Mobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. One challenge of federated learning in mobile edge computing is data heterogeneity since the unified model of federated learning performs poorly when client data are non-independent and identically distributed. Personalized federated learning can obtain amazing models in scenarios where client data are non-independent and identically distributed. This is because the personalized model captures the features of users' local data more accurately than the unified model. The personalized federated learning problem under two-tier (server-client) federated learning structures has been widely studied and applied. However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two learnable normalization parameters trained by clients to improve the performance of personalized models. Furthermore, in order to facilitate the collaboration among edge servers, we propose a similarity aggregation method to assign aggregation weights based on the Tanimoto coefficients between models. The experimental results show that the proposed algorithm not only increases the convergence speed of personalized models but also improves their testing accuracy.

As one of the essential steps to secure government data sharing, Identity Authentication (IA) plays a vital role in the processing of large data. However, the centralized IA scheme based on a trusted third party presents problems of information leakage and single point of failure, and those related to key escrow. Therefore, herein, an effective IA model based on multiattribute centers is designed. First, a private key of each attribute of a data requester is generated by the attribute authorization center. After obtaining the private key of attribute, the data requester generates a personal private key. Second, a dynamic key generation algorithm is proposed, which combines blockchain and smart contracts to periodically update the key of a data requester to prevent theft by external attackers, ensure the traceability of IA, and reduce the risk of privacy leakage. Third, the combination of blockchain and interplanetary file systems is used to store attribute field information of the data requester to further reduce the cost of blockchain information storage and improve the effectiveness of information storage. Experimental results show that the proposed model ensures the privacy and security of identity information and outperforms similar authentication models in terms of computational and communication costs.

Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV. © 2023, The Author(s).

Public clouds have drawn increasing attention from academia and industry due to their high computational and storage performance. Attribute-based encryption (ABE) is the most promising technology to simultaneously achieve confidentiality and fine-grained access control of the cloud-stored data. However, traditional ABE that relies on centralized authority faces several key management issues, such as the key escrow, key distribution, key tracking, key update, and heavy communication and computing overhead for users, which will cause security concerns and impede its widespread application. On the other hand, blockchain technology preserves distributed ledgers to ensure the immutability and transparency of data, which can further solve the security vulnerabilities caused by system centralization. This paper proposes a blockchain-assisted transformation method to solve all the key management problems mentioned above in ciphertext-policy ABE by utilizing technologies such as secret sharing protocols. In addition, our transformation method realizes two additional benefits: outsourced decryption and efficient user revocation, which are extremely valuable for practical implementations. We simulate a demonstration by adopting the most popular permissioned blockchain, Hyperledger Fabric. The security and efficiency analysis reveals that the scheme obtained from our transformation method can achieve replayable chosen-ciphertext security with extremely efficient decryption.

Bitcoin uses a decentralized network of miners and a distributed consensus algorithm to agree on blockchains to process transactions, and designs certain incentive strategy to ensure the system run persistently. However, recent research finds that it is vulnerable to specific game-theoretic attacks, in which a rational attack can gain a disproportionate share of reward by deviating from the honest behaviors. Among these attacks, stubborn mining is generally regarded as the most effective one. This paper propose the optimal stubborn mining strategy, trying to obtain the maximum revenue in stubborn mining. Through careful analysis, we find that the mining strategies in stubborn mining, under different conditions, can be represented as a Markovian Decision Process (MDP), and solving the MDP can result in the optimal strategy. In solving the MDP, we first transform it into a transition-reward Matrices, and then evaluate it to get the largest reward. With the method mentioned above, the attackers can get 46.9% additional than honest miners, and this attack outweighs traditional selfish mining (a compelling and well-studied mining attack) by up to 7.53%. © 2023 Elsevier Inc.

Federated learning is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data. As an important variant, vertical federated learning (VFL) deals with cases in which collaborating organizations own data of the same set of users but with disjoint features. It is generally regarded that VFL is more secure than horizontal federated learning. However, recent research (USENIX Security'22) reveals that it is still possible to conduct label inference attacks in VFL, in which attacker can acquire privately owned labels of other participants; even VFL constructed with model splitting (the kind of VFL architecture with higher security guarantee) cannot escape it. To solve this issue, in this paper, we propose the dispersed training framework. It utilizes secret sharing to break the correlations between the bottom model and the training data. Accordingly, even if the attacker receives the gradients in the training phase, he is incapable to deduce the feature representation of labels from the bottom model. Besides, we design a customized model aggregation method such that the shared model can be privately combined, and the linearity of secret sharing schemes ensures the training accuracy to be preserved. Theoretical and experimental analyses indicate the satisfactory performance and effectiveness of our framework.

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model. They allocate different transmission slots to conflict-ing packets and overcome the challenges caused by the fact that the channel state changes quickly and is un-controllable. The first algorithm proposes a priority -based framework for packet scheduling in recharge-able sensor networks. Every packet is assigned a pri-ority related to the transmission delay and the remain-ing energy of rechargeable batteries, and the packets with higher priority are scheduled first. The second algorithm mainly focuses on the energy efficiency of batteries. The priorities are related to the transmis-sion distance of packets, and the packets with short transmission distance are scheduled first. The sensors are equipped with low-capacity rechargeable batteries, and the harvest-store-use model is used. We consider imperfect batteries. That is, the battery capacity is lim-ited, and battery energy leaks over time. The energy harvesting rate, energy retention rate and transmission power are known. Extensive simulation results indi-cate that the battery capacity has little effect on the packet scheduling delay. Therefore, the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.

