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    Guest Editorial
  • Guest Editorial
    Cooperative Vehicular Content Distribution in Edge Computing Assisted 5G-VANET
    Guiyang Luo, Quan Yuan, Haibo Zhou, Nan Cheng, Zhihan Liu, Fangchun Yang, XueminShen
    2018, 15(7): 1-17.
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    By leveraging the 5G enabled vehicular ad hoc network (5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.
  • Guest Editorial
    Deterministic and Stochastic Simulators for Non-Isotropic V2V-MIMO Wideband Channels
    Yiran Li, Xiang Cheng, Nan Zhang
    2018, 15(7): 18-29.
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    In this paper, we consider a novel two-dimensional (2D) geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) wideband fading channels. The proposed model employs the combination of a two-ring model and a multiple confocal ellipses model, where the signal is sum of the line-of-sight (LoS) component, single-bounced (SB) rays, and double-bounced (DB) rays. Based on the reference model, we derive some expressions of channel statistical properties, including space-time correlation function (STCF), Doppler spectral power density (DPSD), envelope level crossing rate (LCR) and average fade duration (AFD). In addition, corresponding deterministic and stochastic simulation models are developed based on the reference model. Moreover, we compare the statistical properties of the reference model and the two simulation models in different scenarios and investigate the impact of different vehicular traffic densities (VTDs) on the channel statistical properties of the proposed model. Finally, the great agreement between simulation models and the reference model demonstrates not only the utility of simulation models, but also the correctness of theoretical derivations and simulations.
  • Guest Editorial
    A Geometry-Based Non-Stationary MIMO Channel Model for Vehicular Communications
    Yuanyuan Ma, Lei Yang, Xianghan Zheng
    2018, 15(7): 30-38.
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    This paper derives a non-stationary multiple-input multiple-output(MIMO) from the one-ring scattering model. The proposed channel model characterizes vehicular radio propagation channels with considerations of moving base and mobile stations, which makes the angle of arrivals (AOAs) along with the angle of departures (AODs) time-variant. We introduce the methodology of including the time-variant impacts when characterizing non-stationary radio propagation channels through the geometrical channel modelling approach. We analyze the statistical properties of the proposed channel model including the local time-variant autocorrelation function (ACF) and the space cross-correlation functions (CCFs). We show that the model developed in this paper for non-stationary scenarios includes the existing one-ring wide-sense stationary channel model as its special case.
  • Guest Editorial
    Joint Channel Estimation and Decoding Design for 5G-Enabled V2V Channel
    Xuanfan Shen, Yong Liao, Xuewu Dai, Ming Zhao, Kai Liu, Dan Wang
    2018, 15(7): 39-46.
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    This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular (V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing (OFDM) system, we perform extended Kalman filter (EKF) for channel estimation in conjunction with Iterative Detector & Decoder (IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance.
  • Guest Editorial
    Interference-Free Pilot Design and Channel Estimation Using ZCZ Sequences for MIMO-OFDM-Based C-V2X Communications
    Haibin Chen, Rongqing Zhang, Wenjun Zhai, Xiaoli Liang, Guojuan Song
    2018, 15(7): 47-54.
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    Cellular vehicle-to-everything (C-V2X) communications is regarded as a promising and feasible solution for 5G-enabled vehicular communications and networking. In this paper, we investigate the pilot design and channel estimation problem in MIMO-OFDM-based C-V2X systems with severe co-channel interference due to spectrum reusing among different V2X communication links. By using zero-correlation zone (ZCZ) sequences, we provide an interference-free pilot design scheme and a corresponding time-domain (TD) correlation-based channel estimation (TD-CCE) method. We employ the ZCZ sequences from the same family set to be designed as the TD pilot symbols and guarantee the pilot sequeneces for neighboring V2X communication links are code-division multiplexing (CDM). The co-channel pilot interference of the deisgned pilot symbols can be effectively eliminated by exploiting the provided TD-CCE method. Simulation results indicate that the accuracy of channel estimation can be effectively improved by the proposed scheme, whose performance is close to that of the non-interference situation.
  • Guest Editorial
    A Multi-Hop Moving Zone (MMZ) Clustering Scheme Based on Cellular-V2X
    Zahid Khan, Pingzhi Fan
    2018, 15(7): 55-66.
