Abstract
The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better.
Highlights
Since the emergence of the Internet of Things (IoT), the number of devices increases faster [1].As more devices create additional data, the new term big data was coined with three principal characteristics [2,3,4]: volume, velocity, and variety
In a world where various environments are moving toward automation and being smart and where the improvement speed of equipment and number of users are exponential, management issues become more complicated and vital
Reinforcement learning as a machine learning (ML) approach can be suitable and helpful because it can adjust the system in its variable environment without supervisory instructions that have time and economic costs and the possibility of human errors
Summary
Since the emergence of the Internet of Things (IoT), the number of devices increases faster [1].As more devices create additional data, the new term big data was coined with three principal characteristics [2,3,4]: volume, velocity, and variety. Since the emergence of the Internet of Things (IoT), the number of devices increases faster [1]. Forecast predicted that 26.3 billion such nodes would be connected to the Internet by 2020 [5]. As a result of these developments, network bandwidth and latency issues have become more significant. Data loss, and the subsequent wrong decisions caused by bandwidth limitations, it is not logical to transfer all data to the cloud when, at every moment, devices produce numerous data. Fog computing has emerged as a complement to the cloud to handle distributed processing, networking, and storage resources. Due to the proximity of fog devices to end users, they can adequately support applications with quality of service (QoS) and real-time constraints, especially in emergencies
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