Archive

  • Select all
    |
    Guest Editorial
  • Guest Editorial
    Yan Li, Ying Zhang, Fei Luo, Wei Zou, Yu Zhang, Kaijun Zhou
    2021, 18(11): 1-10.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Mobile edge computing is trending nowadays for its computation efficiency and privacy. The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs) in the internet. This paper predicts the overall sales volume of the enterprise through the classic ARIMA model, and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts, so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers, thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning . In general, machine learning relies on powerful computation and storage resources, while mobile edge computing typically provides limited computation resources locally. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.
  • Guest Editorial
    Shengnan Wu, Yingjie Wang, Xiangrong Tong
    2021, 18(11): 11-25.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    With the development of the Internet of Things (IoT), spatio-temporal crowdsourcing (mobile crowdsourcing) has become an emerging paradigm for addressing location-based sensing tasks. However, the delay caused by network transmission has led to low data processing efficiency. Fortunately, edge computing can solve this problem, effectively reduce the delay of data transmission, and improve data processing capacity, so that the crowdsourcing platform can make better decisions faster. Therefore, this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment (MOO-TA) problem in the edge computing environment. The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area. In this paper, the Weighted and Multi-Objective Particle Swarm Combination (WAMOPSC) algorithm is proposed to maximize both platform's and crowd workers' utility, so as to maximize social welfare. The algorithm combines the traditional Linear Weighted Summation (LWS) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose. Through comparison experiments on real data sets, the effectiveness and feasibility of the proposed method are evaluated.
  • Guest Editorial
    Ziying Wu, Danfeng Yan
    2021, 18(11): 26-41.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Multi-access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional Internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based vehicle-aware Multi-access Edge Computing network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.
  • Guest Editorial
    Ting Bao, Lei Xu, Liehuang Zhu, Lihong Wang, Ruiguang Li, Tielei Li
    2021, 18(11): 42-60.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Mobile edge computing (MEC) is an emerging technolohgy that extends cloud computing to the edge of a network. MEC has been applied to a variety of services. Specially, MEC can help to reduce network delay and improve the service quality of recommendation systems. In a MEC-based recommendation system, users' rating data are collected and analyzed by the edge servers. If the servers behave dishonestly or break down, users' privacy may be disclosed. To solve this issue, we design a recommendation framework that applies local differential privacy (LDP) to collaborative filtering. In the proposed framework, users' rating data are perturbed to satisfy LDP and then released to the edge servers. The edge servers perform partial computing task by using the perturbed data. The cloud computing center computes the similarity between items by using the computing results generated by edge servers. We propose a data perturbation method to protect user's original rating values, where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation. And to enhance the protection of privacy, we propose two methods to protect both users' rating values and rating behaviors. Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.
  • Guest Editorial
    Zhongyuan Zhao, Huihui Gao, Wei Hong, Xiaoyu Duan, Mugen Peng
    2021, 18(11): 61-75.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Although content caching and recommendation are two complementary approaches to improve the user experience, it is still challenging to provide an integrated paradigm to fully explore their potential, due to the high complexity and complicated tradeoff relationship. To provide an efficient management framework, the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper. First, a joint transmission scheme of content objects and recommendation lists is designed with edge caching, and an optimization problem is formulated to balance the utility and cost of content caching and recommendation, which is an mixed integer nonlinear programming problem. Second, a reinforcement learning based algorithm is proposed to implement real time management of content caching, recommendation and delivery, which can approach the optimal solution without iterations during each decision epoch. Finally, the simulation results are provided to evaluate the performance of our proposed scheme, which show that it can achieve lower cost than the existing content caching and recommendation schemes.
  • Guest Editorial
    Rui Cao, Weijian Ni, Qingtian Zeng, Faming Lu, Cong Liu, Hua Duan
    2021, 18(11): 76-91.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Remaining time prediction of business processes plays an important role in resource scheduling and plan making. The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction. Existing prediction methods does not take full advantage of these two aspects into consideration. To address this issue, a new prediction method based on trace representation is proposed. More specifically, we first associate the prefix set generated by the event log to different states of the transition system, and encode the structural features of the prefixes in the state. Then, an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system. Next, states in the extended transition system are partitioned by the different lengths of the states, which considers concurrency among multiple process instances. Finally, the long short-term memory (LSTM) deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances. By extensive experimental evaluation using synthetic event logs and real-life event logs, we show that the proposed method outperforms existing baseline methods.