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    A clustering scheme based on pure V2V communications has two prominent issues i.e. broadcast storm and network disconnection. The application of the fifth generation (5G) technology to vehicular networks is an optimal choice due to its wide coverage and low latency features. In this paper, a Multi-hop Moving Zone (MMZ) clustering scheme is proposed by combining IEEE 802.11p with the 3rd Generation Partnership Project (3GPP) 5G cellular technology. In MMZ, vehicles are clustered up-to three hops using V2V communications based on IEEE 802.11p aiming to reduce excessive cellular hand-off cost. While the zonal heads (ZHs) i.e. cluster heads (CHs) are selected by cellular-V2X (C-V2X) on the basis of multi-metrics i.e. relative speed, distance and link life time (LLT). The main goal of MMZ is to form stable clusters achieving high packet delivery and low latency. The simulation results using ns3 show that, 5G wide range technology significantly improves the stability of MMZ in term of ZH duration and change rate. The average Data Packet Delivery Ratio (DPDR) and E2E latency are also improved as compared to the existing clustering schemes.
  • Guest Editorial
    Vehicular Beacon Broadcast Scheduling Based on Age of Information (AoI)
    Yuanzhi Ni, Lin Cai, Yuming Bo
    2018, 15(7): 67-76.
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    Vehicular beaconing plays an important role to facilitate various applications in the paradigm of Internet of Vehicles (IoV). Due to high dynamic and resource limitation in IoV, how to schedule the vehicular beacon broadcast is challenging, especially in dense scenarios. In this paper, we investigate the beacon broadcast scheduling problem considering the Age of Information (AoI). We first propose an algorithm minimizing the expected sum of AoI considering the limited communication resource and vehicle mobility. Then the performance of the proposed algorithm is analyzed. With the proposed algorithm, the optimal solution can be obtained under certain conditions. Extensive simulations are conducted to verify the efficiency, effectiveness and fairness of the proposed solution.
  • Guest Editorial
    A Greedy Traffic Light and Queue Aware Routing Protocol for Urban VANETs
    Yangyang Xia, Xiaoqi Qin, Baoling Liu, Ping Zhang
    2018, 15(7): 77-87.
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    Vehicular Ad-hoc Networks (VANETs) require reliable data dissemination for time-sensitive public safety applications. An efficient routing protocol plays a vital role to achieve satisfactory network performance. It is well known that routing is a challenging problem in VANETs due to the fast-changing network typology caused by high mobility at both ends of transmission. Moreover, under urban environment, there are two non-negligible factors in routing protocol design, the non-uniform vehicle distribution caused by traffic lights, and the network congestion due to high traffic demand in rush hours. In this paper, we propose a greedy traffic light and queue aware routing protocol (GTLQR) which jointly considers the street connectivity, channel quality, relative distance, and queuing delay to alleviate the packet loss caused by vehicle clustering at the intersection and balance the traffic load among vehicles. Through performance evaluation, we show that our proposed protocol outperforms both TLRC and GLSR-L in terms of packet delivery ratio and end-to-end delay.
  • Guest Editorial
    Proactive Content Delivery for Vehicles over Cellular Networks: the Fundamental Benefits of Computing and Caching
    Jiping Jiao, Xuemin Hong, Jianghong Shi
    2018, 15(7): 88-97.
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    The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content demand brings significant challenges for the fifth generation (5G) cellular communication network. To cope with the challenge of massive content delivery, previous studies suggested that the 5G mobile edge network should be designed to integrate communication, computing, and cache (3C) resources to enable advanced functionalities such as proactive content delivery and in-network caching. However, the fundamental benefits achievable by computing and caching in mobile communications networks are not yet properly understood. This paper proposes a novel theoretical framework to characterize the tradeoff among computing, cache, and communication resources required by the mobile edge network to fulfill the task of content delivery. Analytical and numerical results are obtained to characterize the 3C resource tradeoff curve. These results reveal key insights into the fundamental benefits of computing and caching in vehicular mobile content delivery networks.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability
    Lejun Zhang, Tong Wang, Zilong Jin, Nan Su, Chunhui Zhao, Yongjun He
    2018, 15(7): 98-110.
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    Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm (POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.
