Blockchain-based shared manufacturing in support of cyber physical systems: concept,framework, and operation

Shared Manufacturing (SharedMfg), a Peer-to-Peer (P2P)-based resource sharing paradigm boosted by the wide-spread of sharing economy, servitization and Internet of things, tends to massively extend the scope of resource sharing in both vertical and horizontal directions, and as a consequence, it amplifies a credibility gap in the manufacturing area. To respond to this problem, and meanwhile, promoting the SharedMfg, blockchain is attempted to integrate into the SharedMfg. Hence, this paper proposes the Blockchain-based SharedMfg (BSM) framework in support of the application of Cyber Physical Systems (CPS). At the same time, Resource Operation Blockchain (ROB) is constructed for the core operation of BSM framework, which carries out on the basis of a consensus mechanism (i.e., Proof-of-Participation) and a Smart Contract Network (SCN), to facilitate the P2P-based resource sharing paradigm. A prototype system is implemented by the Ethereum framework together with discussions to validate the feasibility of ROB. In brief, BSM framework complementarily combines the blockchain and SharedMfg, beneficial to promote both modes.     

车联网D2D通信中最大化频谱资源利用率分配算法

无线通信技术快速发展,终端设备不断增多,为缓解这一现象,提升系统网络容量,针对车联网蜂窝D2D(device to device)通信资源分配问题,提出了一种最大化频谱资源利用率分配算法.该算法以最大化频谱资源利用率为优化目标,在满足车联网通信的基本服务质量(quality of service,QoS)下,通过V2V(vehicle to vehi-cle)和V2P(vehicle to people)共享信道资源来提高频谱资源利用率.首先利用信道状态信息定义的链路增益因子为终端用户找到潜在的通信链路集合;然后证明终端用户复用链路资源时功率分配问题为一个凸优化问题,利用凸优化理论求得最优传输功率;随后求解最优的信道匹配问题,此问题为多对一的加权匹配问题,为降低算法复杂度用KM(Kuhn Munkres)算法来求解.仿真结果表明,所提算法较其他算法能够有效地提升系统吞吐量、提高频谱资源利用率、提升网络性能,优化车联网通信资源分配问题.     

基于网格的CSCL资源共享平台的研究

资源共享一直是协作学习研究的一个热点.本文针对当前协作学习系统在资源共享及管理方面存在的问题,运用网格计算技术搭建一个平台,实现资源的有效共享及统一管理,消除资源孤岛,优化资源调度.提出了系统的结构模型,讨论了其访问模型,并实现了网格管理器的四种网格服务.     

网格环境中信任-激励相容的资源分配机制

在网格环境中,多数节点希望能够更多地使用其他节点的资源,而不愿共享本地的资源.每个理性的用户在追求自身利益最大化的同时,严重影响了协作整体的运行效率.在实际应用中,要吸引资源的拥有者加入资源协作,就必须保证其利益和安全.讨论了资源激励机制和分配机制之间的依赖与制约关系,从经济、信任角度提出自适应的信任-激励相容的资源分配机制.在网格环境中,资源供需的变化会引发价格的浮动,基于经济学的一般均衡理论,给出了资源提供者的动态价格调整策略.提供者可根据当前资源的供需和负载状况,制定自适应的信任-激励相容的分配策略ATIM(adaptive trust-incentive compatible resource allocation),遵循多贡献多分配的原则,激励节点共享出有价值的资源,最大化资源的聚合效用.在CROWN(China research and development environment over wider-area network)平台中评估了ATIM机制的效率和性能.     

制造商竞争下创新投资对零售商信息分享策略的影响

研究在由两个竞争制造商和一个共同零售商组成的二级供应链中,制造商创新投资对零售商信息分享策略的影响.首先构建3种信息分享策略(不分享、部分分享和均分享)下的博弈模型,并对比分析得出零售商最优信息分享策略及不同策略下制造商/供应链利润大小关系;然后,探讨竞争制造商创新投资效率不等和存在横向溢出效应情形下零售商的信息分享策略.研究结果表明:当制造商投资效率相等时,若投资效率较低,则零售商没有动机分享预测信息;若竞争强度较小且投资效率较高,或者竞争强度较大且投资效率适中,则零售商总是倾向于均分享策略;若竞争强度较大且投资效率较高,则零售商选择部分分享策略.当制造商投资效率不等时,零售商更愿意与投资效率较高的制造商分享信息.此外,横向溢出效应更能激励零售商分享信息.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

J2EE平台上基于Web Services的数据集成研究

数据集成是共享分布的异构数据资源的核心问题.在分析常用数据集成技术的基础上,结合Web Services技术和组件技术,提出了基于Web Services和组件技术实现Mediated系统的数据集成方案.最后,以J2EE为平台给出了一种面向服务的低偶合的数据集成框架,中介器和包装器由EJB组件实现,由组件部署的Web服务为用户提供一个透明的统一的接口,实现异地异构数据资源的共享和整合.     

