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||Abstract in English
||Abstract in Chinese
||Karim Alinani, Annadil Alinani, Dua Hussain Narejo, and Guojun Wang*, "Aggregating Author Profiles from Multiple Publisher Networks to Build a List of Potential Collaborators," IEEE ACCESS, April 2018.
||Recommender systems have roots in numerous fields, and their use is widespread in the modern world. The scientific community is striving to enhance the quality of life by breaking innovative barriers and developing solutions that had never previously been considered. In an ideal world, an individual researcher would participate in various fields of research and make cumulative impactful contributions to benefit society. However, in reality, this goal is difficult to attain without a team of collaborators. Collaboration refers to the information of partnerships that bring uniquely talented researchers together around a common idea. However, efforts to seek such co-authors not only are challenging but also occasionally yield no significant results. In this paper, we propose a recommender system to aggregate author information from multiple publisher networks. It evaluates the trustworthiness of the author recommendations based on the impact of the authors' contributions and the recency and popularity of their work as well as the correlations among these factors. On this basis, the system generates a list of prospective collaborators who might be of interest to a given researcher.
||[SCI&EI检索，IF: 3.244, JCR 1区, 计算机科学]
||Wenyin Yang, Guojun Wang*, Kim-Kwang Raymond Chood, and Shuhong Chen, "HEPart: A balanced hypergraph partitioning algorithm for big data applications," Future Generation Computer Systems, 83: 250-268, June 2018.
||Minimizing the query cost among multi-hosts is important to data processing for big data applications. Hypergraph is good at modelling data 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).
||[SCI&EI检索，IF: 3.997, JCR 1区, 计算机科学]
||Yinglong Dai and Guojun Wang*, "Analyzing Tongue Images Using a Conceptual Alignment Deep Autoencoder," IEEE ACCESS, 6: 5962-5972, March 2018.
||Artificial intelligence can learn some concepts by analyzing sensory data similar to humans. This paper explores how artificial neural networks (ANNs) can learn abstract concepts by analyzing tongue images based on concepts from traditional Chinese medicine (TCM), which is a discipline that relies heavily on practitioner experience. A computer-aided method will be investigated that analyzes sensory data for TCM practitioners. This paper proposes capitalizing on deep learning techniques. A method called the conceptual alignment deep autoencoder (CADAE) is proposed to analyze tongue images that represent different body constitution (BC) types, which are the underlying concepts in TCM. In the first step, CADAE encodes the images to a representation space; in the second step, it decodes the patterns. The experiments demonstrate that CADAE can learn effective representations of abstract concepts aligned with BC types by encoding the tongue images. Furthermore, the representation space of the hidden conceptual neurons can be visualized by a decoder network. The experiments also demonstrate that ANNs acquire different data perspectives when different loss functions are used for training. Numerous representation spaces of ANNs remain to be explored. To some extent, our exploration demonstrates that artificial intelligence (AI) has the ability to learn some concepts in a manner similar to human beings. Based on this ability, AI shows promise in helping humans form new effective concepts that can facilitate medical development and alleviate the burdens of medical practitioners.
||[SCI&EI检索，IF: 3.244, JCR 1区, 计算机科学]
||Yinglong Dai, Guojun Wang* and Kuan-Ching Li, "Conceptual alignment deep neural networks," Journal of Intelligent & Fuzzy Systems, 34(3): 1631-1642, March 2018.
||Deep Neural Networks (DNNs) have powerful recognition abilities to classify different objects. Although the models of DNNs can reach very high accuracy even beyond human level, they are regarded as black boxes that are absent of interpretability. In the training process of DNNs, abstract features can be automatically extracted from high-dimensional data, such as images. However, the extracted features are usually mapped into a representation space that is not aligned with human knowledge. In some cases, the interpretability is necessary, e.g. medical diagnoses. For the purpose of aligning the representation space with human knowledge, this paper proposes a kind of DNNs, termed as Conceptual Alignment Deep Neural Networks (CADNNs), which can produce interpretable representations in the hidden layers. In CADNNs, some hidden neurons are selected as conceptual neurons to extract the human-formed concepts, while other hidden neurons, called free neurons, can be trained freely. All hidden neurons will contribute to the final classification results. Experiments demonstrate that the CADNNs can keep up with the accuracy of DNNs, even though CADNNs have extra constraints of conceptual neurons. Experiments also reveal that the free neurons could learn some concepts aligned with human knowledge in some cases.
||[SCI&EI检索，IF: 1.261, JCR 3区, 计算机科学]
||Sancheng Peng, Guojun Wang*, Yongmei Zhou, Cong Wan, Cong Wang, Shui Yu, and Jianwei Niu, "An Immunization Framework for Social Networks through Big Data Based Influence Modeling," IEEE Transactions on Dependable and Secure Computing, PP(99): 1-1, July 2017.
