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Applying Reinforcement Learning in distribution computational system – Internet of Things

DOI 10.18127/j19997493-202002-10

Keywords:

О.Y. Eremin – Ph.D. (Eng), 

Department «Computer Systems and Networks», Bauman Moscow State Technical University

E-mail: ereminou@bmstu.ru

M.V. Stepanova – Post-graduate Student, 

Department «Computer Systems and Networks», Bauman Moscow State Technical University E-mail: stepanova@bmstu.ru


Internet of Things (IoT) consists of numerous computational nodes which could be used to construct a distributed computational system. Traditional computational approaches for distributed systems could not be implemented into IoT infrastructure due to its nature: continuous number changing of computational nodes, continuous parameters changing in low-speed communication channel, high levels of interference in the radio channels. Thereby, an adaptive method should be designed to fulfill IoT mutability requirements and to ensure the distribution of computational tasks to its nodes. In this paper such adaptive method is described. The method is based on Multi-Armed Bandit (MBA) algorithm belonging to Reinforcement Learning methods section. According to the method Environment is represented by IoT infrastructure and a main computational task distribution node, represented as an Agent, interacts with it. Implementation of MBA in a core of the developed method allows controlling continuous changing of IoT structure. Also, algorithms parameters allow generating different strategies of Agent interaction during tasks nodes distribution.Agent does not require a comprehensive description of an environment to take a decision. Instead, it evaluates previously taken actions and received reactions from the environment by actions to take a decision. Such approach allows avoiding complexity and heterogeneity of IoT structure. The realized approach could be used in software development process for IoT infrastructure.

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