Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They co...Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.展开更多
Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs anoth...Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs another vehicles as performers for workload processing.An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making,which avoids the information collection from the performers and preserves their privacy.We model the interactions among the requester(leader)and multiple performers(followers)as a Stackelberg game.The requester incentivizes the performers to accept the workloads.We derive the Stackelberg equilibrium under complete information.Furthermore,deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’information private.Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others.Finally,numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.展开更多
基金supported in part by the National Natural Science Foundation of China(61971148)the Science and Technology Program of Guangdong Province(2015B010129001)+2 种基金the Natural Science Foundation of Guangxi Province(2018GXNSFDA281013)the Foundation for Science and Technology Project of Guilin City(20190214-3)the Key Science and Technology Project of Guangxi(AA18242021)
文摘Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.
基金supported in part by National Key R&D Program of China under Grant 2020YFB1807802in part by National Natural Science Foundation of China under Grants 62001125 and 61971148+4 种基金in part by FDCT-MOST Joint Project under Grant 0066/2019/AMJin part by Science and Technology Development Fund of Macao SAR under Grant 0162/2019/A3in part by FDCT SKL-IOTSC(UM)2021-2023in part by Research Grant of University of Macao under Grant MYRG2020-00107-IOTSCin part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011287
文摘Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs another vehicles as performers for workload processing.An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making,which avoids the information collection from the performers and preserves their privacy.We model the interactions among the requester(leader)and multiple performers(followers)as a Stackelberg game.The requester incentivizes the performers to accept the workloads.We derive the Stackelberg equilibrium under complete information.Furthermore,deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’information private.Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others.Finally,numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.
