Shuhong Chen

κ-FuzzyTrust

Large-scale mobile social networks (MSNs) facilitate connections between mobile devices and provide an effective mobile computing environment in which users can access, share, and distribute information. In MSNs, users may belong to more than one community or cluster, and overlapping users may play a special role in complex MSNs. For such MSNs, a key problem is how to evaluate or explain user trustworthiness. In this context, trust inference plays a critical role in establishing trusted social links between mobile users. To infer fuzzy trust relations between users in MSNs with overlapping communities, we propose an efficient trust inference mechanism based on fuzzy communities, which we call j-FuzzyTrust. We propose an algorithm for detection of community structure in complex networks under fuzzy degree j and construct a fuzzy implicit social graph. We then construct a mobile social context including static attributes (such as user profile and prestige) and dynamic behavioural characteristics(such as user interaction partners, interaction familiarity, communication location and time) based on the fuzzy implicit social graph. We infer the trust value between two mobile users using this mobile social context. We discuss the aggregation and propagation of trust values for overlapping users and indirect connected users. Finally, we evaluate the performance of j-FuzzyTrust in simulations. The results show the validity of our fuzzy inference mechanism for behavioural trust relationships in MSNs. They also demonstrate that j-FuzzyTrust can infer trust values with high precision. 2014 Elsevier Inc. All rights reserved.

Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

As mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been an increasing number of communications and interactions among users. Taking this advantage, users in MSNs make decisions via collecting and combining trust information from different users. Hence, trust evaluation technology has become a key requirement for network security in MSNs. In such MSNs, however, the community/group structures are dynamically changing, and users may belong to multiple communities/groups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. In this paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trust metrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNs with complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) based on dynamic complex community/group structures and propose an efficient detection algorithm for DynISBG under fuzzy degree κ. We then present a multi‐dimensional fuzzy trust inferring approach that involves four metrics, that is, static attribute trust factor, dynamic behavioral trust factor, long‐term trust evolution factor, and recommendation‐based trust opinion. Moreover, to obtain the recommendation‐based trust opinion about indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, we evaluate the performance of our novel approach with simulations. The results show that, compared with the existing approaches, the proposed model provides a more detailed analysis in trust evaluation with higher accuracy. Copyright

A Trust Model Using Implicit Call Behavioral Graph for Mobile Cloud Computing

Behavior patterns of users in mobile social cloud are always based on real world relationships and can be used to infer a level of trust between users. In this paper, we describe the implicit call behavioral graph which is formed by users’ interactions with call. We rate these relationships to form a dynamic local cloud trust, which enables users to evaluate the trust values between users within the context of a mobile social cloud network. We, then, calculate local trust values according to users’ behavioral attributes, such as call frequency, relevance, call moment, and satisfaction. Due to the unique nature of the social cloud, we discuss the propagation and aggregation of local trust values for global social cloud network. Finally, we evaluate the performance of our trust model through simulations, and show simulation results that demonstrate the importance of interaction-based behavioural relationships in recommendation system.

QoS Evaluation of End-to-End Services in Virtualized Computing Environments: A Stochastic Model Approach

Quality of service QoS optimization for end-to-end e2e services always depends on performance analysis in cloud-based service delivery industry. However, performance analysis of e2e services becomes difficult as the scale and complexity of virtualized computing environments increase. In this paper, the authors present a novel hierarchical stochastic approach to evaluate the QoS of e2e virtualized cloud services using Quasi-Birth Death structures, where jobs arrive according to a stochastic process and request virtual machines VMs, which are specified in terms of resources, i.e., VM-configuration. To reduce the complexity of performance evaluation, the overall virtualized cloud services are partitioned into three sub-hierarchies. The authors analyze each individual sub-hierarchy using stochastic queueing approach. Thus, the key performance metrics of e2e cloud service QoS, such as acceptance probability and e2e response delay incurred on user requests, are obtained.

