π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Okic, Armin; Zanzi, Lanfranco; Sciancalepore, Vincenzo; Redondi, Alessandro E. C.; Costa‐Pérez, Xavier

doi:10.1109/infocom42981.2021.9488677

Cited by 12 publications

(9 citation statements)

References 33 publications

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“…In order to obtain a solution for the overall RAN deployment, the same problem should be solved for all the nodes in the network, therefore introducing scalability issues. Moreover, the centralization of all the necessary up-to-date monitoring information further exacerbates the complexity of this problem, which becomes impractical in real mobile networks characterized by thousands of RAN nodes [28].…”

Section: Related Workmentioning

confidence: 99%

“…This is due to the d-EPR algorithm, which favors the next-hop destination of each user to happen towards a nearby point of interest, or, in our settings, the closest base station location. The instantaneous traffic demand of each end-user is derived starting from the values reported in Sec.VI-A, weighted by a temporal factor to account for the traffic demand fluctuations typical of mobile network scenarios, as those presented for example by [28] and [55]. In the lower part of Fig.…”

Section: A Local Agent Performance Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Rezazadeh¹,

Zanzi²,

Devoti³

et al. 2023

IEEE Trans. Veh. Technol.

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Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.

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Section: Related Workmentioning

confidence: 99%

Section: A Local Agent Performance Assessmentmentioning

confidence: 99%

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Rezazadeh¹,

Zanzi²,

Devoti³

et al. 2023

IEEE Trans. Veh. Technol.

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“…However, it is not always efficient because not every slice is active all the time and uses its PRBs with 100% utilization although this strategy is good enough to have a dedicated network. This means that there can be some leftover PRBs that are not used by the corresponding slices [54], [55].…”

Section: Resource Allocation At Ran For An Emergency Slicementioning

confidence: 99%

Network resource allocation for emergency management based on closed-loop analysis

Blessed¹,

Aliyu²,

Agajo³

et al. 2022

ITU J-FET

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The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.

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“…ITS is the main part of smart cities that can aid in improving the employment of transportation infrastructure, enhance mobility for citizens, and decrease traffic congestion. Traffic forecasts offer experts a period to propose resource provisions to safeguard a safe journey [3]. By congestion, the worth of street networks is restricted.…”

Section: Introductionmentioning

confidence: 99%

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Alruban,

Mengash,

Eltahir

et al. 2024

IEEE Access

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Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.

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π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Cited by 12 publications

References 33 publications

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Network resource allocation for emergency management based on closed-loop analysis

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

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