基于拍卖的物联网搜索数据提供机制

不同于互联网搜索采用爬虫等技术来采集数据,物联网搜索获取数据的主要方式之一是数据拥有者主动提供数据,然而自私性及隐私保护等原因导致数据拥有者不愿主动提供数据。这使得如何有效激励数据拥有者提供数据成为促进物联网搜索发展的关键问题之一。由于竞价拍卖能有效激励参与合作,因此将竞价博弈引入到物联网搜索中,为物联网搜索中的数据采集提供方法。具体地,提出了面向性价比最优的多属性逆向拍卖激励机制,证明了最优数据质量独立于自身报价、其他竞拍者的数据质量及其报价。     

基于 Ontology的个性化元搜索引擎研究

文中提出了一种新的基于Ontology的个性化元搜索引擎系统模型，目的是解决现有搜索引擎很难考虑用户兴趣来实现用户个性化搜索问题。提出了基于Ontology的用户描述文件的表达方法和自动隐式学习算法，以及基于Ontology查询结果合成算法。全面描述了系统三层结构以及用户个性化搜索处理过程，并给出了对系统的评价标准。     

基于用户兴趣的元搜索引擎检索结果合成技术

元搜索引擎综合了多个搜索引擎的搜索结果,提高了搜索的覆盖率,但是它们返回的结果往往数目庞大,并且很多结果与用户查询并不相关。为了提高元搜索引擎的查询精度,文章提出了一种基于用户兴趣的元搜索引擎检索结果合成技术。该技术先对检索结果进行去重、消除死链接．然后根据基于用户兴趣的检索结果优劣比排序算法对结果进行排序,为用户提供贴切的查询结果。该技术能提高用户的检索效率和查询质量。     

物联网中基于相似性计算的传感器搜索

物联网逐渐成为学术界研究的热点领域,无处不在的传感器设备促进了传感器搜索服务的产生。物联网中搜索的强时空性、海量数据的异构性与传感器节点的资源受限性,给物联网搜索引擎高效地查询传感器提出了挑战。该文提出基于传感器定量数值的线性分段拟合相似性(PLSS)搜索算法。PLSS算法通过分段和线性拟合的方法,构建传感器定量数值的相似性计算模型,从而计算传感器的相似度,根据相似度查找最相似的传感器集群。与模糊集(FUZZY)算法和最小二乘法相比,PLSS算法平均查询精度和查询效率较高。与原数据相比,PLSS算法的存储开销至少降低了两个数量级。     

元搜索引擎中基于OWA算子的查询结果合成研究

元搜索引擎综合了多个搜索引擎的搜索结果,提高了搜索的覆盖率,但搜索的结果往往数目庞大,并且很多结果与用户查询并不相关,这直接影响了用户检索的质量,也提高了检索的代价。针对这一问题,提出了基于OWA算子的结果合成算法。它基于用户偏好来获得成员引擎执行某个特定类别查询的性能,采用页面反馈度来给每一个结果页面提供一个基于内容的评分,最后采用乐观OWA算子作为权重函数对结果进行了整合。实验表明,该算法提高了用户的检索效率和质量。     

一种基于概念抽取的元搜索引擎

文章在概念抽取算法基础上，提出一种基于概念的元搜索引擎体系结构。这种体系结构提供统一的用户界面．通过查询适配可以同时使用多个独立搜索引擎的高级搜索功能。并且通过概念抽取对搜索结果进一步分析。使其更为准确和详细。系统提供了概念图这种独特的结果显示方式，引导用户细化搜索范围以得到更为精确的结果。     

元搜索引擎中基于用户兴趣的查询结果合成研究

元搜索引擎综合了多个搜索引擎的搜索结果，提高了搜索的覆盖率，但是它们返回的结果往往数目庞大，并且很多结果与用户查询并不相关，这直接影响了用户检索的质量，也大大提高了用户检索的代价。针对这一问题。文章提出了基于用户兴趣的结果合成算法。它根据用户兴趣对结果进行相似度计算和结果去重，从而为用户提供贴切的查询结果。实验表明。该算法提高了用户的检索效率和质量。     

主题搜索引擎中专业网页索引集构造算法的研究

针对特定主题的搜索引擎构建问题,给出了一种主题搜索引擎的体系结构,并在此基础上,提出了面向主题的专业网页索引集构造算法.模拟实验表明,以该算法为基础的主题搜索引擎能使用户搜索到该主题的权威页和中心页,可以有效地提高搜索引擎的查准率.     

