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    SERVICES AND COMMUNICATIONS IN FOG COMPUTING
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Proactive Personalized Services Through Fog-Cloud Computing in Large-Scale IoT-Based Healthcare Application
    Shuqing He, Bo Cheng, Haifeng Wang, Yuze Huang, Junliang Chen
    2017, 14(11): 1-16.
    Abstract ( )   Knowledge map   Save
    With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing (CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named FogCepCare. Experimental result shows that FogCepCare is superior to the traditional IoT-based healthcare application.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    RT-Notification: A Novel Real-Time Notification Protocol for Wireless Control in Fog Computing
    Li Feng, Jie Yang, Huan Zhang
    2017, 14(11): 17-28.
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    Fog computing is an emerging paradigm that has broad applications including storage, measurement and control. In this paper, we propose a novel real-time notification protocol called RT-Notification for wireless control in fog computing. RT-Notification provides low-latency TDMA communication between an access point in Fog and a large number of portable monitoring devices equipped with sensor and actuator. RT-Notification differentiates two types of controls: urgent downlink actuator-oriented control and normal uplink access & scheduling control. Different from existing protocols, RT-Notification has two salient features: (i) support real-time notification of control frames, while not interrupting ongoing other transmissions, and (ii) support on-demand channel allocation for normal uplink access & scheduling control. RT-Notification can be implemented based on the commercial off-the-shelf 802.11 hardware. Our extensive simulations verify that RT-Notification is very effective in supporting the above two features.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Charging Pile Siting Recommendations via the Fusion of Points of Interest and Vehicle Trajectories
    Yuan Kong, Jianping Wu, Ming Xu, Kezhen Hu
    2017, 14(11): 29-38.
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    By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed algorithm computes appropriate charging pile locations by: 1) mining user Points of Interest from social network; 2) mining parking sites of vehicle form GPS trajectories and 3) fusing the Points of Interest and parking sites together then clustering the fusions with our improved DBSCAN algorithm, whose clustering results indicates the final appropriate charging pile locations. Experimental results show that our proposed methods are more efficient than existing methods.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment
    Hongman Wang, Mengqi Zeng, Zijie Xiong, Fangchun Yang
    2017, 14(11): 39-47.
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    In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance
    Tieliang Gao, Bo Cheng, Junliang Chen, Ming Chen
    2017, 14(11): 48-58.
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    Recommendation system can greatly alleviate the “information overload” in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED (PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis (PLSA) to extract users’ interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users’ similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors’ ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Task Offloading Decision in Fog Computing System
    Qiliang Zhu, Baojiang Si, Feifan Yang, You Ma
    2017, 14(11): 59-68.
    Abstract ( )   Knowledge map   Save
    Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Fog Computing Architecture-Based Data Acquisition for WSN Applications
    Guangwei Zhang, Ruifan Li
    2017, 14(11): 69-81.
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    Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.
  • SERVICES AND COMMUNICATIONS IN FOG COMPUTING
    Locally Minimum Storage Regenerating Codes in Distributed Cloud Storage Systems
    Jing Wang, Wei Luo, Wei Liang, Xiangyang Liu, Xiaodai Dong
    2017, 14(11): 82-91.
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    In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating (MSR) codes and minimum bandwidth regenerating (MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating (LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group (4, 2) or (5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.
    • COVER PAPER
  • COVER PAPER
    Deep Learning for Wireless Physical Layer: Opportunities and Challenges
    Tianqi Wang, Chao-Kai Wen, Hanqing Wang, Feifei Gao, Tao Jiang, Shi Jin
    2017, 14(11): 92-111.
    Abstract ( )   Knowledge map   Save
    Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Single Source Self-Screen Jamming Elimination and Target Detection for Distributed Dual Antennas Radar System
    Qiliang Zhang, Feifei Gao, Qing Sun, Xiaobo Wang
    2017, 14(11): 112-125.