||Social networks are critical in terms of information or malware propagation. However, how to contain the spreading of malware in social networks is still an open and challenging issue. In this paper, we propose a novel defending method through big data based influence modeling. We first establish a social interaction graph based on big data sets of the studied object. Based on the graph, we are able to measure direct influence of individuals by computing each node’s strength, which includes the degree of the node and the total number of messages sent by each user to her friends. Then, we design an algorithm to construct influence spreading tree using the breadth first search strategy, and measure indirect influence of individuals by traversing the tree. We identify the top k influential nodes among all the nodes via the social influence strength, and propose an immunization algorithm to defend social networks against various attacks. The extensive experiments show that influence can spread easily in social networks, and the greater the influence of initial spread node is, the more impact it is on the malware propagation in social networks. The proposed method provides an effective solution to the prevention of malware or malicious messages propagation in social networks.
||[SCI&EI检索，IF: 2.926，CCF A类, JCR 1区, 计算机科学]
||Shaobo Zhang, Kim-Kwang Raymond Choo, Qin Liu, and Guojun Wang*, "Enhancing Privacy through Uniform Grid and Caching in Location-based Services," Future Generation Computer Systems, June 2017.
||With the increasing popularity of location-based services (LBSs), there is a corresponding increase in the potential for location privacy leakage. Existing solutions generally introduce a fully-trusted third party between the users and the location service provider (LSP). However, such an approach offers limited privacy guarantees and incurs high communication overhead. Specifically, once a fully-trusted third party is compromised, user information would likely be exposed. In this paper, we propose a solution designed to enhance location privacy in LBSs. Our scheme is based on the uniform grid, and adopts both order- preserving symmetric encryption (OPSE) and k-anonymity technique. Thus, the anonymizer knows noth- ing about a user’s real location, and it can only implement simple matching and comparison operations. In our approach, we also employ an entity (hereafter referred to as the converter) to transform the user- defined grid structure into the uniform grid structure. This combined with the caching mechanism, allow us to avoid repeated queries from different users on the same query spatial region and consequently, reduce the overhead of the LBS server. The analysis and simulation results demonstrate that our proposal can effectively preserve a user’s location privacy, with reduced overheads at the anonymizer and the LBS server.
||[SCI&EI检索，IF: 3.997, CCF C类, JCR 1区, 计算机科学]
||Shaobo Zhang, Guojun Wang*, Qin Liu, and Jemal H. Abawajy, "A Trajectory Privacy-Preserving Scheme based on Query Exchange in Mobile Social Networks," Soft Computing, June 2017.
||With the increase in the number of active location- based service (LBS) users, protecting the privacy of user trajectory has become a significant concern. In this paper, we propose a deviation-based query exchange (DQE) scheme that obfuscates the users’ query point to mitigate trajectory disclosure in mobile social networks (MSNs). The user finds a best matching user (BMU) in an MSN to exchange queries when a query request is issued. The DQE scheme can pre- vent the attacker from reconstructing the user’s trajectory by collecting data from the LBS server which records only the user’s ID and his BMUs’ locations (fake locations). By virtue of the private matching algorithm based on the matrix confusion, the DQE scheme allows LBS users to enjoy the service while preserving their privacy. In order to test the effectiveness and efficiency of the proposed scheme, we car- ried out extensive security and performance analyses under various conditions. The results of the experiments show that the proposed DQE scheme can protect users’ trajectory pri- vacy effectively and reduce the overhead of the LBS server.
||[SCI&EI检索，IF: 2.472, CCF C类, JCR 2区, 计算机科学]
||Feng Wang, Wenjun Jiang, Xiaolin Li, and Guojun Wang*, "Maximizing Positive Influence Spread in Online Social Networks via Fluid Dynamics," Future Generation Computer Systems, June 2017.
||In online social networks, many application problems can be generalized as influence maximization problem, which targets at finding the top-k influential users. Most of the existing influence spread models ignore user’s attitude and interaction and cannot model the dynamic influence process. We propose a novel influence spread model called Fluidspread, using the fluid dynamics theory to reveal the time- evolving influence spread process. In this paper, we model the influence spread process as the fluid update process in three dimensions: the fluid height difference, the fluid temperature and the temperature difference. To the best of our knowledge, this is first attempt of using the fluid dynamics theory in this field. Moreover, we formulate the Maximizing Positive Influenced Users (MPIU) problem and design the Fluidspread greedy algorithm to solve it. Through the experimental results, we demonstrate the effectiveness and efficiency of our Fluidspread model and Fluidspread greedy algorithm.
||[SCI&EI检索，IF: 3.997, CCF C类, JCR 1区, 计算机科学]
||Tian Wang, Yang Li, Guojun Wang*, Jiannong Cao, Md Zakirul Alam Bhuiyan, Weijia Jia, "Sustainable and Efficient Data Collection from WSNs to Cloud," IEEE Transactions on Sustainable Computing, PP(99): 1-1, March 2017.
