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Über dieses Buch

This book constitutes the thoroughly refereed proceedings of the 11th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2015, held in Wuhan, China, in November 2015.

The 24 full papers and 8 short papers presented were carefully reviewed and selected from numerous submissions. They address topics around networking, technology and systems, including but not limited to collaborative cloud computing, architecture and evaluation, collaborative applications, sensors and Internet of Things (IoT), security.

Inhaltsverzeichnis

Frontmatter

Collaborative Cloud Computing

Frontmatter

Adaptive Multi-keyword Ranked Search Over Encrypted Cloud Data

To preserve data privacy and integrity, sensitive data has to be encrypted before outsourcing to the cloud server. However, this makes keyword search based on plaintext queries obsolete. Therefore, supporting efficient keyword based ranked searches over encrypted data became an open challenge. In recent years, several multi-keyword ranked search schemes have been proposed in trying to solve the posed challenge. However, most recently proposed schemes don’t address the issues regarding dynamics in the keyword dictionary. In this paper, we propose a novel scheme called A-MRSE that addresses and solves these issues. We introduce new algorithms to be used by data owners each time they make modifications that affects the size of the keyword dictionary. We conduct multiple experiments to demonstrate the effectiveness of our newly proposed scheme, and the results illustrates that the performance of A-MRSE scheme is much better that previously proposed schemes.

Daudi Mashauri, Ruixuan Li, Hongmu Han, Xiwu Gu, Zhiyong Xu, Cheng-zhong Xu

A Collaborated IPv6-Packets Matching Mechanism Base on Flow Label in OpenFlow

Software Defined Networks (SDN), the separation of a network device’s control and data planes, do not need to rely on the underlying network equipment (routers, switches, firewall).It is a new network which collaborated IP and a lot of relevant technical content. The control of SDN is completely open, the user can customize any rule strategy to achieve network routing and transmission, which is more flexible and intelligent. Internet Protocol version 6 (IPv6), with the 128-bit address, is the next generation Internet. In this paper, we present a Flow Table structure by using Flow Label and a matching approach which use Flow Label within IPv6 protocol to decrease the size of Flow Table with OpenFlow and the time of forwarding IPv6 packets in SDN based on OpenFlow. The simulations and analyses show that this flow table mechanism performs better.

Weifeng Sun, Huangping Wei, Zhenxing Ji, Qingqing Zhang, Chi Lin

Traveller: A Novel Tourism Platform for Students Based on Cloud Data

Tourism is one of the top choices of contemporary university students for leisure on vacation. Desires that students want to travel extensively become increasingly stronger. However, few correct analysis on the tourism market of students in e-commerce results in its immature development, causing a difficulty in making a personalized travel plan to them. Concerning such a situation, it makes sense to provide students with more high-quality plans based on personalized analysis on their tourism preferences. Thus, it is necessary to design a safe, reliable, stable platform for students’ travel, named Traveller. Cloud computing service is mainly designed for PC machines at present, but it is a trend of future development to combine the cloud computing and mobile application. From the viewpoint of technique, powerful computing capacity and storage capacity are sufficient to improve the user experience. From the viewpoint of mobile user, a sharp increase of users in recent years has become a driving force to push the advancement of mobile application. As a consequence, mobile computing will soon prove its prevalence. In this paper, tourist information will be collected by communicating through a mobile terminal to send requests to the server in the cloud, making full use of cloud server’s features such as efficiency and convenience to update.

Qi-Ying Hu, Chang-Dong Wang, Jia-Xin Hong, Meng-Zhe Hua, Di Huang

Achieving Application-Level Utility Max-Min Fairness of Bandwidth Allocation in Datacenter Networks

Providing fair bandwidth allocation for applications is becoming increasingly compelling in cloud datacenters as different applications compete for shared datacenter network resources. Existing solutions mainly provide bandwidth guarantees for virtual machines (VMs) and achieve the fairness of VM bandwidth allocation. However, scant attention has been paid to application bandwidth guarantees for the fairness of application performance. In this paper, we introduce a rigorous definition of application-level utility max-min fairness, which guides us to develop a non-linear model to investigate the relationship between the fairness of application performance (utility) and the application bandwidth allocation. Based on Newton’s method, we further design a simple yet effective algorithm to solve this problem, and evaluate its effectiveness with extensive experiments using OpenFlow in Mininet virtual network environment. Evaluation results show that our algorithm can achieve utility max-min fair share of bandwidth allocation for applications in datacenter networks, yet with an acceptable computational overhead.

