1,165 research outputs found
Fiber-wireless convergence in next-generation communication networks: systems, architectures, and management
No full textThis book investigates new enabling technologies for Fi-Wi convergence. The editors discuss Fi-Wi technologies at the three major network levels involved in the path towards convergence: system level, network architecture level, and network management level. The main topics will be: a. At system level: Radio over Fiber (digitalized vs. analogic, standardization, E-band and beyond) and 5G wireless technologies; b. Network architecture level: NGPON, WDM-PON, BBU Hotelling, Cloud Radio Access Networks (C-RANs), HetNets. c. Network management level: SDN for convergence, Next-generation Point-of-Presence, Wi-Fi LTE Handover, Cooperative MultiPoint. • Addresses the Fi-Wi convergence issues at three different levels, namely at the system level, network architecture level, and network management level • Provides approaches in communication systems, network architecture, and management that are expected to steer the evolution towards fiber-wireless convergence • Contributions from leading experts in the field of Fiber-Wireless Convergence
A Cost-Efficient and Reliable Resource Allocation Model Based on Cellular Automaton Entropy for Cloud Project Scheduling
No full textResource allocation optimization is a typical cloud project scheduling problem: a problem that limits a cloud system’s ability to execute and deliver a project as originally planned. The entropy, as a measure of the degree of disorder in a system, is an indicator of a system’s tendency to progress out of order and into a chaotic condition, and it can thus serve to measure a cloud system’s reliability for project scheduling. In this paper, cellular automaton is used for modeling the complex cloud project scheduling system. Additionally, a method is presented to analysis the reliability of cloud scheduling system by measuring the average resource entropy (ARE). Furthermore, a new cost-efficient and reliable resource allocation (CERRA) model is proposed based on cellular automaton entropy to aid decision maker for planning projects on the cloud. At last, the proposed model is designed using Matlab toolbox and simulated with three basic cloud scheduling algorithm, First Come First Served Algorithm (FCFS), Min-Min Algorithm and Max-Min Algorithm. The simulation results show that the proposed model can lead to achieve a cost-efficient and reliable resource allocation strategy for running projects on the cloud environment
Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty
No full textIn this chapter, novel atack‐aware routing and wavelength assignment (Aa‐RWA) algo‐
rithms for multiperiod network planning are proposed. The considered physical layer
atacks addressed in this chapter are high‐power jamming atacks. These atacks are
modeled as interactions among lightpaths as a result of intra‐channel and/or inter‐chan‐
nel crosstalk. The proposed Aa‐RWA algorithm frst solves the problem for given trafc
demands, and subsequently, the algorithm is enhanced in order to deal with demands
under uncertainties. The demand uncertainty is considered in order to provide a solu‐
tion for several periods, where the knowledge of demands for future periods can only be
estimated. The objective of the Aa‐RWA algorithm is to minimize the impact of possible
physical layer atacks and at the same time minimize the investment cost (in terms of
switching equipment deployed) during the network planning phase.Konstantinos Manousakis, Panayiotis Kolios and Georgios Ellinas (2017). Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty, Optical Fiber and Wireless Communications, PhD. Rastislav Róka (Ed.), InTech, DOI: 10.5772/intechopen.68491
Data-Driven Bandwidth Allocation in EONs
No full textWe investigate periodically and dynamically reconfiguring
elastic optical networks (EONs) utilizing predictive
bandwidth allocation models found by applying reinforcement
learning. These models aim at efficiently utilizing the network
resources so that the quality-of-service (QoS) requirements are
met, in networks where the traffic is evolving in an uncertain
way.© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
T. Panayiotou, G. Ellinas, "Data-Driven Bandwidth Allocation in EONs" 2018 IEEE Photonics in Switching and Computing (PSC) , September 201
Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors
No full textIn pursuit of autonomous vehicles, achieving human- like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves: (1) extracting data from the highD natural driving study, categorizing it into three driving styles using a rule-based classifier; (2) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; (3) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for human-like driving across styles
Addressing Traffic Prediction Uncertainty in Multi-Period Planning Optical Networks
No full textDeep-quantile regression is leveraged to capture traffic prediction uncertainty
over future network planning intervals. We show that quantile predictions, acting as discriminative
margins, result to significant spectrum savings compared to empirically estimated
myopic margins considered.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
T. Panayiotou, G. Ellinas, "Addressing Traffic Prediction Uncertainty in Multi-Period Planning Optical Networks" 2022 IEEE Optical Fiber Communications Conference and Exhibition (OFC), March 202
Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance At Urban Intersections
No full textIntersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi- access Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles’ trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty- aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted
Welcome note
No full textPresents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record
Edge Learning of Vehicular Trajectories at Regulated Intersections
No full textTrajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology
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