  • Guest Editorial
    Wei Liang, Songyou Xie, Jiahong Cai, Chong Wang, Yujie Hong, Xiaoyan Kui
    2021, 18(11): 92-103.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Efficient response speed and information processing speed are among the characteristics of mobile edge computing (MEC). However, MEC easily causes information leakage and loss problems because it requires frequent data exchange. This work proposes an anonymous privacy data protection and access control scheme based on elliptic curve cryptography (ECC) and bilinear pairing to protect the communication security of the MEC. In the proposed scheme, the information sender encrypts private information through the ECC algorithm, and the information receiver uses its own key information and bilinear pairing to extract and verify the identity of the information sender. During each round of communication, the proposed scheme uses timestamps and random numbers to ensure the freshness of each round of conversation. Experimental results show that the proposed scheme has good security performance and can provide data privacy protection, integrity verification, and traceability for the communication process of MEC. The proposed scheme has a lower cost than other related schemes. The communication and computational cost of the proposed scheme are reduced by 31.08% and 22.31% on average compared with those of the other related schemes.
  • COVER PAPER
  • COVER PAPER
    Han Xiao, Zhiqin Wang, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang
    2021, 18(11): 104-116.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.
  • REVIEW PAPER
  • REVIEW PAPER
    Shahid Sultan Hajam, Shabir Ahmad Sofi
    2021, 18(11): 117-140.
    Abstract ( )   Knowledge map   Save
    Smart cities improve the quality of life of people by utilizing the benefits of Information and communication technology (ICT) and the Internet of things (IoT). The applications of the smart city often rely on the cloud for services. No doubt cloud provides an ample amount of resources as a service but still it has limitations in terms of unreliable latency, mobility, and location awareness due to their multi-hop distance from the IoT devices. Fog computing avoids these limitations by providing services nearer to the edges. In this work we investigate the already proposed IoT-Fog based application specific smart city architectures and review them based on scalability, heterogeneity, mobility, energy conservation, latency, and security. Additionally, we discuss the applications and highlight the challenges that fog computing faces. We also present a case study of a smart city scenario with multiple applications of IoT.
  • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Ning Li, Kun Yao, Zhongliang Deng, Xiaohao Zhao, Jianchang Qin
    2021, 18(11): 141-154.
    Abstract ( )   Knowledge map   Save
    Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing. However, because of the influence of the number of subcarriers and pilots, the complexity of the enumeration method is computationally impractical. The meta-heuristic algorithm of the salp swarm algorithm (SSA) is employed to address this issue. Like most meta-heuristic algorithms, the SSA algorithm is prone to problems such as local optimal values and slow convergence. In this paper, we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy, adaptive weight factor, and increasing local search. Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms. Besides, the CWSSA algorithm is applied to pilot pattern optimization, and its results are better than other methods in terms of BER and MSE.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Maryam hajiee, Mehdi Fartash, Nafiseh Osati Eraghi
    2021, 18(11): 155-167.
    Abstract ( )   Knowledge map   Save
    Nowadays, with the advancement of new technologies such as the Internet of Things, new applications and intelligent networks, the use of wireless sensor network increased considerably. They are prone to a variety of attacks. Thus, network security is of utmost importance to researchers. In the past, methods such as cryptography, authentication and hash function were used to create security in this type of network. However, due to the limitations of this type of network, trust-based methods are used today. Finding a secure route for transferring data among available routes greatly increases security in this network. In this paper, we present aTrust-based Routing Optimization using Multi-Ant Colonies (MACRAT) scheme which is based on the improvement of the ant meta-heuristic algorithm and an improved method for trust assessment which is presented. The simulation results illustrate that MACRAT is more efficient than existing routing protocols. The results show that MACRAT improved by 10% in black hole detection compared to ESRT protocol and by 4% compared to M-CSO protocol, the packet loss rate in MACRAT improved by 30.14% compared to ESRT protocol and 6% compared to M-CSO protocol.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Xiaobo Zhang, Hai Wang, Yifan Xu, Zhibin Feng, Yunpeng Zhang
    2021, 18(11): 168-181.
    Abstract ( )   Knowledge map   Save
    This paper mainly investigates the coordinated anti-jamming channel access problems in multi-user scenarios where there exists a tracking jammer who senses the spectrum and traces the channel with maximal receiving power. To cope with the challenges brought by the tracking jammer, a multi-leader one-follower anti-jamming Stackelberg (MOAS) game is formulated, which is able to model the complex interactions between users and the tracking jammer. In the proposed game, users act as leaders, chose their channel access strategies and transmit firstly. The tracking jammer acts as the follower, whose objective is to find the optimal jamming strategy at each time slot. Besides, the existence of Stackelberg equilibriums (SEs) is proved, which means users reach Nash Equilibriums (NEs) for each jamming strategy while the jammer finds its best response jamming strategy for the current network access case. An active attraction based anti-jamming channel access (3ACA) algorithm is designed to reach SEs, where jammed users keep their channel access strategies unchanged to create access chances for other users. To enhance the fairness of the system, users will adjust their strategies and relearn after certain time slots to provide access chances for those users who sacrifice themselves to attract the tracking jammer.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Kaixin Cheng, Lei Zhu, Changhua Yao, Lu Yu, Xinrong Wu, Xiang Zheng, Lei Wang, Fandi Lin
    2021, 18(11): 182-196.