||The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.
||Qin Liu, Yuhong Guo, Jie Wu, Guojun Wang*，"Effective Query Grouping Strategy in Clouds," Journal of Computer Science and Technology, 32(6): 1231-1249, December 2017.
||As the demand for the development of cloud computing grows, more and more organizations have outsourced their data and query services to the cloud for cost-saving and flexibility. Suppose an organization that has a great number of users querying the cloud-deployed multiple proxy servers to achieve cost efficiency and load balancing. Given n queries, each of which is expressed as several keywords, and k proxy servers, the problem to be solved is how to classify n queries into k groups, in order to minimize the difference between each group and the number of distinct keywords in all groups. Since this problem is NP-hard, it is solved in mathematic and heuristic ways. Mathematic grouping uses a local optimization method, and heuristic grouping is based on k-means. Specifically, two extensions are provided: the first one focuses on robustness, i.e., each user obtains search results even if some proxy servers fail; the second one focuses on benefit, i.e., each user can retrieve as many files as possible that may be of interest without increasing the sum. Extensive evaluations have been conducted on both a synthetic dataset and real query traces to verify the effectiveness of our strategies.
||[SCI&EI检索，IF: 0.956, CCF B类, JCR 3区, 计算机科学]
||Md Zakirul Alam Bhuiyan, Guojun Wang*, Jie Wu, Jiannong Cao, Xuefeng Liu, and Tian Wang, "Dependable Structural Health Monitoring Using Wireless Sensor Networks," IEEE Transactions on Dependable and Secure Computing, 14(4): 363-376, August 2017.
||As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming an increasingly compelling platform for engineering structural health monitoring (SHM) due to relatively low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called DependSHM) and then examining its ability to cope with sensor faults and constraints. We find evidence that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by one or more faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. In DependSHM, we present a distributed automated algorithm to detect such types of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through comprehensive simulations and a WSN prototype system implementation, we evaluate the effectiveness of DependSHM.
||[SCI&EI检索，IF: 2.926，CCF A类, JCR 1区, 计算机科学，ESI高被引论文（记录时间：2018年1月）]
||Ziran Peng, Guojun Wang*, Huabin Jiang, Shuangwu Meng, "Research and Improvement of ECG Compression Algorithm based on EZW," Computer Methods and Programs in Biomedicine, 145: 157-166, July 2017.
||Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces signal compression ratio. This paper studies the optimization of the EZW compression algorithm and aims to improve it. First, we used lifting wavelet transformation to process electrocardiograph (ECG) signals, focusing on the lifting algorithm. Second, we utilized the EZW compression coding algorithm, through the ECG information decomposition to deter- mine the feature detection value. Then, according to the feature information, we weighted the wavelet coefficients of ECG (through the coefficient as a measure of weight) to achieve the goal of improved compression benefit.
||[SCI&EI检索，IF: 2.503, JCR 2区, 计算机科学]
||Wenyin Yang, Guojun Wang*, Md Zakirul Alam Bhuiyan, and Kim-Kwang Raymond Choo, "Hypergraph Partitioning for Social Networks Based on Information Entropy Modularity," Journal of Network and Computer Applications, 86: 59-71, May 2017.
||A social network is a typical scale-free network with power-law degree distribution characteristics. It demonstrates several natural imbalanced clusters when it is abstracted as a graph, and expands quickly under its generative mechanism. Hypergraph is superior for modeling multi-user operations in social networks, and partitioning the hypergraph modeled social networks could ease the scaling problems. However, today's popular hypergraph partitioning tools are not sufficiently scalable; thus, unable to achieve high partitioning quality for naturally imbalanced datasets. Recently proposed hypergraph partitioner, hyperpart, replaces the balance constraint with an entropy constraint to achieve high-fidelity partitioning solutions, but it is not tailored for scale-free networks, like social networks. In order to achieve scalable and high quality partitioning results for hypergraph modeled social networks, we propose a partitioning method, EQHyperpart, which utilizes information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. This EQ considers power-law degree distribution while describing the “natural” structure of scale-free networks. We then apply simulated annealing and introduce a new definition of hyperedge cut, micro cut, to avoid the local minima in convergence of partitioning, developing EQHyperpart into two specific partitioners, namely: EQHyperpart-SA and EQHyperpart-MC. Finally, we evaluate the utility of our proposed method using classical social network datasets, including Facebook dataset. Findings show that EQHyperpart partitioners are more scalable than competing approaches, achieving a tradeoff between modularity retaining and cut size minimizing under balance constraints, and an auto-tradeoff without balance constraints for hypergraph modeled social networks.
||[SCI&EI检索，IF: 3.5，CCF C类, JCR 1区, 计算机科学]
||Qin Liu, Guojun Wang*, Feng Li, Shuhui Yang, and Jie Wu, "Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks," IEEE Transactions on Parallel and Distributed Systems, 28(5): 1417-1429, May 2017.
