A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

Lasso, Fabian Fernando Jurado; Marchegiani, Letizia; Jurado, J. F.; Mahfouz, Adnan Abu; Fafoutis, Xenofon

doi:10.36227/techrxiv.16777618.v1

Cited by 4 publications

(2 citation statements)

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“…Through the use of reinforcement learning, our solution seeks to strike a harmonious balance between minimizing control overhead and enhancing overall network efficiency. Readers interested in a comprehensive overview of reinforcement learning and its networking applications are referred to [24]- [27].…”

Section: Proposed Solution For Overhead Reductionmentioning

confidence: 99%

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ELISE: A Reinforcement Learning Framework to Optimize the Sloftframe Size of the TSCH Protocol in IoT Networks

Lasso¹,

Barzegaran²,

Jurado³

et al. 2023

Preprint

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<p>The Industrial Internet of Things (IIoT) is shaping the next generation of cyber-physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a Reinforcement Learning (RL) framework to optimize the slotframe size of the Time Slotted Channel Hopping (TSCH) protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user’s requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.</p>

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Section: Proposed Solution For Overhead Reductionmentioning

confidence: 99%

“…We cast the challenge of minimizing control overhead as a Markov Decision Process (MDP). The MDP is characterized by a tuple ⟨S, A, R⟩, where S denotes the set of states, A signifies the set of actions, and R encapsulates the reward function [28].…”

Section: Proposed Solution For Overhead Reductionmentioning

confidence: 99%