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    Detecting target echo in the existence of self-screen jamming is a challenging work for radar system, especially when digital radio frequency memory (DRFM) technique is employed that mixes the jamming and target echo both in spatial and time-frequency domain. The conventional way to solve this problem would suffer from performance degradation when physical target (PT) and false target (FT) are superposed in time. In this paper, we propose a new spatial filter according to the different correlation characteristic between PT and FT. The filter takes the ratio of expected signal power to expected jamming and noise power as the objective function under the constant filter modulus constraint. The optimal filter coefficients are derived with a generalized rayleigh quotient approach. Moreover, we analytically compute the target detection probability and demonstrate the applicability of the proposed method to the correlation coefficient. Monte Carlo simulations are provided to corroborate the proposed studies. Furthermore, the proposed method has simple architecture and low computation complexity, making it easily applied in modern radar system.
  • COMMUNICATIONS THEORIES & SYSTEMS
    3D Audio Coding Approach Based on Spatial Perception Features
    Cheng Yang, Ruimin Hu, Xiaochen Wang, Yuhong Yang, Maosheng Zhang, Wei Chen
    2017, 14(11): 126-140.
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    A new three-dimensional (3D) audio coding approach is presented to improve the spatial perceptual quality of 3D audio. Different from other audio coding approaches, the distance side information is also quantified, and the non-uniform perceptual quantization is proposed based on the spatial perception features of the human auditory system, which is named as concentric spheres spatial quantization (CSSQ) method. Comparison results were presented, which showed that a better distance perceptual quality of 3D audio can be enhanced by 5.7%~8.8% through extracting and coding the distance side information comparing with the directional audio coding, and the bit rate of our coding method is decreased of 8.07% comparing with the spatial squeeze surround audio coding.
  • COMMUNICATIONS THEORIES & SYSTEMS
    An Efficient Machine Learning Approach for Indoor Localization
    Lingwen Zhang, Yishun Li, Yajun Gu, Wenkao Yang
    2017, 14(11): 141-150.
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    Indoor localization has gained much attention over several decades due to enormous applications. However, the accuracy of indoor localization is hard to improve because the signal propagation has small scale effects which leads to inaccurate measurements. In this paper, we propose an efficient learning approach that combines grid search based kernel support vector machine and principle component analysis. The proposed approach applies principle component analysis to reduce high dimensional measurements. Then we design a grid search algorithm to optimize the parameters of kernel support vector machine in order to improve the localization accuracy. Experimental results indicate that the proposed approach reduces the localization error and improves the computational efficiency comparing with K-nearest neighbor, Back Propagation Neural Network and Support Vector Machine based methods.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Compressive Sensing Approach in Multicarrier Sparsely Indexing Modulation Systems
    Mostafa Salah, Osama A. Omer, Usama S. Mohamed
    2017, 14(11): 151-166.
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    recently the indexed modulation (IM) technique in conjunction with the multi-carrier modulation gains an increasing attention. It conveys additional information on the subcarrier indices by activating specific subcarriers in the frequency domain besides the conventional amplitude-phase modulation of the activated subcarriers. Orthogonal frequency division multiplexing (OFDM) with IM (OFDM-IM) is deeply compared with the classical OFDM. It leads to an attractive trade-off between the spectral efficiency (SE) and the energy efficiency (EE). In this paper, the concept of the combinatorial modulation is introduced from a new point of view. The sparsity mapping is suggested intentionally to enable the compressive sensing (CS) concept in the data recovery process to provide further performance and EE enhancement without SE loss. Generating artificial data sparsity in the frequency domain along with naturally embedded channel sparsity in the time domain allows joint data recovery and channel estimation in a double sparsity framework. Based on simulation results, the performance of the proposed approach agrees with the predicted CS superiority even under low signal-to-noise ratio without channel coding. Moreover, the proposed sparsely indexed modulation system outperforms the conventional OFDM system and the OFDM-IM system in terms of error performance, peak-to-average power ratio (PAPR) and energy efficiency under the same spectral efficiency.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Ergodic Rate Analysis on Applying Antenna Selection in D2D Communication Underlaying Cellular Networks
    Senjie Zhang, Yuyang Wang, Zhiqiang He, Shi Jin
    2017, 14(11): 167-184.