||The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target’s one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node’s 1-neighborhood graph is isomorphic with k 1 other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key privacy property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
||提出了一种社会网络数据的安全发布方案。提出了节点之间概率不可区分的概念，设计了一个启发式算法更好地抵抗1-neighborhood攻击。启发式算法首先将满足某些条件的节点进行分组，使得每个分组的节点至少为k个。然后，利用随机漫步（Random Walk）测试一个分组内的节点的1-neighborhood图是否匹配，对于不匹配的节点，利用启发式匿名算法将两个节点图做相似处理。最后，利用一定概率p随机修改每个节点的1-neighborhood图。通过对真实社会数据（Arxiv ASTRO-PH collaboration network）进行实验，验证了方案的有效性和可行性。
||[SCI&EI检索，IF: 4.181, CCF A类, JCR 1区, 计算机科学]
||Ziran Peng, Guojun Wang*, "An Optimal Energy-Saving Real-Time Task-Scheduling Algorithm for Mobile Terminals," International Journal of Distributed Sensor Networks, 13(5), 12 pages, May 2017.
||This article discusses the principles, mechanisms, and strategy of hard real-time task scheduling appropriate for mobile terminal equipment. Mobile terminals have timeliness requirements for completing hard real-time tasks and also clear energy-management requirements. Therefore, this study attempts to schedule tasks under these two constraints to achieve an optimal level of energy savings. First, terminal equipment operating time and standby time should meet the maximum requirements, and second, all tasks should meet the constraints of real-time parallel scheduling. We propose a scheduling strategy based on grouping according to the latest cut-off time, with each group adopting a dynamic optimiza- tion strategy to make scheduling decisions. The feasibility and validity of this algorithm are demonstrated through experi- ments and simulations.
||[SCI&EI检索，IF: 1.239, JCR 3区, 计算机科学]
||Md Zakirul Alam Bhuiyan, Jie Wu, Guojun Wang*, Tian Wang, and Mehedi Hassan, "e-Sampling: Event-Sensitive Autonomous Adaptive Sensing and Low-cost Monitoring in Networked Sensing Systems," ACM Transactions on Autonomous and Adaptive Systems, 12(1): 1, May 2017.
||Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including wireless sensor networks (WSNs). Existing algorithms are primarily employed to detect events such as ob- jects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a net- work unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This paper studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed “event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)” by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automat- ically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identi- fication algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the “uninteresting” data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad-range of applications. We apply e-Sampling to structural health mon- itoring (SHM) and fire event monitoring (FEM), which are typical applications of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications.
||[SCI&EI检索，IF: 2.133，CCF B类, JCR 2区, 计算机科学]
||Tao Peng, Qin Liu, Dacheng Meng, and Guojun Wang*, "Collaborative Trajectory Privacy Preserving Scheme in Location-based Services," Information Sciences, 387: 165-179, May 2017.
||Location-based services (LBSs) have been gaining considerable popularity and are becom- ing the fastest growing activity-related services that people use in their daily life. While users benefit from LBSs, the collection and analysis of participators’ location data and tra- jectory information may jeopardize their privacy. Existing proposals focus mostly on snap- shot queries. However, privacy preservation in continuous LBSs is more challenging than in snapshot queries because adversaries could use the spatial and temporal correlations on the user trajectory to infer the user’s private information. In this paper, we propose the collaborative trajectory privacy preserving (CTPP) scheme for continuous queries, in which trajectory privacy is guaranteed by caching-aware collaboration between users, without the need for any fully trusted entities. The main idea of our scheme is to obfuscate the actual trajectory of a user by issuing fake queries to confuse the LBS adversary. We first present a multi-hop caching-aware cloaking algorithm to collect valuable information from multi- hop peers based on collaborative caching. Then, we describe a collaborative privacy pre- serving querying algorithm that issues a fake query to confuse the location service provider (LSP). Extensive experimental results verify the effectiveness and efficiency of our scheme in terms of processing time and communication cost.
||提出了一种基于用户合作的轨迹隐私保护方法（Collaborative Trajectory Privacy Preserving，CTPP）。针对位置服务中的用户轨迹隐私，通过向移动用户周围多跳邻居搜寻有价值的信息构造匿名区域，并发布假查询以扰乱真实轨迹中的查询时间和变换查询区域位置，使得攻击者很难重构用户轨迹。(
||[SCI&EI检索，IF:4.832, CCF B类, JCR 1区, 计算机科学]
||Shuhong Chen, Guojun Wang*, Guofeng Yan, Dongqing Xie, "Multi-dimensional Fuzzy Trust Evaluation for Mobile Social Networks based on Dynamic Community Structures," Concurrency and Computation: Practice and Experience, 29(7): 1-22, April 2017.
