一种基于蜂窝网络的D2D通信资源分配算法

设备直连（Device-to-device,D2D）通信用户通过复用蜂窝用户的频谱资源可提高频谱利用率和系统的整体通信容量,但复用蜂窝网络频谱资源会带来严重的干扰,影响整体通信质量。针对干扰问题,本文通过建立通信系统模型,研究系统的线性规划问题以及相应的最佳资源分配算法。考虑到最佳算法复杂度较高,本文提出一种启发式算法来分配通信资源,通过遍历D2D对用户与蜂窝用户之间的干扰矩阵找到最小干扰值,分配复用资源给相应的蜂窝用户和D2D对用户。在蜂窝用户都分配到通信资源之后,对D2D对用户进行专用通信资源分配。仿真结果表明,该算法在降低算法复杂度的基础上显著地减小了D2D对用户对蜂窝用户的干扰,能够最大程度地增加D2D对用户的数量。     

基于QoS的D2D能效优化

为解决基站数十亿通信设备的通信能耗和D2D通信过程中复用频谱资源对蜂窝用户造成的干扰问题,建立一种优化模型系统,解决用户通信服务质量(quality of service,QoS)和系统能效的权衡问题。针对优化问题中遇到的NP难题,提出一种启发式资源分配算法1-search,降低用户搜索范围以及匹配用户的搜索时间;利用松弛的方法忽略次要干扰的影响,降低算法复杂度。仿真结果表明,1-search算法可以提高系统能效,具有较好的收敛性。     

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。     

一种结合拍卖的D2D协作通信中继选择算法

D2D协作通信技术可以满足提高蜂窝通信系统容量、扩大网络覆盖范围的通信需求。针对一些边缘用户受到信道衰落影响较大,从而影响用户的服务质量问题,提出了一种将拍卖原则引入D2D协作通信的中继选择算法。该算法首先在满足D2D通信要求和信噪比限制条件下得到中继备选集合;然后以优化链路吞吐量为目标选择最优中继节点;最后当两个或多个用户同时选中一个最优中继节点时,采用拍卖原则为D2D发射端分配最终中继。仿真结果表明,所提算法不仅有效地提高了吞吐量,同时保证了系统的中断概率。     

Location privacy: going beyond K-anonymity,cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.     

Network-Sensitive Service Discovery

We consider the problem of network-sensitive service selection (NSSS): finding services that match a particular set of functional and network properties. Current solutions handle this problem using a two-step process. First, a user obtains a list of candidates through service discovery. Then, the user applies a network-sensitive server selection technique to find the best service. Such approaches are complex and expensive since each user has to solve the NSSS problem independently. In this paper, we present a simple alternative: network-sensitive service discovery (NSSD). By integrating network-sensitivity into the service discovery process, NSSD allows users who are looking for services to specify both the desired functional and network properties at the same time. Users benefit since they only have to solve a restricted version of the server selection problem. Moreover, NSSD can solve the NSSS problem more efficiently by amortizing the overhead over many users. We present the design of NSSD, a prototype implementation, and experimental results that illustrate how NSSD can be used for different applications.     

An efficient hybrid push-pull methodology for peer-to-peer video live streaming system on mobile broadcasting social media

With the rapid growth of wireless communication technology, the availability of highly flexible and video-friendly mobile terminal platforms (such as smartphones and tablets), the emergence of major video content providers (like YouTube, Ustream, and PPTV, which provide a large catalog of attractive contents), Peer-to-Peer (P2P) live video streaming over the wireless and Internet is becoming more and more attractive to users. One of the main challenges is to provide a good quality of service though the dynamic behavior of the network. Traditionally, tree-based model uses a push method, that broadcaster transfers data to other users. This model has low start-up delay. However, there are two main problems in this method: if the bandwidth of an internal node is low, children nodes may lose data and when an internal node failure, other nodes can’t receive data until completing the recovery of the tree. On the other hand, mesh-based model uses a pull method, has low bandwidth of a neighbor node by pulling necessary data from a number of neighbor nodes. However, mesh-based model requires large buffers to support pull data from neighbor peers and there is an adjustment between minimum delay by sending pull request and overhead of whole system. So, both models have their own strengths and weaknesses. This paper proposes a new hybrid push-pull live P2P video streaming protocol called MobileCast that combines the benefits of pull and push mechanisms for live video delivery. We present new topology for P2P network with more stable and provide better video streaming quality. Our main goal is to minimize the network end-to-end delay, startup time, overhead, packet loss compared to the pure mesh networks, pure tree networks and provide a good quality of service though the dynamic behavior of the network.