Wangying Ye, Fei Xu, Wei Zhang

Cost-Effective Service Provisioning for Hybrid Cloud Applications

A hybrid cloud, which combines a private cloud and a public cloud, has become more and more popular. For most corporations, they leverage one public cloud. However, with fierce competition among public cloud providers, public cloud services change frequently, which may lead to service unavailability and a less cost-effective hybrid cloud solution. As a result, leveraging multiple public clouds in the hybrid cloud is a potential solution. In this paper, we identify such a problem in current hybrid cloud and analyze the necessity of load balancing for hybrid cloud applications. Focusing on cost minimization and performance guarantee, we propose a Least Cost per Connection (LCC) algorithm so as to choose the most cost-effective clouds along with adapting changes among multiple public clouds. The simulation results show that our solution can significantly decrease the outsourcing cost as well as guarantee QoS of applications.

Bin Luo, Yipei Niu, Fangming Liu

Architecture and Evaluation

Frontmatter

On Rule Placement for Multi-path Routing in Software-Defined Networks

Software Defined Network (SDN) is a newly emerging network architecture with the core concept of separating the control plane and the data plane. A centralized controller is introduced to manage and configure network equipments to realize flexible control of network traffic. SDN technology provides a good platform for application-oriented network innovations to improve network resource utilization, simplify network management, and reduce operating cost. With SDN devices (e.g., OpenFlow switches), routing becomes more flexible by simply changing the contents of flow tables. The flow table is usually implemented in expensive and power-hungry Ternary Content Addressable Memory (TCAM), which is thus capacity-limited. How to optimize the network performance with the consideration of limited TCAM capacity is therefore significant. For example, multi-path routing (MPR) has been widely regarded as a promising method to promote the network performance. However, MPR is at the expense of additional forwarding rule, imposing a heavy burden on the limited flow table. In this paper, we are motivated to investigate an MPR schedule problem with joint consideration of forwarding rule placement. An integer linear programming (ILP) model is formulated to describe this optimization problem. To address the computation complexity, we further design a three-phase heuristic algorithm. Its high efficiency is validated by the fact that it much approaches the optimal solution, according to our extensive simulation studies.

Jie Zhang, Deze Zeng, Lin Gu, Hong Yao, Yuanyuan Fan

Crowdstore: A Crowdsourcing Graph Database

Existing crowdsourcing database systems fail to support complex, collaborative or responsive crowd work. These systems implement human computation as independent tasks published online, and subsequently chosen by individual workers. Such pull model does not support worker collaboration and its expertise matching relies on workers’ subjective self-assessment. An extension to graph query languages combined with an enhanced database system components can express and facilitate social collaboration, sophisticated expert discovery and low-latency crowd work. In this paper we present such an extension, CRowdPQ, backed up by the database management system Crowdstore.

Vitaliy Liptchinsky, Benjamin Satzger, Stefan Schulte, Schahram Dustdar

An ARM-Based Hadoop Performance Evaluation Platform: Design and Implementation

As the growth of cluster scale, huge power consumption will be a major bottleneck for future large-scale high performance cluster. However, most existing cloud-clusters are based on power-hungry X86-64 which merely aims to common enterprise applications. In this paper, we improve the cluster performance by leveraging ARM SoCs which feature energy-efficient. In our prototype, cluster with five Cubieboard4, we run HPL and achieve 9.025 GFLOPS which exhibits a great computational potential. Moreover, we build our measurement model and conduct extensive evaluation by comparing the performance of the cluster with WordCount, k-Means (etc.) running in Map-Reduce mode and Spark mode respectively. The experiment results demonstrate that our cluster can guarantee higher computational efficiency on compute-intensive utilities with the RDD feature of Spark. Finally, we propose a more suitable theoretical hybrid architecture of future cloud clusters with a stronger master and customized ARMv8 based TaskTrackers for data-intensive computing.