一种基于用户路径模型的搜索引擎检索性能度量方法

 搜索引擎是信息时代人们获取所需信息的重要手段,搜索引擎性能度量方法也成为了一个重要的研究课题.本文分析了国内外搜索引擎检索性能度量的研究状况,提出了一种基于用户路径模型的性能度量方法.从用户搜索行为的角度研究了用户行为模型,通过抽象用户行为模型到路径模型的映射,给出基于用户路径模型的搜索引擎检索性能的评估步骤,针对度量数据给出了成功搜索度量方法,并通过度量平台的实现验证了该度量方法的实践意义.     

基于Nutch的网页排序算法研究

网页排序算法对根据用户查询词搜索到的大量页面进行排序,从而返回给用户,因此排序算法对搜索引擎的好坏起着关键作用。Nutch搜索引擎只实现了基本的综合排序模型,针对Nutch默认排序算法的不足,在Page Rank算法中加入时间因子、链接权重因子,并结合How Net来计算网页的语义相似度,将改进后的Page Rank算法和基于语义的主题相关度算法应用在Nutch排序算法中。实验结果表明:改进的排序算法使得Nutch的搜索结果排序准确率和首页命中率都有了明显提升。     

Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

Massive, diverse, and high-frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness-constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency-aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster-usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users.     

Cooperative content downloading scheme based on D2D multicast communications

Aiming at the content downloading scenario in the network such as cellular network and vehicular network,a cooperative content downloading scheme based on device-to-device multicast communications was proposed to improve the content downloading performance.Such a scheme took advantage of the proximity feature of the content requesters and groups them into disjoint clusters.Firstly,the base station delivered the entire content to a cluster,and each content requester in a cluster downloaded a proportion of the entire content.Then,the content requesters took turns sharing their downloaded content with other content requesters in the same cluster.In this way,the additional resource consumption of content sharing in a cluster was shared by the content requesters in the cluster together.During this process,a pricing-based approach was proposed to motivate these content requesters to cooperate with each other.Such an approach only required the base station to maintain a content downloading price.Based on this,the joint issue of user clustering,link scheduling and power allocation was studied to minimize the total cost of content requesters.To obtain a superior solution with reductive complexity,a joint optimization algorithm was designed based on the coalition formation game.Numerical results show that compared with the traditional non-cooperative scheme,the total cost of content requesters was reduced by 37.15%.Under different numbers of content requesters and cellular users,the proposed scheme is both superior to other benchmark schemes.     

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.     

CAPR: context‐aware participant recruitment mechanism in mobile crowdsourcing

With the advances of sensing, wireless communication, and mobile computing, mobile crowdsourcing has become a new paradigm for data collection and retrieval that has attracted considerable attention. This paper addresses the fundamental research issue in mobile crowdsourcing: Which participants should be selected as winners in each time slot with the aim of maximizing the total utility of the service provider in the long term? First, a double‐sided combinatorial auction model is introduced to describe the relationships between the mobile users and requesters from the perspective of supply and demand at a given time. Then, the coupling between the utility values of the system in different time slots is investigated. Based on the aforementioned analyses, this paper proposes a context‐aware participant recruitment mechanism, in which the mobile crowdsourcing system dynamically adjusts the participant recruitment mechanism depending on the ratio between the numbers of mobile users and requesters. Context‐aware participant recruitment consists of two main components: (1) a heuristic algorithm based on the greedy strategy to determine the winning participants and (2) a critical payment scheme, which guarantees the rationality of the proposed mechanism. Finally, extensive simulations demonstrate that the proposed mechanism achieves high system utility in the long term. Copyright © 2016 John Wiley & Sons, Ltd.     