||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 evalua- tion 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 communi- ties/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 . 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 fac- tor, 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.
||[SCI&EI检索，IF: 1.133, CCF C类, JCR 3区, 计算机科学]
||Qin Liu, Guojun Wang*, Xuhui Liu, Tao Peng, and Jie Wu, "Achieving Reliable and Secure Services in Cloud Computing Environments," Computers and Electrical Engineering, 59: 153-164, April 2017.
||In cloud computing environments, resources stored on the cloud servers are transmitted in the form of data flow to the clients via networks. Due to the real-time and ubiquitous re- quirements of cloud computing services, how to design a sophisticated transmission model to ensure service reliability and security becomes a key problem. In this paper, we first propose a Comprehensive Transmission (CT) model, by combining the Client/Server (C/S) mode and the Peer-to-Peer (P2P) mode for reliable data transmission. Then, we design a Two-Phase Resource Sharing (TPRS) protocol, which mainly consists of a pre-filtering phase and a verification phase, to efficiently and privately achieve authorized resource sharing in the CT model. Extensive experiments have been conducted on the synthetic data set to verify the feasibility of our protocol.
||[SCI&EI检索，IF:1.57, JCR 3区, 计算机科学]
||Entao Luo, Qin Liu, Jemal H. Abawajy, and Guojun Wang*, "Privacy-Preserving Multi-Hop Profile-Matching Protocol for Proximity Mobile Social Networks," Future Generation Computer Systems, 68: 222-223, March 2017.
||Proximity-based mobile social networks (PMSNs) enable users to easily discover and foster social interactions with others through user-profile matching. The user profiles in PMSNs contain sensitive personal information and an occasional leak will violate people’s privacy. Hence, it is a major concern. In this paper, we propose a privacy-preserving multi-hop profile-matching protocol for PMSNs. The proposed protocol allows users to customize their own matching preference and to make the matching results more precise. Unlike the state-of-the-art profile matching approaches that focus only within a single-hop, the proposed approach makes profile matching within several hops. Moreover, analysis of the security and performance indicates that the proposed protocol achieves secure and privacy-preserving friend discovery with higher efficiency. It executes in less time and consumes less energy than other related protocols.
||[SCI&EI检索，IF: 3.997，CCF C类, JCR 1区, 计算机科学]
||Feng Wang, Jianbin Li*, Wenjun Jiang, and Guojun Wang*, "Temporal Topic-Based Multi-Dimensional Social Influence Evaluation in Online Social Networks," Wireless Personal Communications, 95(3): 2143-2171, February 2017.
||Analytical applications in online social networks can be generalized as the influence evaluation problem, which targets at finding most influential users. Nowadays social influence evaluation is still an open and challenging issue. Most influence evaluation models focus on the single dimensional evaluation factor but fail to research on the multi- dimensional factors. In this paper, we propose a novel influence evaluation model: the temporal topic influence (TTI) evaluation model, which is a time-aware, content-aware and structure-aware evaluation model. For the aim of multi-dimensional evaluation, we incorporate multi-dimensional measure factors into our model, including the time factor, the topological information and the topic distribution information, etc. We propose a novel concept of user gravitational ability which is inspired by Newton’s law of universal gravitation. It can integrate multi-dimensional factors in an appropriate way. Our experi- ments are conducted on the Sina Weibo data set. Through the experimental analysis, we prove TTI model can calculate users’ influences effectively and efficiently. The TTI model can distinguish the value of users’ influences. And the TTI model identifies the top-k influential users with higher quality. We also validate the effect of time and topic measure factors in the influence evaluation process.
||[SCI&EI检索，IF: 0.951, JCR 4区, 计算机科学]
||Sancheng Peng, Guojun Wang*, and Dongqing Xie, "Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges," IEEE Network, 33(1): 11-17, January 2017.
||Social influence analysis has become one of the most important technologies in modern information and service industries. It will de nite- ly become an essential mechanism to perform complex analysis in social networking big data. It is attracting an increasing amount of research ranging from popular topics extraction to social influence analysis, including analysis and pro- cessing of big data, social influence evaluation, influential users identification, and information diffusion modeling. We provide a comprehen- sive investigation of social in uence analysis, and discuss the characteristics of social in uence and the architecture of social in uence analysis based on social networking big data. The relationship between big data and social influence analysis is also discussed. In addition, research challeng- es relevant to real-world issues based on social networking big data in social influence analysis are discussed, focusing on research issues such as scalability, data collection, dynamic evolution, causal relationships, network heterogeneity, eval- uation metrics, and effective mechanisms. Our goal is to provide a broad research guideline of existing and ongoing efforts via social influence analysis in large-scale social networks, and to help researchers better understand the existing work, and design new algorithms and methods for social in uence analysis.