  • NETWORKS & SECURITY
    Recommending Authors and Papers Based on ACTTM Community and Bilayer Citation Network
    Meilian Lu, Zhihe Qu, Mengxing Wang, Zhen Qin
    2018, 15(7): 111-130.
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    Citation network is often used for academic recommendation. However, it is difficult to achieve high recommendation accuracy and low time complexity because it is often very large and sparse and different citations have different purposes. What’s more, some citations include unreasonable information, such as in case of intentional self-citation. To improve the accuracy of citation network-based academic recommendation and reduce the time complexity, we propose an academic recommendation method for recommending authors and papers. In which, an author-paper bilayer citation network is built, then an enhanced topic model, Author Community Topic Time Model (ACTTM) is proposed to detect high quality author communities in the author layer, and a set of attributes are proposed to comprehensively depict the author/paper nodes in the bilayer citation network. Experimental results prove that the proposed ACTTM can detect high quality author communities and facilitate low time complexity, and the proposed academic recommendation method can effectively improve the recommendation accuracy.
  • NETWORKS & SECURITY
    A Controller-Based Architecture for Information Centric Network Construction and Topology management
    Zeinab Zali, Massoud Reza Hashemi, Ilaria Cianci, Alfredo Grieco, Gennaro Boggia
    2018, 15(7): 131-145.
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    Information-Centric Networking (ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking (SDN). The SDN controller manages the content distribution, caching, and routing based on the users’ requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.
  • NETWORKS & SECURITY
    Calculate Joint Probability Distribution of Steady Directed Cyclic Graph with Local Data and Domain Casual Knowledge
    Qin Zhang, Kun Qiu, Zhan Zhang
    2018, 15(7): 146-155.
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    It is desired to obtain the joint probability distribution (JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph (DAG). However, steady directed cyclic graphs (DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph (S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.
  • NETWORKS & SECURITY
    Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition
    Minsheng Ma, Ruimin Hu, Shihong Chen, Jing Xiao, Zhongyuan Wang
    2018, 15(7): 156-167.
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    Backgroundsubtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition (LRSD) methods offer an appropriate framework for background modeling, they fail to account for image’s local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.
  • NETWORKS & SECURITY
    Design and Implementation of an Adaptive Feedback Queue Algorithm over OpenFlow Networks
    Jiawei Wu, Xiuquan Qiao, Junliang Chen
    2018, 15(7): 168-179.
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    The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service (QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback (PPMF) queues based on software defined networks (SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.
  • NETWORKS & SECURITY
    Exploiting Geo-Social Correlations to Improve Pairwise Ranking for Point-of-Interest Recommendation
    Rong Gao, Jing Li, Bo Du, Xuefei Li, Jun Chang, Chengfang Song, Donghua Liu
    2018, 15(7): 180-201.
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    Recently, as location-based social network (LBSN) rapidly grow, point-of-interest (POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user’s preferences very accurately, because the users are most concerned with the list of ranked point-of-interests (POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model (GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users’ geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.
  • NETWORKS & SECURITY
    Secret Key Generation Based on Two-Way Randomness for TDD-SISO System
    Shengjun Zhang, Liang Jin, Yangming Lou, Zhou Zhong
    2018, 15(7): 202-216.
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    A novel secret key generation (SKG) method based on two-way randomness is proposed for TDD-SISO system. The legitimate transceivers mutually transmit their own random signal via reciprocal wireless channel, then the multiplication of transmitted and received signal is used as common randomness to generate secret keys. In quasi-static channel, the theoretical SKG rates (SKGRs) of the three SKG methods, namely wireless channel based, one-way randomness and two-way randomness, are derived and compared. Further, two practical SKG schemes based on two-way randomness, Scheme-1bit and Scheme-3bit, are completely designed and simulated. Generally, Scheme-1bit applies to low signal to noise ratio (SNR) scenarios and achieves 0.13~0.86bit/Ts SKGR and 10-2~10-5 level secret key outage probability (SKOP), while Scheme-3bit fits high SNR situation and obtains 0.93~1.35bit/Ts SKGR and 10-3~10-4 level SKOP. At last, the national institute of standards and technology (NIST) test is conducted to evaluate the secret key randomness (SKRD) and the test results show that both of the proposed schemes have passed the test.