A Self-Optimizing QoS-Based Access for IoT Environments

Nowadays, providing Internet of Things (IoT) environments with service level guarantee is a challenging task. Moreover, IoT services should be autonomous in order to minimize human intervention and thus to reduce the operational management cost of the corresponding big scale infrastructure. We describe in this paper a service level-based IoT architecture enabling the establishment of an IoT Service Level Agreement (iSLA) between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). The proposed iSLA specifies the requirements of an IoT service, used in a specific application domain (e-health, smart cities, etc.), in terms of different measurable Quality of Service (QoS) parameters. In order to achieve this agreement, several QoS mechanisms are to be implemented within each layer of the IoT architecture. In this context, we propose an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism to provide different IoT services with QoS guarantee. Our proposal called QBAIoT (QoS-based Access for IoT) creates different Contention Access Periods (CAP) according to different traffic types of the IoT environment. These CAPs are QoS-based and enable traffic differentiation. Thus, a QoS CAP is configured with several slots during which only IoT objects belonging to the same QoS class can send their data. Furthermore, we specify a self-management closed control loop in order to provide our IoT architecture with a self-optimizing capability concerning QoS CAPs slots allocation. This capability takes into account the actual usage of QoS CAPs as well as the characteristics of the corresponding traffic class.

     

Hybrid time slot allocation algorithm based on LoRa Internet of things

To improve the efficiency and stability of data transmission in the long-range (LoRa) Internet of things (IoT),a hybrid time slot allocation algorithm is proposed, which implements a priority mechanism with high-priority nodes sending data in fixed time slots and low-priority nodes using the carrier sense multiple access (CSMA) algorithm to compete for shared time slots to transmit data. To improve network efficiency, a gateway is used to adjust the time slot allocation policy according to network status and balance the number of fixed and shared time slots. And more, a retransmission time slot is added to the time slot allocation algorithm, which redesigns the time frame structure, and adopts a retransmission mechanism to improve communication reliability. Simulation and measurement results show that the packet loss rate and transmission delay of the proposed hybrid algorithm are smaller than those of the fixed slot allocation algorithm, making the proposed algorithm more suitable for LoRa IoT.     

基于强化学习的合作频谱分配算法

为了解决认知无线电网络中的频谱分配问题,提出了一种基于用户体验质量的合作强化学习频谱分配算法,将认知网络中的次用户模拟为强化学习中的智能体,并在次用户间引入合作机制,新加入用户可以吸收借鉴其他用户的强化学习经验,能够以更快的速度获得最佳的频谱分配方案;并且在频谱分配过程中引入了主用户和次用户之间的价格博弈因素,允许主用...     

机器学习在物联网虚假用户识别中的运用

随着通信技术的发展,物联网卡和5G技术将得到大规模应用,但存在个别企业利用物联网卡资费便宜、没有实名制等特点从中非法牟利、破坏社会稳定的问题,不利于行业健康发展。因此如何识别虚假用户成为物联网行业研究的重要课题。主要研究了在实时海量的物联网终端数据中,如何运用机器学习模型高效地识别疑似虚假用户。具体来看,通过研究相关数据的特征,采用基于正样本和未标记样本的半监督式学习模型建立实时监控异常行为的模型,达到识别物联网行业中潜在虚假用户的目的。本研究成果体现在节约大量人力物力的同时,可以帮助相关部门、人员及时发现用户的异常行为,采取相应的措施避免产生较大损失,具有广泛的行业应用前景。     

基于QoS的语义Web服务组合优化算法

在面向服务的环境下,存在众多可满足服务请求者要求的服务组合方案,如何从中选出最优或近似最优方案是Web服务组合优化要解决的问题.设计出一个不必探索整个解空间的优化算法——SBG算法,为服务请求者提供最优选择方案.实验结果表明,这种方法可有效提高选择方案的求解效率.     

多用户MIMO-OFDM系统基于QoE效用函数的跨层资源分配

对多用户MIMO-OFDM系统下行链路的资源分配问题进行了研究.依据实时(RT,real-time)和非实时(NRT,non real-time)业务的不同特点分别设计了QoE效用函数,并提出一种基于RT &NRT QoE效用函数的跨层资源分配算法.该算法利用所设计的效用函数计算用户分得资源所贡献的QoE增量,并根据由QoE增量确定的用户优先级完成时频资源块的分配,进而通过“拟注水”方式对功率分配进行优化.仿真结果表明,该算法相比已有算法在用户平均QoE、实时业务平均分组时延、非实时业务平均分组丢失率以及系统和吞吐量方面的性能均得到了明显改善.