||[SCI&EI检索，IF:7.23, JCR 1区, 计算机科学]
||Tao Peng, Qin Liu, and Guojun Wang*, "A Multilevel Access Control Scheme for Data Security in Transparent Computing," IEEE Computing in Science and Engineering, 19(1): 46-53, January/February 2017.
||The Multilevel Access Control Scheme in Transparent Computing (MACTC) can protect user data by providing different security levels, while offering multilevel access control and valid identity authentication. The proposed scheme is effective in multilevel data security, exible in authorized resource sharing, and secure against various malicious attacks.
||提出了透明计算环境下多安全级别的访问控制方法（The Multilevel Access Control Scheme in Transparent 设计了一个基于无线传感器网络的建筑体健康事件监测信息物理系统，并提出了一个新的基于模型的网内决策支持框架MODEM。该框架针对通用的事件监测结构利用传感器节点进行感知并对复杂事件进行简单的本地决策。针对工程建筑中大型物理建筑由一系列子建筑结构组成这一现象，该框架将无线传感器节点进行分组并利用组内的最终决策来独立处理建筑体子结构中的事件监测问题。该框架是分布式的，并能够达到与原始的基于有线的监测架构相近的监测质量，与以往的监测模型相比，该框架中数据传输和计算的能耗更小。该框架的有效性通过模拟实验和真实实验进行了证实。Computing，MACTC）。利用透明计算跨平台的特点，结合身份认证方法实现不同安全级别数据访问控制。在认证服务器上引入多项式的方法动态地管理不同安全级别数据的访问控制及授权用户的更改和撤销。
||[SCI&EI检索，IF: 2.074, JCR 2区, 计算机科学]
||Entao Luo, Qin Liu, Guojun Wang*, "Hierarchical Multi-authority and Attribute-based Encryption Friend Discovery Scheme in Mobile Social Networks," IEEE Communications Letters, 20(9): 1772-1775, June 2016.
||In mobile social networks, to guarantee the secu- rity and privacy in the friend discovery process, we propose a hierarchical multi-authority and attribute-based encryption (ABE) friend discovery scheme based on ciphertext-policy (CP)- ABE. It employs character attribute subsets to achieve flexible fine-grained access control, which solves the problem of single- point failure and performance bottleneck. Performance analysis demonstrates the superiority of our scheme in terms of system initialization time and key generation time.
||[SCI&EI检索，IF:1.988, JCR 2区, 计算机科学]
||Li Li, Wang Yang, Md. Zakirul Alam Bhuiyan, Guojun Wang, "Unsupervised Learning of Indoor Localization based on Received Signal Strength," Wireless Communications and Mobile Computing, 16(15): 2225-2237, May 2016.
||Most indoor wireless sensor network localization methods require costly site surveys to collect fingerprint information for later comparison. Moreover, due to the dynamic nature of fingerprint information in indoor wireless environments, the need for site surveys may be ongoing. In this work, indoor localization is addressed with an unsupervised learning algorithm. Our novel algorithm based on received signal strength combines the information conveyed by both range-based and range-free localization with state-of-art optimization techniques. A specially designed hierarchical Bayesian hidden Markov model coupled with a particle filter helps mitigate non-line-of-sight and multipath errors. This grid-based data sample process, derived from the theory of Dirichlet processes, simplifies the global optimization problem of unsupervised learning by employing a single initial hyper-parameter. Meanwhile, for obtaining accurate coordinates of mobile nodes, a unique semidefinite programming method is used to provide feedback to the radio propagation model. This feedback step can enable the grid-based algorithms not only to establish the coordinates of a mobile node, but also to optimize the accuracy iteratively. Theoretical and experimental analyses indicate that the proposed algorithm can achieve better localization accuracy than conventional range-based algorithms without adding computation cost.
||提出了一种混合使用rang-based算法和无监督学习算法解决无线网络室内定位问题的新算法。比较了rang-free算法、rang-based算法和无监督学习算法三种算 法的优缺点。针对无监督学习的局部优化问题，利用Dirichlet分布改进求解算法。另外针对无监督学习算法的实时定位问题，使用PF算法避免模型中状态长间隔依赖的问题。利用了基于核密度估计的计算方法，用于计算数据与位置变量的似然概率。对于混合模型中的层次贝叶斯隐马尔科夫模型，用合理的参数设计和函数选择满足了将聚类方法， 迭代方法，rang-based方法，核密度方法，运动模型，概率模型嵌入到同一个模型中的任务，设计了Gibbs采样算法进行推导求解。为更好地将无监督学习方法的结果反馈到rang-based算法，以让rang-free算法的定位结果和无监督学习算法的定位结果差距最小为优化目标，建立了一种最大似然估计模型，并将其转化为二阶锥规划问题，进而转化为半正定规划，运用启发式算法进行求解。通过实验，验证了算法的有效性和可行性。
||[SCI&EI检索，IF: 1.899, CCF C, JCR 2区, 计算机科学]
||Wenjun Jiang, and Guojun Wang*, Md Zakirul Alam Bhuiyan, and Jie Wu, "Understanding Graph-based Trust Evaluation in Online Social Networks: Methodologies and Challenges," ACM Computing Surveys, 49(1), Article 10, May 2016.
