Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers...Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.展开更多
Individual countries are requested to submit nationally determined contributions(NDCs)to alleviate global warming in the Paris Agreement.However,the global climate effects and regional contributions are not explicitly...Individual countries are requested to submit nationally determined contributions(NDCs)to alleviate global warming in the Paris Agreement.However,the global climate effects and regional contributions are not explicitly considered in the countries’decision-making process.In this study,we evaluate the global temperature slowdown of the NDC scenario(ΔT=0.6°C)and attribute the global temperature slowdown to certain regions of the world with a compact earth system model.Considering reductions in CO_(2),CH_(4),N_(2)O,BC,and SO_(2),the R5OECD(the Organization for Economic Co-operation and Development in 1990)and R5ASIA(Asian countries)are the top two contributors to global warming mitigation,accounting for 39.3%and 36.8%,respectively.R5LAM(Latin America and the Caribbean)and R5MAF(the Middle East and Africa)followed behind,with contributions of 11.5%and 8.9%,respectively.The remaining 3.5%is attributed to R5REF(the Reforming Economies).Carbon Dioxide emission reduction is the decisive factor of regional contributions,but not the only one.Other greenhouse gases are also important,especially for R5MAF.The contribution of short-lived aerosols is small but significant,notably SO_(2)reduction in R5ASIA.We argue that additional species beyond CO_(2)need to be considered,including short-lived pollutants,when planning a route to mitigate climate change.It needs to be emphasized that there is still a gap to achieve the Paris Agreement 2-degree target with current NDC efforts,let alone the ambitious 1.5-degree target.All countries need to pursue stricter reduction policies for a more sustainable world.展开更多
基金supported in part by NSFC (62102099, U22A2054, 62101594)in part by the Pearl River Talent Recruitment Program (2021QN02S643)+9 种基金Guangzhou Basic Research Program (2023A04J1699)in part by the National Research Foundation, SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programmeMOE Tier 1 under Grant RG87/22in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165)in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)。
文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
基金funded by the undergraduate student research training program of the Ministry of Education, the National Natural Science Foundation of China (Grants Nos. 41771495, 41830641, and 41988101)the Second Tibetan Plateau Scientific Expedition and Research Program Grant 2019QZKK0208+1 种基金funded by the European Research Council Synergy project “Imbalance-P ” (Grant No. ERC-2013-Sy G-610028)the European Union’s Horizon 2020 research and innovation project “CONSTRAIN” (Grant No. 820829)
文摘Individual countries are requested to submit nationally determined contributions(NDCs)to alleviate global warming in the Paris Agreement.However,the global climate effects and regional contributions are not explicitly considered in the countries’decision-making process.In this study,we evaluate the global temperature slowdown of the NDC scenario(ΔT=0.6°C)and attribute the global temperature slowdown to certain regions of the world with a compact earth system model.Considering reductions in CO_(2),CH_(4),N_(2)O,BC,and SO_(2),the R5OECD(the Organization for Economic Co-operation and Development in 1990)and R5ASIA(Asian countries)are the top two contributors to global warming mitigation,accounting for 39.3%and 36.8%,respectively.R5LAM(Latin America and the Caribbean)and R5MAF(the Middle East and Africa)followed behind,with contributions of 11.5%and 8.9%,respectively.The remaining 3.5%is attributed to R5REF(the Reforming Economies).Carbon Dioxide emission reduction is the decisive factor of regional contributions,but not the only one.Other greenhouse gases are also important,especially for R5MAF.The contribution of short-lived aerosols is small but significant,notably SO_(2)reduction in R5ASIA.We argue that additional species beyond CO_(2)need to be considered,including short-lived pollutants,when planning a route to mitigate climate change.It needs to be emphasized that there is still a gap to achieve the Paris Agreement 2-degree target with current NDC efforts,let alone the ambitious 1.5-degree target.All countries need to pursue stricter reduction policies for a more sustainable world.