HEPart: A balanced hypergraph partitioning algorithm for big data applications

Abstract Minimizing the query cost among multi-hosts is important to data processing for big data applications. Hypergraph is good at modelingdata and data relationships of complex networks, the typical big data applications, by representing multi-way relationships or interactions as hyperedges. Hypergraph partitioning (HP) helps to partition the query loads on several hosts, enabling the horizontal scaling of large-scale networks. Existing heuristic HP algorithms are generally vertex hypergraph partitioning, designed to minimize the number of cut hyperedges while satisfying the balance requirements of part weights regarding vertices. However, since workloads are mainly produced by group operations, minimizing query costs landing on hyperedges and balancing the workloads should be the objectives in horizontal scaling. We thus propose a heuristic hyperedge partitioning algorithm, HEPart . Specifically, HEPart directly partitions the hypergraph into K sub-hypergraphs with a minimum cutsize for vertices, while satisfying the balance constraint on hyperedge weights, based on the effective move of hyperedges. The performance of HEPart is evaluated using several complex network datasets modeled by undirected hypergraphs, under different cutsize metrics. The partitioning quality of HEPart is then compared with alternative hyperedge partitioners and vertex hypergraph partitioning algorithms. The experimental findings demonstrate the utility of HEPart (e.g. low cut cost while keeping load balancing as required, especially over scale-free networks).

Performance analysis for (X,S)-bottleneck cell in large-scale wireless networks

We present a performance analytical model for (X,S)-bottleneck cell and perform some probabilistic analysis on the performances of (X,S)-bottleneck cell, such as the probability of balance state, the transmission probability of a flow, and throughput. To capture the essential aspects of (X,S)-bottleneck cell, we use two-hierarchy Quasi-Birth-Death models (QBDs). The general characters of (X,S)-bottleneck cell are govern by the first hierarchy QBD, while the characters of each flow are captured by the second hierarchy QBD. Based on the model, we present a methodology to derive some theoretic ranges for these probabilities. Our results show that the proposed model can analyze effectively the performance of (X,S)-bottleneck cell. The results in the work are helpful for designing and managing wireless networks.

User-Controlled Encrypted Data Sharing Model in Cloud Storage

Cloud storage services provide us convenience for storing and sharing vast amounts of data by its low cost, high scalability and other advantages while it brings out security risks as well. A user-controlled encrypted data sharing model in cloud storage (UESMCS) is put forward hereby. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Thus, the reliability and safety for data sharing can be achieved provided the trustworthy third party being brought in. The experimental results show that UESMCS ensures data security in cloud storage services platform and enhances the operational performance for data sharing. The security sharing mechanism perfectly fits the actual cloud storage environment.

A Research on User Interest Model Using Ontology and VSM Extension

The user interest model is the key technology for us to efficiently obtain personalized high quality resources in huge amounts of data. On the basis of traditional user interest model, combining with the concept of ontology, this paper comes up with a construction method based on ontology semantic tree and space vector method, and its eigenvalue is changing by using forgetting curve. The model describes a theme by using semantic concept tree, and it not only simply to describe the semantic relations between concepts, but also can reflect the user interest drift character. It is more accurate in characterization of user interest, and better reflects the change of user interest, and efficiently improves the quality of network document resource personalized services. Finally the experiment describes the update process of the proposed model by using an example, and illustrates that the proposed model is simple and practical.

Hierarchical routing algorithm for ad hoc networks using mobile VMN

One of the most significant challenges introduced by routing protocol in mobile networks is coping with the unpredictable motion and the unreliable behaviour of mobile nodes. In this paper, we present a hierarchical routing algorithm based on virtual mobile node VMN. A routing path can be found rapidly by exchanging path information between VMNs without accurate topology information. We discuss the routing process and the implementing details of the proposed routing algorithm. Finally, we evaluate the performance of our hierarchical routing algorithm through simulations, and the results show that the number of mobile WAVE generated by routing algorithm is well satisfied with the quality of service QoS requirements of all source and destination nodes. Furthermore, we compare the performance on VMN failure and message delivery ratio using hierarchical and non-hierarchical routing approaches, the results show that we can obtain magnitude better performance by HRA-VMN than hierarchical state routing HSR and non-hierarchical routing approach.

Probabilistic analysis for state reachability of wireless lossy channel systems

In this paper we perform some probabilistic analysis on state reachabilities of wireless lossy channel systems (WLCS) using Continuous Stochastic Logic (CSL) for Quasi Birth-Death model (QBD). To study WLCS in a continuous time setting, we first extend Probabilistic Lossy Channel Systems (PLCS) towards WLCS. We show that the state set S0 is an attractor of WLCS model, and discuss the accumulated steady-state probabilistic reachability and the transient probabilistic reachability for WLCS. The results from the case study of WLCS show the versatility of our analytical technique and our works lead to a significantly analysis foundation where the underlying communication medium is lossy.