||Online social networks (OSNs) are becoming a popular method of meeting people and keeping in touch with friends. OSNs resort to trust evaluation models and algorithms, as to improve service qualities and enhance user experiences. Much research has been done to evaluate trust and predict the trustworthiness of a target, usually from the view of a source. Graph-based approaches make up a major portion of the existing works, in which the trust value is calculated through a trusted graph (or trusted network, web of trust, multiple trust chains). In this paper, we focus on graph-based trust evaluation models in OSNs, particularly in computer science literature. We first summarize the features of OSNs and the properties of trust. Then, we comparatively review two categories of graph-simplification based and graph-analogy based approaches, and discuss their individual problems and challenges. We also analyze the common challenges of all graph-based models. To provide an integrated view of trust evaluation, we conduct a brief review of its pre-and-post processes, i.e., the preparation and the validation of trust models, including information collection, performance evaluation, and related applications. Finally, we identify some open challenges that all trust models are facing.
||[SCI&EI检索，IF:6.748, JCR 1区, 计算机科学]
||Li Li, Wang Yang, and Guojun Wang, "Intelligent Fusion of Information Derived from Received Signal Strength and Inertial Measurements for Indoor Wireless Localization," AEU-International Journal of Electronics and Communications, 70(9): 1105-1113, April 2016.
||In this paper, we focus on improving the accuracy of wireless localization in wireless sensor networks using information derived from the inertial measurement unit (IMU) in a smartphone and the received signal strength (RSS). We propose an algorithm to relate the RSSs and measurements obtained from the IMU to the coordinates of an indoor robot. To deal with the dynamic nature of fingerprint information in an indoor radio environment, we first use the hierarchical Bayesian hidden Markov model (HB-HMM) to process a time series of RSSs. Unlike other HMM-based methods, the HB-HMM depends only on a single initial hyper-parameter for global optimization. Next, we evaluate the measurements obtained from the IMU to identify the robot’s state, which includes the rotating, moving, and bumping states. We used the IMU accelerometers to estimate the velocity. Lastly, a method based on the particle filter (PF) was used to fuse the results obtained from RSS and IMU. Experiments show that our algorithm can achieve better accuracy than related algorithms.
||[SCI&EI检索, IF: 1.147, JCR 4区, 计算机科学]
||Zheng Ma, Jin Zheng, Weijia Jia, Guojun Wang*, "An Efficient Spatial Query Processing Algorithm in Multi-sink Wireless Sensor Networks," International Journal of Sensor Networks, 22(4): 274-282, April 2016.
||In order to address the problem of energy and time-efficient execution of spatial queries in multi-sink wireless sensor networks, an efficient hybrid spatial query processing algorithm (EHSQP) is proposed in this paper. In EHSQP, sink nodes perform the sector selection for the query region and seek the query route to a query region using the known infrastructure information. Sensor nodes can adjust the query dissemination and reversing route for query results based on the available local information. To minimise the energy consumption and the response time, the proposed EHSQP algorithm ensures that only the relevant nodes are involved in the query execution. Experimental results show that the proposed algorithm reduces communication cost significantly, and saves energy and time very effectively for the connected sensors in the given region. The proposed technique has an advantage over other techniques in terms of energy and time-efficient query cover with lower communication cost.
||[SCI&EI检索，IF: 0.635, JCR 4区, 计算机科学]
||Shui Yu, Guojun Wang, and Wanlei Zhou, "Modeling Malicious Activities in Cyber Space," IEEE Network, 29(6): 83-87, December 2015.
||Cyber attacks are an unfortunate part of society as an increasing amount of critical infrastructure is managed and controlled via the Internet. In order to protect legit- imate users, it is critical for us to obtain an accurate and timely understanding of our cyber opponents. However, at the moment we lack effective tools to do this. In this article we summarize the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models, and present promising directions for possible efforts in the near future.
||[SCI&EI检索，IF:7.23, JCR 1区, 计算机科学]
||Tian Wang, Yiqiao Cai, Weijia Jia, Sheng Wen, Guojun Wang, Hui Tian, Yonghong Chen, and Bineng Zhong, "Maximizing Real-time Streaming Services based on A Multi-servers Networking Framework," Computer Networks, 93(1): 199-212, December 2015.
||In recent years, we have witnessed substantial exploitation of real-time streaming applica- tions, such as video surveillance system on road crosses of a city. So far, real world applica- tions mainly rely on the traditional well-known client–server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the process- ing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi- server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real- time streams delivery and storage. Based on this framework, we further introduce a (1 − ε) approximation algorithm to solve the NP-complete “maximum services” (MS) problem with the intention of handling large number of streaming flows originated by networks and max- imizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on sim- ulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme signifi- cantly outperforms the traditional solutions.