     

基于上下文的VANET服务推荐中间件

车载自组织网络(vehicle ad hoc network, VANET)作为智慧城市的重要组成部分,它需要为车辆安全、便捷交通及舒适驾驶提供众多的服务.目前针对车联网中服务发现的研究主要集中在服务发现质量和服务发现延时,但是随着VANET中服务数量和种类的增加,车联网中的信息激增问题变得越来越严重,因此如何按照个性化需求为用户推荐合适的服务成为目前车联网中亟需解决的问题.针对现有车联网中服务选择策略的不足,提出一种基于上下文的车联网服务推荐中间件体系结构,该中间件可以利用车辆丰富的上下文信息和用户的历史服务记录为用户推荐服务.利用离线分析方法,提出一种基于上下文的服务推荐方法,将既符合车辆上下文约束且满足用户偏好的服务推荐给用户.仿真结果表明,中间件推荐的服务合理且符合用户偏好,同时可以降低服务导致的绕路概率.     

基于D2D对分组的TDD系统资源分配算法

针对TDD系统中多个D2D用户复用一个蜂窝用户下行信道资源,在最大化系统吞吐量的目标下,提出一种D2D对分组资源分配算法,该算法由3部分组成:1)根据信道数确定D2D对分组数,按D2D对间距确定分组中心,并将余下D2D对按其对组内D2D用户通信中断概率影响程度划分到对应分组;2)通过比较D2D对分组与蜂窝用户干扰影响强度,利用匹配算法为D2D对分组寻找相应蜂窝用户资源并复用;3)进一步根据蜂窝用户和D2D用户QOS要求,去掉组内干扰较大D2D对,从而得到最终复用蜂窝用户资源的D2D对.仿真分析表明:所提出算法既能允许系统接入较多D2D对,还能提高系统吞吐量.     

A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks

The major driver for deploying next generation wireless cellular systems is their ability to efficiently deliver resource-demanding services, many of which require symmetric communication between an uplink mobile user and a downlink mobile user that belong to the same network. In this work, we propose a utility-based joint uplink/downlink scheduling algorithm suitable for wireless services involving pairwise communication among mobile users. While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. By jointly considering the time varying channel conditions in both the uplink and the downlink directions, the proposed algorithm avoids wasting of resources and achieves notable performance gains in terms of increased number of active connections, lower packet drop rate, and increased network throughput. These gains are achieved with a tradeoff cost in terms of complexity and signaling overhead. For overhead reduction in practical scenarios, we propose an implementation over clusters within the network.     

Policing as a Service in the Cloud

ABSTRACT

Security and privacy are fundamental concerns in cloud computing both in terms of legal complications and user trust. Cloud computing is a new computing paradigm, aiming to provide reliable, customized, and guaranteed computing dynamic environment for end users. However, the existing security and privacy issues in the cloud still present a strong barrier for users to adopt cloud computing solutions. This paper investigates the security and privacy challenges in cloud computing in order to explore methods that improve the users’ trust in the adaptation of the cloud. Policing as a Service can be offered by the cloud providers with the intention of empowering users to monitor and guard their assets in the cloud. This service is beneficial both to the cloud providers and the users. However, at first, the cloud providers may only be able to offer basic auditing services due to undeveloped tools and applications. Similar to other services delivered in the cloud, users can purchase this service to gain some control over their data. The subservices of the proposed service can be Privacy as a Service and Forensics as a Service. These services give users a sense of transparency and control over their data in the cloud while better security and privacy safeguards are sought.     