On the Application of Game-Theoretic Mechanism Design for Resource Allocation in Multimedia Systems

In this paper, we study the system-level computational resource allocation problem among multiple multimedia tasks. We consider the multimedia tasks to be autonomous, i.e., they are selfish and behave strategically. We propose a resource allocation framework based on mechanism design to prevent the tasks from behaving strategically and manipulating the available system resources. We apply two mechanisms in the framework and assess their advantages over proportional-share resource allocation algorithms, which are often used in multimedia systems. We show in the simulations that the incorporation of mechanism design for system resource allocation is a promising solution that achieves efficient, fair and robust allocation against manipulation from strategic applications.      

网格环境下的电子病历整合设计

不同电子病历在数据上的异构性阻碍了信息的共享,本文采用基于OSGA-DAI的中间件法解决异构医疗数据的集成问题。OGSA-DAI通过Web Service的方式对数据源进行服务化的封装,屏蔽了数据资源在存储管理上的异构。对于数据资源在信息表示上的异构性,则采用信息映射的方法,将原数据库中的数据映射到一张虚拟的表中,使用户不必了解原数据资源的表示信息即可查询获取数据。     

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.     

异构网络资源分配：改进多对一转移匹配

随着无线业务的急剧增长,短缺的频谱资源正面临着巨大挑战。采用无线异构网络被视作解决此问题,提高频谱利用率的一种有效手段。但是,由于宏蜂窝和微蜂窝共享相同频谱资源,同层和跨层干扰非常严重,这时如何合理进行资源分配成了一个棘手的问题。针对该问题,根据匹配理论提出了一种改进多对一转移匹配算法进行资源分配。该算法在满足交换条件下,通过微蜂窝用户不断地交换其匹配资源,最终形成稳定转移匹配。仿真结果表明,所提改进转移匹配算法较传统转移匹配算法和改进Gale-Shapley匹配算法性能更易收敛到最优解,同时提高了频谱利用率,降低了计算复杂度。     

EasiSMP: A Resource-Oriented Programming Framework Supporting Runtime Propagation of RESTful Resources

In order to simplify programming for building sensor networks, macro-programming methods have been pro- posed in prior work. Most of them are designed for the dedicated networks and specific scenarios where devices are mostly homogeneous. Nevertheless the methods rarely consider those shared networks which are composed of heterogeneous de- vices, e.g., sensors, actuators, mobile devices, and share resources among themselves. In this paper, we present EasiSMP, a resource-oriented programming framework for these shared networks and generic application scenarios. In this framework, the devices and their functionalities are abstracted into RESTful virtual resources （VRs） each of which is labelled by a uni- form resource identifier （URI）. The post-deployment VR can be globally accessed and reused to propagate new resource（s） at runtime. To support the resource propagation, programming primitives are proposed and a virtual resource engine （VRE） is studied. To perform evaluation, EasiSMP is deployed into a relic monitoring network. Experimental results show that programming using Ea-siSMP is concise, and the average deployment overhead is decreased by up to 27% compared with the node-level programming.     

基于和声算法异构Hadoop集群资源分配优化

异构Map-Reduce环境中资源分配策略直接影响其响应时间,如何利用有效的策略将计算任务分配到计算资源是亟待解决的问题。利用和声搜索算法对异构Hadoop集群中的计算资源分配问题进行优化。对问题进行建模时考虑了异构计算机集群中各节点的处理能力、带宽和线路质量和源数据位置等因素对计算资源分配的影响,利用和声搜索算法优化资源分配策略,以期在满足用户需求的前提下提高系统的响应时间。并用Gridsim对算法进行仿真实验,实验结果表明利用和声搜索算法可以达到减少系统响应时间的目的。     

基于多对一Gale-Shapley算法的D2D通信资源分配

针对D2D通信复用异构蜂窝网络上行信道产生的干扰问题和频谱资源优化问题进行研究,提出一种基于多对一Gale-Shapley算法的D2D通信资源分配方案。本方案允许多个D2D用户共享一个蜂窝用户信道资源,通过设置信干噪比（SINR）门限保证用户的通信服务质量（QOS）。根据信道分配情况,构建D2D用户和信道的偏好列表,最大化系统总容量。仿真结果表明,该方案收敛较快,复杂度较低,能够有效保证用户的通信服务质量,系统总容量接近最优解。本研究为实现D2D用户和蜂窝用户的频谱资源共享,提高频谱利用率提供了一种有效方案。     

DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy

In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results. Instead of SDP dataset sharing, SDP model sharing is a potential solution to alleviate this problem and can encourage researchers in the research community and practitioners in the industrial community to share more models. However, directly sharing models may result in privacy disclosure, such as model inversion attack. To the best of our knowledge, we are the first to apply differential privacy (DP) to privacy-preserving SDP model sharing and then propose a novel method DP-Share, since DP mechanisms can prevent this attack when the privacy budget is carefully selected. In particular, DP-Share first performs data preprocessing for the dataset, such as over-sampling for minority instances (i.e., defective modules) and conducting discretization for continuous features to optimize privacy budget allocation. Then, it uses a novel sampling strategy to create a set of training sets. Finally it constructs decision trees based on these training sets and these decision trees can form a random forest (i.e., model). The last phase of DP-Share uses Laplace and exponential mechanisms to satisfy the requirements of DP. In our empirical studies, we choose nine experimental subjects from real software projects. Then, we use AUC (area under ROC curve) as the performance measure and holdout as our model validation technique. After privacy and utility analysis, we find that DP-Share can achieve better performance than a baseline method DF-Enhance in most cases when using the same privacy budget. Moreover, we also provide guidelines to effectively use our proposed method. Our work attempts to fill the research gap in terms of differential privacy for SDP, which can encourage researchers and practitioners to share more SDP models and then effectively advance the state of the art of SDP.     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

WSN中层次型拓扑控制与网络资源配置联合设计方法

综合考虑异构无线传感器网络中节点速率分配、簇的划分规则和链路层网络频带资源占用情况, 提出一种基于拓扑控制与资源优化分配的层次型路由算法. 在网络层, 该算法根据成员节点和簇首节点的速率分配机制建立节点流量平衡模型. 在链路层, 分析无线传感器网络频谱共享行为, 研究邻近用户间访问冲突的规避抑制模型, 重构网络频带资源. 通过引入带宽比例因子将可用频带划分成若干子带, 提高网络频带资源的利用效率. 本文基于跨层联合设计思路, 建立一个混合整数非线性规划问题,对异构无线传感器网络中拓扑控制和网络资源分配问题联合设计, 得到最优的分簇结果和资源分配方案. 最后, 在设定网络拓扑中评估性能, 仿真结果证实该算法在网络频带资源充分利用的同时, 可实现最优的簇首匹配和路由建立结果.     

XDS技术框架下区域医疗信息共享的设计与实现

区域各医疗机构系统平台彼此异构并且相对独立,难以实现信息资源共享和交换,严重阻碍了区域医疗一体化的发展,造成了严重的医疗信息资源浪费。通过设计一种基于IHE-XDS技术框架协议的区域信息集成平台,解决了各医院异构信息系统的集成问题,保证了信息的连续性、一致性和完整性,实现了流程协同和优化,可用来指导不同医疗服务提供方之间实现信息共享交换,并实现区域多医疗机构间的医疗信息共享及协同医疗。     

IaaS环境下多租户安全资源分配算法和安全服务调度框架

针对基础设施即服务（IaaS）环境下多租户使用安全服务时由于安全资源有限和安全资源分配不均导致的效率低下问题,提出了一个租户安全资源调度框架。首先以最小最大公平算法为基础,结合Fair Scheduler的调度思想为租户设定了最小共享量和资源需求量属性;然后通过安全服务资源分配算法在保证租户最小共享量满足的前提下,尽可能公平地满足租户的资源需求;最后结合租户内任务调度和租户间资源抢占算法,实现了租户安全服务调度框架。实验结果表明,在随机资源分配条件下,安全服务资源分配算法与传统资源分配算法相比在资源利用率和作业效率上均有明显提高,安全服务调度框架可以有效解决多租户安全资源的分配和强占问题。     

Sharing Political Content in Online Social Media: A Planned and Unplanned Behaviour Approach

Human’s decision making is not necessarily always planned; their unplanned behaviour—determined by natural personality traits—also contributes to the decision making process. In this study, we investigate factors related to planned and unplanned behaviour to understand why people share political content in online social media. Based on an online survey of 257 social media users, our results demonstrate that the factors representing both planned (i.e., perceived social recognition and altruistic motivation) and unplanned behaviour (i.e., extroversion and impulsiveness) affect people’s political content sharing behaviour. Our study understands that sharing political content is not like sharing other forms of content such as tourist attractions—the former can provoke serious punishment in some countries. Accordingly, trait impulsiveness is negatively associated with political content sharing behaviour. We also found that collective opinion moderates people’s planned behaviour, but not their unplanned behaviour. In other words, personality traits are unaffected by others’ opinions, but traits that humans can control can be shaped by others’.