Sensors are being extensively deployed and are expected to expand at significant rates in the coming years. They typically generate a large volume of data on the internet of things (IoT) application areas like smart cities, intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog computing are potential and competitive strategies for collecting, processing, and distributing IoT data. However, cloud, edge, and fog-based solutions need to tackle the distribution of a high volume of IoT data efficiently through constrained and limited resource network infrastructures. This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm. The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe architecture to access massive and highly distributed IoT data. It is demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on SARSA enhances the IoT aggregator buffer occupation and network link utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing according to the Pub/Sub topic's priority and network constraint requirements.
The Internet of Things (IoT) is considered a major trend in computing and in specific areas such as Smart Cities, Smart Grid, Industry 4.0, and mobile applications based on 5G. Typically, this set of technologies requires the orchestration of heterogeneous resources that are allocated over distinct infrastructures such as Cloud Computing, Cloud of Things, Datacenters, and network backbones. Consistent with this demand, the PSIoT-Orch framework was designed to orchestrate massive IoT traffic and to allocate network resources between Aggregators and Consumers in a Publish / Subscribe strategy. This dissertation aims to build an intelligent module for PSIoT-Orch that is capable of handling data types with different transmission requirements, aiming at the efficient use of a limited communication link. The proposed component uses Reinforcement Learning, more specifically, the SARSA algorithm to dynamically adjust the available bandwidth according to transmission priority. This solution, named PSIoT-SARSA, is validated in a simulation environment under the statistical methods of Analysis of Variance and Response Surface Analysis and, at the end of the study, it is observed that it obtained promising results. The contributions are focused on gathering an approach that allows allocating bandwidth in an intelligent way, allowing efficient scheduling of the IoT flow, in the scenario of the Smart Grid.
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