ID 原文 译文
3393 针对 6G 时代电力物联网海量终端接入冲突严重、队列积压大、能量效率低等问题，提出了一种基于上下文学习的接入控制算法。 In view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the mas-sive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-basedaccess control (CLAC) algorithm was proposed.
3394 所提算法基于强化学习和快速上行链路授权技术，考虑终端活跃与休眠 2 种状态，优化目标为在终端接入服务质量需求的长期约束下最大化网络总能量效率。 The proposed algorithm was based on reinforcement learning and fastuplink grant technology, considering active state and dormant state of terminals, and the optimization objective was tomaximize the total network energy efficiency under the long-term constraint of terminal access service quality require-ments.
3395 利用李雅普诺夫优化对长期优化目标与约束进行解耦，将长期优化问题转化为单时隙独立的确定性子问题，并利用基于终端状态感知的上置信界算法进行求解。 Lyapunov optimization was used to decouple the long-term optimization objective and constraint, and thelong-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems,which could be solved by the terminal state-aware upper confidence bound algorithm.
3396 仿真结果表明，所提算法能够在满足终端接入服务质量需求的同时，有效提高网络总能量效率。 The simulation results show thatCLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.
3397 相较于传统快速上行链路授权算法，所提算法可提高平均能量效率 48.11%，提高满足接入服务质量需求的终端比例54.95%，降低平均队列积压 83.83%。 Com-pared with the traditional fast uplink grant, CLAC can improve the average energy efficiency by 48.11%, increase the proportion of terminals meeting access service quality requirements by 54.95%, and reduce the average queue backlog by83.83%.
3398 针对基于密文策略的属性加密（CP-ABE）在低时延需求较高的雾计算环境中，存在加解密开销大、属性更新效率低的问题，提出了一种雾计算中细粒度属性更新的外包计算访问控制方案， To slove the problem that in the fog computing environment with comparatively high low latency demand, ci-phertext policy attribute based encryption (CP-ABE) faced the problems of high encryption and decryption overhead andlow efficiency of attribute update, an fine-grained attribute update and outsourcing computing access control scheme infog computing was proposed.
3399 使用模加法一致性秘密（密钥）分享技术构建访问控制树，将加解密计算操作外包给雾节点，降低用户加解密开销； The unanimous consent control by modular addition technique was used to construct anaccess control tree, and the computing operations of ecryption and decryption were outsourced to fog nodes in order toreduce user encryption and decryption overhead.
3400 结合重加密机制，在雾节点建立组密钥二叉树对密文进行重加密，实现对用户属性的灵活更新。 Combined with the re-encryption mechanism, a group key binary treewas established at the fog node to re-encrypt the ciphertext so that user attribute can be updated flexibly.
3401 安全性分析表明，所提方案在决策双线性 Diffie-Hellman 假设下是安全的。 The securityanalysis shows that the proposed scheme is safe under the decision bilinear Diffie-Hellman hypothesis.
3402 仿真实验结果表明，所提方案中用户加解密时间开销相比其他方案更小，属性更新效率更高。 Compared withother schemes, the results of simulation experiment prove that the time cost of user encryption and decryption in thisscheme is lower and the efficiency of attribute update is higher.