department of Electronics and Communication Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Affiliated to VTU Belagavi, Vijayapur, Karnataka, India.
2Ashok V. Sutagundar, Assistant Professor, Department of Electronics and Communication Engineering, BEC Bagalkot, Karnataka, India.

Manuscript received on May 08, 2021. | Revised Manuscript received on May 15, 2021. | Manuscript published on May 30, 2021. | PP: 162-167 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.A58240510121 | DOI: 10.35940/ijrte.A5824.0510121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The automation of several applications is creating engrossment in Internet of Things (IoT). The prerequisite for employing IoT in daily life is the ability to interact with devices technologies and process the sensed data. The difficulty to process the sensed data with scarce resources for diverse requests is challenging. Learning the behaviour of changing demand and processing with available resources has resulted in adaptation policy. Adaptation policy by employing Reinforcement learning and Fuzzy logic is proposed to adapt the resources for executing the tasks. Prioritization of task, allocating of resources to the tasks by adapting with available resources with assured Quality of Service (QoS) is performed. Fuzzy Q learning Adaptation Algorithm (FQAA) is designed for evaluating resource adaptation mechanisms to execute the heterogenous tasks. The algorithm with different configuration is simulated using Ifogsim and python. It is compared with traditional method that is without adaptation mechanism, performs better compared to other algorithms in terms of Cost, energy consumption and latency. 
Keywords: Adaptation, Ifogsim, Resource cost, Energy Consumption, Reinforcement Learning, Fuzzy logic.