无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR （signal to interference plus noise ratio）模型准确地描述了无线信号传播的固有特性,能够真实反映无线信号之间的干扰,提出了一种在动态无线网络中基于SINR模型的常数近似因子的在线分布式链路调度算法（OLD＿LS）.在线的意思是指,在算法执行的过程中任意节点可以随时加入网络,也可以随时离开网络.节点任意加入网络或者从网络中离开体现了无线网络的动态变化的特性. OLD＿LS算法把网络区域划分为多个正六边形,局部化SINR模型的全局干扰.设计了动态网络下的领导者选举算法（LE）,只要网络节点的动态变化速率小于1/ε, LE就可以在O （logn+logR）时间复杂度内以高概率选举出领导者.其中,常数ε满足ε≤5(1-21-α/2)/6,α表示路径损耗指数, n是网络节点的规模, R是最长链路的长度.根据文献调研,所提算法是第1个用于动态无线网络的在线分布式链路调度算法.

物联网（Internet of Things,IoT）中有效的链路调度可以提高节点能量负载均衡、降低信息传输时延,其包含两个子问题:（1）最大化链路调度（Maximization Link Scheduling,MLS）,即最大化单时隙内并发链路的数量;（2）最短链路调度（Shortest Link Scheduling,SLS）,即最小化每条链路至少成功传输一次的时隙数量。Signal-Interference-Noise Ratio（SINR）模型是研究链路调度问题常见的干扰模型,Rayleigh衰落模型比SINR模型更加真实,但由于接收信号强度的不确定性,理论上,Rayleigh衰落模型下链路成功传输的概率达不到1,因此允许链路以很小的概率传输失败。于是当链路成功传输的概率大于或等于1-ε时,则认为该链路被成功调度,其中ε记作可接受的传输失败概率。针对Rayleigh衰落模型下无线网络中链路调度问题,论文的主要工作和创新点如下:（1）首先分析流行的链路调度算法GHW,得到任意两条成功链路之间的最短距离,使用该距离实现全局干扰局部化。然后利用干扰局部化思想设计集中式Distance-based Link Scheduling（DLS）算法和分布式 DLS（Distributed DLS,DDLS）算法解决 MLS 问题,重复执行DDLS解决SLS问题,并给出O（nlnn）近似因子,其中n是链路数量。最后,与当前流行的链路调度算法比较,仿真验证所设计算法提高了其调度性能。（2）协议干扰模型（protocol interference model,PIM）是一种常见的干扰局部化模型,但由于其简单性,该模型通常得不到准确结论。通过建立PIM的干扰半径与Rayleigh衰落模型下链路成功传输概率1-ε之间的关系,得到Rayleigh衰落模型下应用PIM获得1-ε成功传输概率的充分条件,于是在保证其准确性的同时实现干扰局部化。利用该思想设计集中式和分布式算法解决MLS问题,仿真结果表明所设计算法提高了 GHW的调度性能。（3）利用节点传输范围与链路长度之间的关系实现干扰局部化,并证明传输范围之外的累加干扰有上界且不会对链路成功传输与否产生影响,在此基础上应用PIM分别设计分布式的异步和同步算法解决链路调度问题,从而实现高传输可靠性和低传输时延。然后,使用局部信息为每条链路的发送端设置发起传输的概率来设计分布式算法解决SLS问题。最后,仿真验证所设计算法可以从不同程度上提高传输可靠性与延迟性能。（4）分析传统链路多样性划分算法LDP,得到任意两条成功链路之间的最短距离,并实现干扰局部化,然后结合相继干扰消除技术（successive interference cancellation,SIC）和载波侦听机制,分别提出了改进的LDP算法和分布式Carrier Sensing based Distributed Recursive Link Elimination（CSDRLE）算法解决链路调度问题。最后,仿真结果表明CSDRLE算法的调度性能明显优于GHW和LDP算法的调度性能,并在某些场景下优于（3）中同步分布式链路调度算法的调度性能。

可见光（Visible Light Communication,VLC）通信作为无线通信的一个全新手段,引起了国内外研究者的广泛关注,是今后研究光无线通信的一个重要方向.基于发光二极管（LED）的可见光通信将照明和数据通信融合到应用中,能够实现高能效,高频谱效率以及相对较好的安全性和可靠性.而大规模多输入多输出（MIMO）更是具有提高可见光通信（VLC）系统传输速率的巨大潜力.然而,高通道间干扰（ICI）是严重影响传输性能的主要因素.在本文中,利用发射机处已知的信道状态信息（CSI）,通过分析MIMO-VLC的特殊特征,提出了可见光链路容忍集调度算法（LTS-MIMO）,并且对它们进行最优调度,以提高网络的吞吐量从而达到最大化网络性能的目的.