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    By reusing the spectrum of a cellular network, device-to-device (D2D) communications is known to greatly improve the spectral efficiency bypassing the base station (BS) of the cellular network. Antenna selection is the most cost efficient scheme for interference management, which is crucial to D2D systems. This paper investigates the achievable rate performance of the D2D communication underlaying the cellular network where a multiple-antenna base station with antenna selection scheme is deployed. We derive an exact closed-form expression of the ergodic achievable rate. Also, using Jensen’s inequality, two pairs of upper and lower bounds of the rate are derived and we validate the tightness of the two sets of bounds. Based on the bounds obtained, we analyze the ergodic achievable rate in noise-limited scenario, interference-limited high SNR scenario and larger-scale antenna systems. Our analysis shows that the presence of D2D users could be counter-productive if the SNR at cellular UE is high. Further analysis shows that the relationship between the ergodic rate and the number of antennas it positive, but keeps decreasing as the antenna number increasing. These show the inefficiency of antenna selection in D2D interference management.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Cognitive Radios: A Survey On Spectrum Sensing, Security and Spectrum Handoff
    Vishakha Ramani, Sanjay K. Sharma
    2017, 14(11): 185-208.
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    Pervasive wireless computing and communication have created an ever-increasing demand for more radio spectrum. Since, most of the spectrum is underutilized, it motivated the introduction of the concept of cognitive radios, a dynamic spectrum access enabling technology. The first stage of cognitive radio is to sense the environment and determine which parts of the spectrum are available. This is achieved through spectrum sensing. However, spectrum sensing poses the most fundamental challenge in cognitive radios. Moreover, cognitive radios suffer from many vulnerabilities and the security attacks can severely degrade the performance of cognitive radios. This paper surveys state-of-the-art research on spectrum sensing and security threats in cognitive radios. Lastly, we also consider the analysis of issues related to spectrum handoffs in cognitive radios.
  • NETWORKS & SECURITY
    A Real-Time Attack on the GMR-2 Encryption Algorithm in Satellite Phones
    Jingmei Liu, Linsen Zhao, Jingwei Liu
    2017, 14(11): 209-217.
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    In this paper, we present one method to attack on the GMR-2 algorithm used in satellite phone under the chosen-plaintexts. First using the relationship of the rows of the two s-boxes and outputs of the F coordinate, we attack on the GMR-2 algorithm. Then we deduce the happening probability of read-collision, and analyze its mathematical expectation. Finally, combining with the read-collision, we present an improved method to attack on the GMR-2 algorithm. The research results show that the complexity of the improved algorithm is about 220, and the session key Kc can be recovered in about 0.3 seconds. Compared with the available method, our method takes less time than the guess-and-decide attack method which is 700s.
  • NETWORKS & SECURITY
    MINDTL: Multiple Incomplete Domains Transfer Learning for Information Recommendation
    Ming He, Jiuling Zhang, Jiang Zhang
    2017, 14(11): 218-236.
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    Collaborative filtering is the most popular and successful information recommendation technique. However, it can suffer from data sparsity issue in cases where the systems do not have sufficient domain information. Transfer learning, which enables information to be transferred from source domains to target domain, presents an unprecedented opportunity to alleviate this issue. A few recent works focus on transferring user-item rating information from a dense domain to a sparse target domain, while almost all methods need that each rating matrix in source domain to be extracted should be complete. To address this issue, in this paper we propose a novel multiple incomplete domains transfer learning model for cross-domain collaborative filtering. The transfer learning process consists of two steps. First, the user-item ratings information in incomplete source domains are compressed into multiple informative compact cluster-level matrixes, which are referred as codebooks. Second, we reconstruct the target matrix based on the codebooks. Specifically, for the purpose of maximizing the knowledge transfer, we design a new algorithm to learn the rating knowledge efficiently from multiple incomplete domains. Extensive experiments on real datasets demonstrate that our proposed approach significantly outperforms existing methods.
  • NETWORKS & SECURITY
    Key Management Using Chebyshev Polynomials for Mobile Ad Hoc Networks
    K.R. Ramkumar, Raman Singh
    2017, 14(11): 237-246.
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    A dedicated key server cannot be instituted to manage keys for MANETs since they are dynamic and unstable. The Lagrange’s polynomial and curve fitting are being used to implement hierarchical key management for Mobile Ad hoc Networks (MANETs). The polynomial interpolation by Lagrange and curve fitting requires high computational efforts for higher order polynomials and moreover they are susceptible to Runge’s phenomenon. The Chebyshev polynomials are secure, accurate, and stable and there is no limit to the degree of the polynomials. The distributed key management is a big challenge in these time varying networks. In this work, the Chebyshev polynomials are used to perform key management and tested in various conditions. The secret key shares generation, symmetric key construction and key distribution by using Chebyshev polynomials are the main elements of this projected work. The significance property of Chebyshev polynomials is its recursive nature. The mobile nodes usually have less computational power and less memory, the key management by using Chebyshev polynomials reduces the burden of mobile nodes to implement the overall system.