||[SCI&EI检索，IF: 2.516，CCF B类, JCR 1区, 计算机科学]
||Zhenyu Zhou, Mianxiong Dong, Kaoru Ota, Guojun Wang, and Laurence Yang, "Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN based LTE-A Networks," IEEE Internet of Things Journal, 3(3): 428-438, November 2015.
||Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) net- works based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deploy- ment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint chan- nel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a con- vex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algo- rithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simu- lations, and the implementation issues and complexity analysis are discussed in detail.
||[SCI&EI检索，IF: 7.596, JCR 1区, 计算机科学，ESI高被引论文（记录时间：2018年1月）]
||Xiaofei Xing, Dongqing Xie, and Guojun Wang, "Energy-Balanced Data Gathering and Aggregating in WSNs: A Compressed Sensing Scheme," International Journal of Distributed Sensor Networks, 2015: 1-10, January 2015.
||Compressed sensing (CS) is an emerging sampling technique by which the data sampling and aggregating can be done simultaneously, which can be applied to many elds, including data processing in wireless sensor networks (WSNs). In WSNs, data aggregating can reduce data transmission cost and improve energy e ciency. Existing CS-based data gathering work in WSNs utilizes the centralized method to process the data by a sink node, which causes the load imbalance and “coverage hole” problems, and so forth. In this paper, we propose an energy-balanced data gathering and aggregating (EDGA) scheme that integrates a clustering hierarchical structure with the CS to optimize and balance the amount of data transmitted. We also design a data reconstruction algorithm to perform data recovery tasks by utilizing the orthogonal matching pursuit theory, which helps to reconstruct the original data accurately and e ectively at sink node. e advantages of the proposed scheme compared with other state-of-the-art related methods are measured on the metrics of data recovery ratio and energy e ciency. We implement our scheme on a simulation platform using a real dataset from Intel lab. Simulation results demonstrate that the proposed data gathering and aggregating scheme guarantees accurate data reconstruction performance and obtains energy e ciency signi cantly compared to existing methods.
||[SCI&EI检索，IF: 1.239, JCR 3区, 计算机科学]
||Xiaofei Xing, Guojun Wang*, Jie Li, "Collaborative Target Tracking in Wireless Sensor Networks, "Ad Hoc & Sensor Wireless Networks, 23(1/2): 117-135, October 2014.
||Target tracking is a killer application in wireless sensor networks. A lot of work has been done to improve the localization and tracking algo- rithms with smart sensors. However, achieving a high tracking accuracy with energy efficiency is challenging. In this paper, we propose a col- laborative target tracking (CTT) scheme that enables accurate tracking with a binary detection model. In this scheme, all sensors in the moni- toring field are divided into clusters using a clustering algorithm. This scheme consists of three parts: (i) A tracking node group near the tar- get is first constructed when the distance weights of the sensors meet the requirements of tracking accuracy; (ii) A tracking group is updated dynamically with the moving of the target; (iii) A tracking group pre- dicts the target trajectory and adjusts the frequency of data reporting according to the target’s velocity. Extensive simulation results show that our scheme achieves high performance in terms of tracking accuracy and energy efficiency under different settings.
||[SCI&EI检索，IF: 1.034, JCR 4区, 计算机科学]
||Jin Zheng, Md Zakirul Alam Bhuiyan, Shaohua Liang, Xiaofei Xing, and Guojun Wang*, "Auction-Based Adaptive Sensor Activation Algorithm for Target Tracking in Wireless Sensor Networks," Future Generation Computer Systems, 39: 88-99, October 2014.
||Due to the severe resource constraints in wireless sensor networks (WSNs), designing an efficient target tracking algorithm for WSNs in terms of energy efficiency and high tracking quality becomes a challenging issue. WSNs usually provide centralized information, e.g., the locations and directions of a target, choosing sensors around the target, etc. However, some ready strategies may not be used directly because of high communication costs to get the responses for tracking tasks from a central server and low quality of tracking. In this paper, we propose a fully distributed algorithm, an auction-based adaptive sensor activation algorithm (AASA), for target tracking in WSNs. Clusters are formed ahead of the target movements in an interesting way where the process of cluster formation is due to a predicted region (PR) and cluster members are chosen from the PR via an auction mechanism. On the basis of PR calculation, only the nodes in the PR are activated and the rest of the nodes remain in the sleeping state. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of nodes are adaptively adjusted according to current tracking quality. Instead of fixed interval (usually used in existing work), tracking interval is also dynamically adapted. Extensive simulation results, compared to existing work, show that AASA achieves high performance in terms of quality of tracking, energy efficiency, and network lifetime.
||[SCI&EI检索，IF: 3.997, CCF C类, JCR 1区, 计算机科学]