Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is t...Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .展开更多
Service providers usually require detailed statistics in order to improve their services.On the other hand,privacy concerns are intensifying and sensitive data is protected by legislation,such as GDPR(General Data Pro...Service providers usually require detailed statistics in order to improve their services.On the other hand,privacy concerns are intensifying and sensitive data is protected by legislation,such as GDPR(General Data Protection Regulation).In this paper,we present the design,implementation,and evaluation of a marketplace that allows“data consumers”to buy information from“data providers”,which can then be used for generating meaningful statistics.Additionally,our system enables“system operators”that can select which data providers are allowed to provide data,based on filtering criteria specified by the data consumer.We leverage local differential privacy to protect the data provider's privacy against data consumers,as well as against system operators,and we build a blockchain-based solution for ensuring fair exchange,and immutable data logs.Our design targets use cases that involve hundreds or even thousands of data providers.We prove the feasibility of our approach through a proof-of concept implementation of a measurement sharing application for smart-grid systems.展开更多
文摘Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .
基金supported by the EU funded Horizon 2020 project SOFIE(Secure Open Federation for Internet Everywhere),under grant agreement No.779984.
文摘Service providers usually require detailed statistics in order to improve their services.On the other hand,privacy concerns are intensifying and sensitive data is protected by legislation,such as GDPR(General Data Protection Regulation).In this paper,we present the design,implementation,and evaluation of a marketplace that allows“data consumers”to buy information from“data providers”,which can then be used for generating meaningful statistics.Additionally,our system enables“system operators”that can select which data providers are allowed to provide data,based on filtering criteria specified by the data consumer.We leverage local differential privacy to protect the data provider's privacy against data consumers,as well as against system operators,and we build a blockchain-based solution for ensuring fair exchange,and immutable data logs.Our design targets use cases that involve hundreds or even thousands of data providers.We prove the feasibility of our approach through a proof-of concept implementation of a measurement sharing application for smart-grid systems.
