2 research outputs found
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Task offloading optimization in Mobile Edge Computing based on Deep Reinforcement Learning
The Cloud Computing (CC) paradigm has risen in recent years as a solution to a need for computation and battery constrained User Equipment (UE) to run increasingly intensive computation tasks. Nevertheless, given the centralized nature of the CC paradigm, this option introduces significant network congestion problems and unpredictable communication delays unsuitable for real-time applications. In order to cope with these problems, the Mobile Edge Computing (MEC) concept has been introduced, which proposes to bring computation resources closer to the edge of the mobile networks in a distributed way. However, given that these edge computation resources are limited, this paradigm comes with its set of challenges that need to be solved in order to make it viable. This work proposes to innovate by presenting a network management agent capable of making offloading decisions from a heterogeneous network of UEs to a heterogeneous network of MEC servers. This agent is the orchestrator of a group of 5G Small Cells (SCeNBs), enhanced with computation and storage capabilities. In order to solve this high complexity problem, an Advantage Actor-Critic (A2C) agent is implemented and tested against several baselines. The proposed solution is shown to be at the baselines by making intelligent decisions taking into account computation, battery, delay and communication constraints ignored by the baselines. The solution is also shown to be scalable, data-efficient, robust, stable and adjustable to address not only the overall system performance but also to take into account the worst-case scenario.</p
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An intelligent intrusion detection system for 5G-enabled internet of vehicles
The deployment of 5G technology has drawn attention to different computer-based scenarios. It is useful in the context of Smart Cities, the Internet of Things (IoT), and Edge Computing, among other systems. With the high number of connected vehicles, providing network security solutions for the Internet of Vehicles (IoV) is not a trivial process due to its decentralized management structure and heterogeneous characteristics (e.g., connection time, and high-frequency changes in network topology due to high mobility, among others). Machine learning (ML) algorithms have the potential to extract patterns to cover security requirements better and to detect/classify malicious behavior in a network. Based on this, in this work we propose an Intrusion Detection System (IDS) for detecting Flooding attacks in vehicular scenarios. We also simulate 5G-enabled vehicular scenarios using the Network Simulator 3 (NS-3). We generate four datasets considering different numbers of nodes, attackers, and mobility patterns extracted from Simulation of Urban MObility (SUMO). Furthermore, our conducted tests show that the proposed IDS achieved an F1 score of 1.00 and 0.98 using decision trees and random forests, respectively, which means that it was able to properly classify the Flooding attack in the 5G vehicular environment considered