Xiaohu Fan, Si Chen, Shipeng Qi, Xincheng Luo, Jing Zeng, Hao Huang, Changsheng Xie

Research on Service Organization Based on Decorator Pattern

With the development of web service applications, how to improve the efficiency of service discovery is an important research work in service computing era. Based on the service clusters which are formed through service clustering, this paper uses the Decorator Pattern ideology to organize the service clusters according to the collaborative relationships between them. The tree structure is used to express the organized service clusters with certain correlations, and it helps to realize service discovery efficiently. It also discusses how to add new services to the service cluster organization dynamically. The experiment results show the method can enhance the efficiency of services (atomic and composite services) discovery.

Jianxiao Liu, Zaiwen Feng, Zonglin Tian, Feng Liu, Xiaoxia Li

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

This paper proposes neighborhood-based approach for QoS-prediction of cloud services by taking advantages of collaborative intelligence. Different from heuristic collaborative filtering and matrix-factorization, we set a formal neighborhood-based prediction framework which allows an efficient global optimization scheme, and then exploits different baseline estimate components to improve predictive performance. To validate our methods, a large-scale QoS-specific dataset which consists of invocation records from 339 service users on 5,825 web services on a world-scale distributed network is used. Experimental results show that the learned neighborhood-based models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than state-of-the-art prediction methods.

Hao Wu, Jun He, Bo Li, Yijian Pei

Collaborative Application

Frontmatter

Multi-core Accelerated Operational Transformation for Collaborative Editing

This article proposes a parallel operational transformation (OT) algorithm for collaborative editing. OT maintains the eventual consistency of replicated data in optimistic way, allowing users to manipulate the shared document simultaneously. It has been the first choice for most collaborative applications. However, existing approaches must keep the number of operations generated in a session small so that it can provide a decent responsive time. The multi-core/many-core architectures are becoming pervasive in recent years. Unfortunately, there is no prior work which has explored accelerating operational transformation algorithms with available computation power. We present a lock-free operation history which are accessed by a batch of remote operations at the same time. Moreover, a data parallel computation model is constructed to accelerate the integration of local operations. To the best of our knowledge, this is the first parallel OT algorithm. Experimental results show our proposed algorithm outperforms the stat-of-art algorithms for collaborative editing.

Weiwei Cai, Fazhi He, Xiao Lv

NFV: Near Field Vibration Based Group Device Pairing

In this paper, we propose a group device pairing system called NFV to enable group communication using mobile device equipped with motion sensors. A group of people put their mobile devices on the table and wait for a secure connection. We propose a vibration-propagation based key delivery scheme to transmit a secure connection key among a group of trusted mobile devices. Based on this key, group users establish a confidential communication channel between their devices. NFV achieves group devices pairing without the complex operations needed in prior works. We implemented NFV using off-the-shelf Android smartphones. The experimental results shows the efficiency and security of our system.

Zhiping Jiang, Jinsong Han, Wei Xi, Jizhong Zhao

A Novel Method for Chinese Named Entity Recognition Based on Character Vector

In this paper, a novel method using for Chinese named entity recognition is proposed. For each class, A posteriori probability model is acquired by combing probabilistic model and character vector, which are acquired from each class by using training data. After segment Chinese sentence into words, the posteriori probability of every words in each class can be calculated by using model we proposed, and thus the type of word could be determined according to maximum posteriori probability.

Jing Lu, Mao Ye, Zhi Tang, Xiao-Jun Huang, Jia-Le Ma

Android Apps Security Evaluation System in the Cloud

It is an uncertain problem that evaluating the security of Android Apps. We can’t be sure of the danger with sensitive permissions in an individual of Apps. Permissions are an important factor in security decisions of Apps. For the Apps security evaluation, the paper proceed from the Android permission mechanism, proposes a classified dynamic security evaluation method. Apps security evaluation system include the large-scale permissions capturing and classification risk evaluation algorithm. The system could find the minimum permissions which are the common features of Apps. The minimum permissions can be dynamically changed according to different classified Apps. We adopt Euclidean distance-based similarity calculation algorithm to evaluate risk. The difference value determines the APP’s malicious risk. Experiments prove that the system has reference value to the APP security assessment.

