ns3-ai

Yin, Hao; Liu, Pengyu; Liu, Keshu; Cao, Liu; Zhang, Lytianyang; Gao, Yayu; Hei, Xiaojun

doi:10.1145/3389400.3389404

Cited by 65 publications

(13 citation statements)

References 3 publications

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“…The two parts, i.e. the environment and the ACDQL algorithm, communicate through NS3-AI with the help of a shared memory [51]. In particular, at each time step, the environment puts the state of the environment into the shared memory based on which the ACDQL algorithm decides what the best movement for the UAV-BS is and puts it into the shared memory.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

Parvaresh

Kantarcı

2023

IEEE Open J. Commun. Soc.

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Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base stations, are considered to have promising potential to tackle the limitations of ground base stations. They can provide cost-effective Internet connection to es that are out of infrastructure. They can also take over quickly as service providers when ground base stations fail in an unanticipated manner. UAV-BSs benefit from their mobile nature that enables them to change their 3D locations if the demand profile changes rapidly. In order to effectively leverage the mobility of UAV-BSs so as to maximize the performance of the network, 3D location of UAV-BSs requires continuous optimization. However, solving the optimization problem of UAV-BSs is NP-hard with no deterministic solution in polynomial time. In this paper, we propose a continuous actor-critic deep reinforcement learning solution in order to solve the location optimization problem of UAV-BSs in the presence of mobile endpoints. The simulation results show that the proposed model significantly improves the network performance compared to Qlearning, deep Q-learning and conventional algorithms. While the Q-learning and deep Q-learning-based baselines reach the sum data rate of 35 Mbps and 42 Mbps respectively, our proposed ACDQL-based strategy maximizes the sum data rate of endpoints to 45 Mbps. Furthermore, the proposed ACDQLbased methodology reduces the convergence time of the UAV-BS placement optimization by 85 percent compared to the Q-learning and deep Q-learning baselines.

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Section: Simulation Resultsmentioning

confidence: 99%

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

Parvaresh

Kantarcı

2023

IEEE Open J. Commun. Soc.

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“…Third, related to challenges in implementing the use of AI in computer networks (Yin et al, 2020). In this study, there are several challenges faced in implementing the use of AI on computer networks, including related to the availability of sufficient data to train AI systems, limited ability of AI systems to recognize new types of malware, and dependence on technology that continues to grow and requires considerable costs for development and maintenance.…”

Section: Discussionmentioning

confidence: 99%

Exploring The Effectiveness of Artificial Intelligence in Detecting Malware and Improving Cybersecurity in Computer Networks

Komarudin¹,

Maulani

Herdianto³

et al. 2023

Eduvest

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Malware, in particular, has been identified as a major cy-bersecurity challenge due to its ability to infiltrate computer networks, steal sensi-tive data, and cause major damage to computer systems. The purpose of this study was to explore the effectiveness of artificial in-telligence in detecting malware and improving cybersecurity in computer net-works. Success rate in detecting and preventing malware attacks on computer networks using AI-based methods. The time it takes to detect and prevent malware attacks on computer net-works using AI-based cyber protection methods. Furthermore, the selection of two types of malware that are often found on computer networks, namely Trojans and Worms, and data sampling was then test-ed on a simulation system. In this study, three different AI techniques were applied, namely Support Vector Machine, Neural Network , and Decision Tree to detect malware on computer networks.

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“…Moreover, connecting these two frameworks with data interaction is more reasonable and convenient. Therefore, the ns3-AI framework was proposed in [ 243 , 244 ]. The ns3-AI framework provides an efficient workflow between ns-3 and Python-based modules, enabling seamless data transfer and interaction between the two modules.…”

Section: Ns-3-based ML Frameworkmentioning

confidence: 99%

“…The ns3-AI framework provides an efficient workflow between ns-3 and Python-based modules, enabling seamless data transfer and interaction between the two modules. As an example, using the ns-3-AI framework: (a) LSTM has been utilized to predict the channel quality, and (b) RL method for controlling the congestion occurring in TCP [ 244 ].…”

Section: Ns-3-based ML Frameworkmentioning

confidence: 99%

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Farhad

Pyun

2023

Sensors

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The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

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ns3-ai

Cited by 65 publications

References 3 publications

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

Exploring The Effectiveness of Artificial Intelligence in Detecting Malware and Improving Cybersecurity in Computer Networks

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

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