INET Framework

Mészáros, Levente; Varga, A.; Kirsche, Michael

doi:10.1007/978-3-030-12842-5_2

Cited by 49 publications

(15 citation statements)

References 4 publications

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“…To simulate the wireless communication channel and protocol, the 5G simulator Simu5G 1.2.0 [ 32 ] was used, along with OMNeT++ 5.6.2 [ 36 ] and INET 4.2.2 [ 37 ]. Simu5G is an OMNeT++ library for the end-to-end performance evaluation of 5G networks.…”

Section: Testbed and Experimentsmentioning

confidence: 99%

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks

Guillén-Pérez

Cano

2022

Sensors

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The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5LA can reduce waiting time by more than 99% and time loss by more than 95%.

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Section: Testbed and Experimentsmentioning

confidence: 99%

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks

Guillén-Pérez

Cano

2022

Sensors

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“…Experiments were conducted on a C++ discrete event simulator called OMNeT++ [27], with the INET framework [28] to model wireless protocols and mobile networks. This environment allows simulation of unreliable communication channels and realistic layers of the OSI communication model.…”

Section: A Simulation Environmentmentioning

confidence: 99%

Centrality-Based Eventual Leader Election in Dynamic Networks

Favier¹,

Arantes²,

Lejeune³

et al. 2021

2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Thus, we have proposed a clustering algorithm that best fits the scaling of the sensors. Finally, the different contributions are simulated under OMNET++ coupled with the INET Framework [17]. In summary, our main contributions are structured around the following points:…”

Section: Introductionmentioning

confidence: 99%

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

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Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

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INET Framework

Cited by 49 publications

References 4 publications

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks

Centrality-Based Eventual Leader Election in Dynamic Networks

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

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