    Abstract ( )   Knowledge map   Save
    Communication behavior recognition is an issue with increasingly importance in the anti-terrorism and national defense area. However, the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior. Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene. Thus, communication behavior recognition using raw sensing data under small-sample condition has become a new challenge. In this paper, a data enhanced communication behavior recognition (DECBR) scheme is proposed to meet this challenge. Firstly, a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme. Then, an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition. Moreover, DCGAN is applied to support data enhancement, which realize communication behavior recognition under small-sample condition. Finally, the scheme is verified by experiments under different data size. The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under small-sample condition.
  • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Liqin Yang, Liang Zhang
    2021, 18(11): 197-209.
    Abstract ( )   Knowledge map   Save
    Clinical practice guidelines (CPGs) contain evidence-based and economically reasonable medical treatment processes. Executable medical treatment processes in healthcare information systems can assist the treatment processes. To this end, business process modeling technologies have been exploited to model medical treatment processes. However, medical treatment processes are usually flexible and knowledge-intensive. To reduce the effort in modeling, we summarize several treatment patterns (i.e., frequent behaviors in medical treatment processes in CPGs), and represent them by three process modeling languages (i.e., BPMN, DMN, and CMMN). Based on the summarized treatment patterns, we propose a pattern-based integrated framework for modeling medical treatment processes. A modeling platform is implemented to support the use of treatment patterns, by which the feasibility of our approach is validated. An empirical analysis is discussed based on the coverage rates of treatment patterns. Feedback from interviewed physicians in a Chinese hospital shows that executable medical treatment processes of CPGs provide a convenient way to obtain guidance, thus assisting daily work for medical workers.
  • BUSINESS PROCESS & MANAGEMENT
  • BUSINESS PROCESS & MANAGEMENT
    Amit Kumar Gautam, Rakesh Kumar
    2021, 18(11): 210-228.
    Abstract ( )   Knowledge map   Save
    Wireless Sensor Network (WSN) has witnessed an unpredictable growth for the last few decades. It has many applications in various critical sectors such as real-time monitoring of nuclear power plant, disaster management, environment, military area etc. However, due to the distributed and remote deployment of sensor nodes in such networks, they are highly vulnerable to different security threats. The sensor network always needs a proficient key management scheme to secure data because of resource-constrained nodes. Existing polynomial based key management schemes are simple, but the computational complexity is a big issue. Lucas polynomials, Fibonacci polynomials, Chebychev polynomials are used in Engineering, Physics, Combinatory and Numerical analysis etc. In this paper, we propose a key management scheme using $(p, q)$- Lucas polynomial to improve the security of WSN. In $(p, q)$- Lucas polynomial, $p$ represents a random base number while $q$ represents a substitute value of $x$ in the polynomial. The value of $p$ is unique, and $q$ is different according to communication between nodes. Analysis of the proposed method on several parameters such as computational overhead, efficiency and storage cost have been performed and compared with existing related schemes. The analysis demonstrates that the proposed $(p, q)$- Lucas polynomial based key management scheme outperforms over other polynomials in terms of the number of keys used and efficiency.
  • BUSINESS PROCESS & MANAGEMENT
    Guosheng Kang, Liqing Yang, Liang Zhang, Jianxun Liu, Yiping Wen
    2021, 18(11): 229-242.
    Abstract ( )   Knowledge map   Save
    Nowadays, enterprises need to continually adjust their business processes to adapt to the changes of business environments, especially when one business needs to be deployed in different application scenarios, which is called spatial variability in this paper. In the field of BPM (Business Process Management), configurable business process models have demonstrated their effectiveness in aspects of process modeling and model reuse. Yet, we found that the existing techniques lead to complex configurable models, and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow. S-BPM (Subject-oriented Business Process Management) models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process execution. In this paper, we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm, in which business objects are also included. Our approach is validated with a general business process developed for the Real Estate Administration (REA) in a certain province of China. The resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.
  • BUSINESS PROCESS & MANAGEMENT
    Cheng Zeng, Haifeng Zhang, Junwei Ren, Chaodong Wen, Peng He
    2021, 18(11): 243-256.
    Abstract ( )   Knowledge map   Save
    In recent years, online reservation systems of country hotel have become increasingly popular in rural areas. How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved. Aiming at the problem of cold start and data sparseness in recommendation, a Hybrid Recommendation method based on Graph Embedding (HRGE) is proposed. First, three types of network are built, including user-user network based on user tag, house-house network based on house tag, and user-user network based on user behavior. Then, by using the method of graph embedding, three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes. Finally, these characterization vectors are used to make a hybrid recommendation. The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province. The experimental results show that, compared with traditional recommendation algorithms, the comprehensive evaluation index (F1) of the HRGE is improved by $20%$ and the Mean Average Precision (MAP) is increased by $11%$.