Hao Wang, Tao Li, Tong Zhang, Jie Wang

Sensor and Internet of Things

Frontmatter

Protecting Privacy for Big Data in Body Sensor Networks: A Differential Privacy Approach

As a special kind of application of wireless sensor networks, Body Sensor Networks (BSNs) have broad perspectives especially in clinical caring and medical monitoring. Big data acquired from BSNs usually contain sensitive information, which are compulsory to be appropriately protected. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, a differential privacy protection scheme for big data in body sensor network is proposed. We introduce the concept of dynamic noise thresholds which makes our scheme more suitable for processing big data. It can ensure privacy during the whole life cycle of the data, which makes privacy protection for big data in BSNs promising. Extensive experiments are conducted to outline the merits of our scheme. Experimental results reveal that our scheme has higher level of privacy protection. Even in the case where the attacker has full background knowledge, it still provides sufficient ambiguity, which ensures being unable to match people based on the ECG data characteristic so as to preserve the privacy.

Chi Lin, Zihao Song, Qing Liu, Weifeng Sun, Guowei Wu

Characterizing Interference in a Campus WiFi Network via Mobile Crowd Sensing

WiFi networks and smartphones have been penetrating into people’s daily life pervasively. The increasingly dense deployments of WiFi APs have led to the severe spectrum usage overlap and channel interference. In this paper, we proposed a mobile crowd sensing method to characterize the interference experienced by a campus WiFi network by utilizing the powerful sensing capability of smartphone and users’ mobility. We designed and implemented a mobile measurement App. This App can help the volunteers to sense the neighboring WiFi APs in the background on the Android mobile phones. The measurement data are then uploaded to the measurement repository server for further data analysis. Our measurement results showed that both 2.4 GHz and 5 GHz WiFi APs have been commonly deployed on campus, and 2.4 GHz APs dominate for around $$80\,\%$$ of total measured APs. The spectrum overlap and channel interference in the 2.4 GHz band is much severe than that in the 5 GHz band. The rising WiFi interference is due to the uncoordinated planning, random deployment and intensive density of WiFi networks at different locations. Our field measurement study may provide guidelines to design the next generation software-defined WiFi networks in order to achieve high performance with minimized interference.

Chengwei Zhang, Dongsheng Qiu, Shiling Mao, Xiaojun Hei, Wenqing Cheng

On Participant Selection for Minimum Cost Participatory Urban Sensing with Guaranteed Quality of Information

Exploring vehicles to conduct participatory urban sensing has become an economic and efficient sensing paradigm to pursue the smart city vision. Intuitively, having more vehicles participate in one sensing task, higher quality-of-information (QoI) can be achieved. However, more participation also implies a higher sensing cost, which include the cost pay to participated vehicles and 3G traffic cost. This paper introduces an interesting problem on how to select an appropriate set of vehicles to minimize the sensing cost while guaranteeing the required QoI. In this paper, we define a new QoI metric called coverage ratio satisfaction (CRS) with the consideration of coverage from both temporary and spatial aspects. Based on the CRS definition, we formulate the minimum cost CRS guaranteeing problem as an integer linear problem and propose a participant selection strategy called Vehicles Participant Selection (VPS). The high efficiency of VPS is extensively validated by real trace based experiments.

Hong Yao, Changkai Zhang, Chao Liu, Qingzhong Liang, Xuesong Yan, Chengyu Hu

$$\lambda $$ -CoAP: An Internet of Things and Cloud Computing Integration Based on the Lambda Architecture and CoAP

The Internet of Things (IoT) is an emerging technology that is growing continuously thanks to the number of devices deployed and data generated. Nevertheless, an upper layer to abstract the limitations of storing, processing, battery and networking is becoming a mandatory need in this field. Cloud Computing is an especially suitable technology that can supplement this field in the limitations mentioned. However, the current platforms are not prepared for querying large amounts of data with arbitrary functions in real-time, which are necessary requirements for real-time systems. This paper presents $$\lambda $$-CoAP architecture, a novel paradigm not introduced yet to the best of our knowledge, which proposes an integration of Cloud Computing and Internet of Things through the Lambda Architecture (LA) and a Constrained Application Protocol (CoAP) middleware. The $$\lambda $$-CoAP architecture has the purpose to query, process and analyze large amounts of IoT data with arbitrary functions in real-time. On the other hand, the CoAP middleware is a lightweight middleware that can be deployed in resource constrained devices and allows the way of the IoT towards the Web of Things. Moreover, the $$\lambda $$-CoAP also contains a set of components with well defined interfaces for querying, managing, and actuating over the system.