基于灰狼优化和匈牙利算法的D2D资源分配策略

当前,车联网系统中存在频谱资源有限、系统干扰较大、D2D(device to device)用户通信质量差等问题。针对上述情况,提出了一种基于灰狼优化和匈牙利算法的D2D资源分配策略。在保证蜂窝用户以及D2D用户通信质量的前提下,该模型首先利用灰狼优化获得每个D2D用户的最佳发射功率,然后利用匈牙利算法实现D2D用户与蜂窝用户的最佳信道匹配,最大化D2D用户的总容量。仿真结果表明,该算法与和声搜索算法和遗传算法相比,在有效提升D2D用户的总容量同时,单个D2D用户也能获得较高的最大、最小容量。     

一种联合D2D资源分配的中继选择算法的研究

为提高蜂窝网络中边缘用户的QoS,可在蜂窝网络中引入了D2D辅助中继技术,然而由于D2D链路复用蜂窝资源会与蜂窝链路之间产生同频干扰,为了降低这种干扰,提出了一种联合资源分配的中继选择算法,该算法首先使用一种资源分配策略,使得D2D链路对蜂窝链路产生的干扰最小,然后结合资源分配结果提出了一个中继选择方案,该方案不仅考虑了D2D链路和回程链路的信道质量,而且还同时考虑到了对蜂窝链路的干扰问题。通过仿真验证,本文所提出的算法不仅能有效提升边缘用户吞吐量,同时保证了中心用户的QoS。     

Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user’s exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce communication resources, and network partitions, we propose three key features to enhance our algorithm: (1) An information sharing scheme enables mobile users to share their gathered peer location information to reduce communication overhead; (2) A historical location scheme allows mobile users to utilize stale peer location information to overcome the network partition problem; and (3) A cloaked area adjustment scheme guarantees that our spatial cloaking algorithm is free from a “center-of-cloaked-area” privacy attack. Experimental results show that our P2P spatial cloaking algorithm is scalable while guaranteeing the user’s location privacy protection.     

On web user tracking of browsing patterns for personalised advertising

On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour.     

基于用户满意度的学习服务发现算法

引入用户满意度因子,设计一个学习服务发现算法——eLSDAus,并应用于网络学习系统中。该算法允许用户参与服务发现的过程,对服务发现的效果进行评价。学习服务发现系统将用户评价反馈到学习服务发现算法,利用修正函数修正更新发布服务各属性的匹配度权值,优化反馈给用户的综合匹配度的计算。实验结果表明,在发布的学习服务数量超过1万时,该算法能提高服务发现的查全率4％～5％。网络学习者使用该系统7天后,对学习服务发现结果的总体满意比率可达到93％以上。     

HOPE: a hybrid deep neural model for out-of-town next POI recommendation

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

     

An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack

In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

     

一种基于匿名区域变换的位置隐私保护方法

针对基于位置服务的应用中存在的用户位置隐私泄露问题,提出一种基于匿名区域变换的位置隐私保护方法。在离用户一定距离处选择一个锚点生成匿名区域后,利用邻近节点处理法计算用户邻近节点查询结果与用户真实位置之间的距离,从而实现在保护用户位置隐私的同时得到精确的查询结果。理论分析和实验结果表明,与Cloaking Region和SpaceTwist算法相比,该方法在保证较低通信开销的前提下,具有较好的位置隐私保护性能。     

Design Strategies for Idle Screens of Smartphones Based on Service Usages

Linking specific smartphone services to user characteristics can inform design guidelines and improve the competitive edge of smartphone models. A key component in the design of the user interface is the idle screen, as it is the starting point for all smartphone services. The focus of this study is to elicit insights for establishing strategies for improving idle screen design by relating user behaviors and smartphone services. Benefit segmentation is employed for users’ responses to questionnaire items about service usages. Smartphone services are identified through a factor analysis and user segments are obtained by a cluster analysis. The findings can contribute to classifying smartphone services according to users’ behaviors and preferences for them and to suggesting a design alternative for idle screens, depending on the features of user groups for improved market segmentation.