TY - JOUR
T1 - Blockchain-Based Layered Secure Edge Content Delivery in UAV-Assisted Vehicular Networks
AU - Xu, Qichao
AU - Jin, Jie
AU - Su, Zhou
AU - Li, Ruidong
AU - Wang, Yuntao
AU - Fang, Dongfeng
AU - Wu, Yuan
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - Recently, edge content delivery has been promoted for video applications in unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs). UAVs could proactively cache (video) contents and transmit them to nearby vehicular users, thereby significantly mitigating delivery latency. However, since UAVs are typically deployed by untrusted third parties, the contents may be illicitly accessed by some curious and even malicious UAVs, compromising users' privacy. Besides, due to the limited resources, UAVs may be unwilling to deliver contents without adequate compensations. To address these issues, in this paper, we propose a novel secure edge content delivery scheme in UVNs. Specifically, we first devise a blockchain-based layered secure edge content delivery framework. With the scalable video coding (SVC) that encodes each content into a base layer and multiple enhancement layers, only the enhancement layers of the content are delivered by UAVs, ensuring that untrusted UAVs without the base layer cannot recover the content to access explicit information. Meanwhile, a lightweight consortium blockchain is utilized to supervise the enhancement layer delivery services of UAVs in a distributed fashion. The delegated proof of stake (DPoS)-based practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to immutably and traceably record content delivery transactions between UAVs and vehicular users. Then, we formulate the content delivery incentive problem as a Stackelberg game, where UAVs act as game leaders to determine content delivery prices and vehicular users act as game followers to determine the number of required enhancement layers. Afterwards, through game analysis using the backward induction approach, the Stackelberg equilibrium is attained as the solution to the formulated problem, where the optimal strategies of both UAVs and vehicular users are derived by the Q-learning algorithm. Finally, extensive simulations are conducted to demonstrate that the proposed scheme can significantly enhance the security of delivered contents and efficiently motivate UAVs to cooperatively deliver contents.
AB - Recently, edge content delivery has been promoted for video applications in unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs). UAVs could proactively cache (video) contents and transmit them to nearby vehicular users, thereby significantly mitigating delivery latency. However, since UAVs are typically deployed by untrusted third parties, the contents may be illicitly accessed by some curious and even malicious UAVs, compromising users' privacy. Besides, due to the limited resources, UAVs may be unwilling to deliver contents without adequate compensations. To address these issues, in this paper, we propose a novel secure edge content delivery scheme in UVNs. Specifically, we first devise a blockchain-based layered secure edge content delivery framework. With the scalable video coding (SVC) that encodes each content into a base layer and multiple enhancement layers, only the enhancement layers of the content are delivered by UAVs, ensuring that untrusted UAVs without the base layer cannot recover the content to access explicit information. Meanwhile, a lightweight consortium blockchain is utilized to supervise the enhancement layer delivery services of UAVs in a distributed fashion. The delegated proof of stake (DPoS)-based practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to immutably and traceably record content delivery transactions between UAVs and vehicular users. Then, we formulate the content delivery incentive problem as a Stackelberg game, where UAVs act as game leaders to determine content delivery prices and vehicular users act as game followers to determine the number of required enhancement layers. Afterwards, through game analysis using the backward induction approach, the Stackelberg equilibrium is attained as the solution to the formulated problem, where the optimal strategies of both UAVs and vehicular users are derived by the Q-learning algorithm. Finally, extensive simulations are conducted to demonstrate that the proposed scheme can significantly enhance the security of delivered contents and efficiently motivate UAVs to cooperatively deliver contents.
KW - Stackelberg game
KW - Unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs)
KW - blockchain
KW - scalable video coding
KW - secure edge content delivery
UR - https://www.scopus.com/pages/publications/85211971412
U2 - 10.1109/TVT.2024.3505982
DO - 10.1109/TVT.2024.3505982
M3 - 文章
AN - SCOPUS:85211971412
SN - 0018-9545
VL - 74
SP - 7914
EP - 7927
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
ER -