Manuel Díaz, Cristian Martín, Bartolomé Rubio

A Framework for Multiscale-, QoC- and Privacy-aware Context Dissemination in the Internet of Things

The tremendous amount of context information that can be generated by the Internet of Things (IoT) calls for new solutions able to dig for the relevant information fitting applications’ needs. This paper proposes to leverage multiscale-, Quality of Context (QoC)- and privacy-awareness for the efficient filtering of context information disseminated between the decoupled producers and consumers of the IoT. We first discuss some specific challenges that must be addressed by next generation context managers, including multiscalability, distributed push and pull communications, and the consideration of both QoC and privacy constraints. We then answer these challenges with a new context dissemination framework involving visibility and forwarding filters and illustrate it through the implementation of a collaborative social welfare scenario.

Sophie Chabridon, Denis Conan, Thierry Desprats, Mohamed Mbarki, Chantal Taconet, Léon Lim, Pierrick Marie, Sam Rottenberg

Security

Frontmatter

SSG: Sensor Security Guard for Android Smartphones

The smartphone sensors provide extraordinary user experience in various Android apps, e.g. sport apps, gravity sensing games. Recent works have been proposed to launch powerful sensor-based attacks such as location tracing and sound eavesdropping. The use of sensors does not require any permission in Android apps, so these attacks are very difficult to be noticed by the app users. Furthermore, the combination of various kinds of sensors generates numerous types of attacks which are hard to be systematically studied.To better address the attacks, we have developed a taxonomy on sensor-based attacks from five aspects. In this work, we propose a sensor API hooking and information filtering framework, Sensor Security Guard (SSG). Unlike any rough hooking framework, this system provides fine-grained processing for different security levels set by the users, or by default. The sensor data is blocked, forged or processed under different mode strategies and then returned to the apps. In addition, according to the taxonomy, SSG develops fine-grained corresponding countermeasures. We evaluate the usability of SSG on 30 popular apps chosen from Google Market. SSG does not cause any crash of either the Android system or the apps while working. The result indicated that SSG could significantly preserve the users’ privacy with acceptable energy lost.

Bodong Li, Yuanyuan Zhang, Chen Lyu, Juanru Li, Dawu Gu

Fast Secure Scalar Product Protocol with (almost) Optimal Efficiency

Secure scalar product protocol has wide applications for privacy-preservation in collaborative computation. In this paper, we propose a new secure scalar product protocol, which does not employ any public-key encryption and third party. Compared to scalar product computation without privacy-preservation, our proposed scheme introduces no extra communication overheads and little extra computation cost. That is, the new scheme can achieve almost optimal running efficiency, and thus is much applicable to privacy-preservation for large-scale data in collaborative computation. Theoretical analysis and evaluation indicate the security and efficiency of our scheme.

Youwen Zhu, Zhikuan Wang, Bilal Hassan, Yue Zhang, Jian Wang, Cheng Qian

Efficient Secure Authenticated Key Exchange Without NAXOS’ Approach Based on Decision Linear Problem

LaMacchia, Lauter and Mityagin [4] presents significant security model for Authenticated Key Exchange (AKE) protocols (eCK) which it is extending for Canetti-Krawczyk model (CK). They contrived a protocol secured in that model called NAXOS. eCK model allows adversary to obtain ephemeral secret information corresponding to the test session which complexify the security proof. To vanquish this NAXOS combines an ephemeral private key with a static private key to generate an ephemeral public in the form $$X = g^{H(x,a)}$$. As a consequence, the discrete logarithm of an ephemeral public key is hidden via an additional random oracle. In this paper we present AKE protocol secure in eCK model under Decision Linear assumption(DLIN) without using NAXOS trick with a fastened reduction, which reduce the risk of leaking the static private key, that because of the derivation of the ephemeral public key is independent from the static private key. This is in contrast to protocols that use the NAXOS’ approach. And minimize the use of the random oracle, by applying it only to the session key derivation. Moreover, each ephemeral and static key has its particular generator which gives tight security for the protocol.