  • NETWORKS & SECURITY
    Control Flow Obfuscation Based Protection Method for Android Applications
    Yong Peng, Guanyu Su, Bin Tian, Maohua Sun, Qi Li
    2017, 14(11): 247-259.
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    With the popularization and rapid development of mobile intelligent terminals (MITs), the number of mobile applications, or apps, has increased exponentially. It is increasingly common for malicious code to be inserted into counterfeit apps, which can cause significant economic damage and threaten the security of users. Code obfuscation techniques are a highly efficient group of methods for code security protection. In this paper, we propose a novel control flow obfuscation based method for Android code protection. First, algorithms to insert irrelevant code and flatten the control flow are employed that minimize the cost of obfuscation while ensuring its strength. Second, we improve the traditional methods of control flow flattening to further reduce the costs of obfuscation. Lastly, the use of opaque predicates is strengthened by establishing an access control strategy, which converts the identification of opaque predicates in the entire program into a graph traversal problem, and thereby increases the strength of the code protection. We did some experiments to evaluate our method, and the results show that the proposed method can work well.
  • NETWORKS & SECURITY
    The Research of Customer Loyalty Improvement in Telecom Industry Based on NPS Data Mining
    Lili Tong, Yiting Wang, Fan Wen, Xiaowen Li
    2017, 14(11): 260-268.
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    In recent years, the telecommunications have used the concept of NPS (Net Promoter Score) for customer relationship management, but there is neither definite theory research nor instructive instance research. However, this paper summarizes an approach with instance case analysis to improve customer loyalty via NPS data mining, which has extensive and practical significance for tele-companies. First, this paper finds some driven forces of customer loyalty, which are relative to customer consumption such as the call duration, the usage of data, ARPU, etc., by using some innovative reasoning-analysis based on IG (Information Gain) and xg-boost decision-making tree model, so the tele-companies can predict the role of individual customer and form daily monitoring on big data, which will save a lot of NPS survey cost. Second, this paper summarizes how customer group feature impacts the relationship between NPS and financial performance. Taking ARPU value as the performance goals, we divide the sample customers into 6 groups and summarize their characteristics based on k-means clustering, and give targeted suggestion of each group.
    • SIGNAL PROCESSING FOR COMMUNICATIONS
  • SIGNAL PROCESSING FOR COMMUNICATIONS
    A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems
    Zhenyu Zhang, Xiaoming Dai, Yuanyuan Dong, Xiyuan Wang, Tong Liu
    2017, 14(11): 269-278.
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    Massive multiple-input multiple-output (MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error (MMSE) signal detection using the accelerated overrelaxation (AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.
  • SIGNAL PROCESSING FOR COMMUNICATIONS
    A Novel Peak-to-Average Power Ratio Reduction Scheme via Tone Reservation in OFDM Systems
    Jurong Bai, Yong Li, Wei Cheng, Huimin Du, Yanben Wang
    2017, 14(11): 279-290.
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    In this paper, a novel signal-to-clipping noise ratio and least squares approximation tone reservation scheme (SCR-LSA TR) is proposed to reduce the peak-to-average power ratio for orthogonal frequency division multiplexing systems. During the SCR procedure, only the element with the maximal amplitude is picked for processing, which not only decreases the algorithm complexity, but also helps to overcome the BER deterioration. With the LSA method, the amplitude of the peak-cancelling signals can approximate to that of the original clipping noise as much as possible. Through the combination of the optimization factor in the LSA method, the classic SCR method can achieve better PAPR reduction with faster convergence. Simulation results show that the proposed SCR-LSA TR scheme has less in-band distortion and smaller out-of-band spectral radiation. The BER of the proposed scheme shows a better performance especially under the 16-QAM over the additive white Gaussian noise channel.
    • NEWS
  • NEWS
    China Communications Editorial Board Meeting 2017 Held in Qingdao
    The Editorial Board of China Communications
    2017, 14(11): 291-292.
    Abstract ( )   Knowledge map   Save