Mojahed Mohamed, Xiaofen Wang, Xiaosong Zhang

Towards Secure Distributed Hash Table

A distributed hash table (DHT) provides decentralized lookup service for distributed applications. All current implementations of DHT are achieved by the individual components being run by the participants of the application in question. Namely, the correctness of the DHT relies on that all the participants follow the same protocol. Unfortunately, this aspect of the current approach makes DHT seriously vulnerable to attacks. Such security and fault tolerance concerns about DHT prompted several attempts to improve the vulnerability of DHT. However, all the proposed solutions also rely on the code to be executed correctly. We present in this paper a novel way for implementing DHT, giving rise to an architecture we call GDHT, for Governed Distributed Hash Table. GDHT implements the required protocol with a powerful means for establishing policies governing the behaviors of the participants of DHT. By carrying out the protocol by an equally distributed middleware, the correctness of the execution of routing algorithm is guaranteed. Moreover, the execution of the security module and improvements on routing algorithm can also be ensured.

Zhe Wang, Naftaly H. Minsky

An Anomaly Detection Model for Network Intrusions Using One-Class SVM and Scaling Strategy

Intrusion detection acts as an effective countermeasure to solve the network security problems. Support Vector Machine (SVM) is one of the widely used intrusion detection techniques. However, the commonly used two-class SVM algorithms are facing difficulties of constructing the training dataset. That is because in many real application scenarios, normal connection records are easy to be obtained, but attack records are not so. We propose an anomaly detection model for network intrusions by using one-class SVM and scaling strategy. The one-class SVM adopts only normal network connection records as the training dataset. The scaling strategy guarantees that the variability of feature values can reflect their importance, thus improving the detection accuracy significantly. Experimental results on KDDCUP99 dataset show that compared to Probabilistic Neural Network (PNN) and C-SVM, our one-class SVM based model achieves higher detection rates and yields average better performance in terms of precision, recall and F-value.

Ming Zhang, Boyi Xu, Dongxia Wang

Short Paper

Frontmatter

Layered Consistency Management for Advanced Collaborative Compound Document Authoring

In distributed collaborative document authoring environments, the preservation of a globally consistent data state is an important factor. However, synchronization conflicts are unavoidable and constitute a serious challenge. Our advanced compound document system provides the basis for a novel consistency management approach, in particular regarding autonomous conflict detection and resolution. Current techniques to achieve and maintain global consistency in distributed environments almost exclusively utilize file-based data structures, thereby limiting the accessibility to supplementary information.In this paper, we present a layer-based consistency management approach harnessing a fine-granular, graph-based data representation and relational dependencies. We discuss the application of concurrent conflict detection and resolution modules designed to preserve user intent while avoiding workflow interruptions. The combination of an advanced compound document system with autonomous, layer-based consistency management has the potential to notably increase reliability and facilitate the collaborative authoring process.

Johannes Klein, Jean Botev, Steffen Rothkugel

On Ambiguity Issues of Converting LaTeX Mathematical Formula to Content MathML

Facing the demand of providing retrieval result with rich semantic information for users in math searching, mathematical formulas of LaTeX are usually converted to Content MathML. For the problem of ambiguity formulas in the process of conversion, a method of semantic disambiguation for mathematics formulas which is based on the operator context was proposed. At first, ambiguity operator was found according to the ambiguity operator mapping table. Then, the ambiguity operator context is got through the array traversal. At last, the specific meaning was conjectured according to the ambiguity operator context. The experimental results show that compared with the type-system the method can make up for its disadvantages in simple formulas aspect and gets a higher average accuracy. In practical application, this method can effectively solve the problem of ambiguity formulas in the process of conversion.

Kai Wang, Xinfu Li, Xuedong Tian

LTMF: Local-Based Tag Integration Model for Recommendation

There are two primary approaches to collaborative filtering: memory- based and model-based. The traditional techniques fail to integrate with these two approaches and also can’t fully utilize the tag features which data contains. Based on mining local information, this paper combines neighborhood method and matrix factorization technique. By taking fuller consideration of the tag features, we propose an algorithm named LTMF (Local-Tag MF). After the real data validation, this model performs better than other state-of-art algorithms.

Deyuan Zheng, Huan Huo, Shang-ye Chen, Biao Xu, Liang Liu

A Privacy-Friendly Model for an Efficient and Effective Activity Scheduling Inside Dynamic Virtual Organizations

The cooperation among people is one of the key factors for most processes and activities. The efficiency and the effectiveness of the cooperation has an intrinsic value, which significantly affects performances and outcomes. Open communities, as well as spontaneous or predefined virtual organizations, are demanding for a more solid and consistent support for activity scheduling and managing in a context of flexibility and respect of the individual needs. This paper proposes a privacy-friendly model to support virtual organizations in the scheduling and management of their most valuable resource: the time.

Salvatore F. Pileggi

A Discovery Method of Service Bottleneck for Distributed Service

In order to deal with the large scale access to billions of users currently, large internet companies have adopted the distributed service of parallel processing to support it. The uncertainty of user behavior and software multi-tier architecture led to the distributed service behavior of uncertainty and complex dynamic combination between services, so it is difficult to detect the service bottleneck of distributed service. In this paper, we propose a service bottleneck discovery model of distributed service which is based on the two layer structure: the analysis based on the behavior attribute of the service and the relationship between the services.

Jie Wang, Tao Li, Hao Wang, Tong Zhang

A Collaborative Rear-End Collision Warning Algorithm in Vehicular Ad Hoc Networks

How to solve rear-end collision warning problem has become an increasingly tough task nowadays. Numerous studies have been investigated on this field in past decades, either time-consuming or with strict assumptions. In this paper, we have proposed a collaborative rear-end collision warning algorithm (CORECWA), to assess traffic risk in accordance with real-time traffic data detected, transmitted and processed, by vehicles and infrastructures in vehicular ad hoc networks (VANETs) collaboratively. CORECWA considers some influential factors, including space headway between the two preceding and following vehicles, velocity of these two vehicles, drivers’ behavior characteristics, to evaluate the current traffic risk of the following vehicle. Experiments results demonstrate that CORECWA can gain better performance, compared with a well-acknowledged algorithm HONDA algorithm.

Binbin Zhou, Hexin Lv, Huafeng Chen, Ping Xu

Analysis of Signaling Overhead and Performance Evaluation in Cellular Networks of WeChat Software

The instant communication software such as WeChat, QQ and Fetion becomes popular with the rapid development of mobile terminals and wireless personal communication technology. To refresh the online or offline status of such software, signaling message must be sent every given intervals. However, the signaling message raised by huge number of users will cause severe overhead of mobile networks, which will affect the outage performance of network. In this work, we analyze the signaling overhead caused by such software and evaluate the influence using the system level simulation platform. Results indicate that, the signaling overhead will affect the outage performance when the density of users is great. Practical solution is also raised at the end of our paper.

Yuan Gao, Hong Ao, Jian Chu, Zhou Bo, Weigui Zhou, Yi Li

Exploration of Applying Crowdsourcing in Geosciences: A Case Study of Qinghai-Tibetan Lake Extraction

With the emerging of vast quantities of geospatial data, large temporal and spatial scale of data are used in geosciences research nowadays. As a lot of data processing tasks such as image interpretation are hard to be processed automatically, and the data process workload is huge, crowdsourcing is studied as a supplement tool of cloud computing technology and advanced algorithms. This paper outlines the procedure and methodology of applying crowdsourcing in geoscientific data process. And based on the GSCloud platform, a case study of Qinghai-Tibetan Lake Extraction task has been carried out to explore the feasibility of the application of crowdsourcing in geosciences. By analyzing the case, the paper summarizes the problems and characteristics, and advantages and challenges are also presented at last.

Jianghua Zhao, Xuezhi Wang, Qinghui Lin, Jianhui Li

Backmatter

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