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30 June 2019, Volume 3 Issue 2

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Frontier and Comprehensive Review

Development status and trend of space information network and laser communication

Huilin JIANG,Qiang FU,Yiwu ZHAO,Xianzhu LIU

2019, 3(2):  1-8.  doi:10.11959/j.issn.2096-3750.2019.00113

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An integrated space-earth information network is an important symbol of a strong country in science and technology.According to China’s overall plan,an information network with global coverage,on-demand services,local access and security and credibility will be built by 2030 to serve national defense and social development.Space laser communication is an important means to realize high speed information transmission and security.Firstly,space-earth integration network was introduced from three aspects:concept connotation and system architecture,research status and development trend,overall goal and main difficulties.Then,the concept,characteristics,research status and development trend of space laser communication were summarized in order to provide useful reference for the construction of space-earth integrated information network in China.

Theory and Technology

Miniaturized lensless microfluidic on-chip bio-imaging detection

Jin CHEN,Xiwei HUANG,Yangbo LI,Jixuan LIU,Lingling SUN

2019, 3(2):  9-19.  doi:10.11959/j.issn.2096-3750.2019.00111

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The conventional high-resolution optical microscopy for biomedical imaging has high cost and low portability due to bulky optical lenses system.To realize miniaturized imaging,the recent advance of lensless microfluidic on-chip imaging technique based on the integration of CMOS image sensor and microfluidics has provided one promising solution.Therefore,the architecture,principle,problems and improvement solutions for different miniaturized lensless microfluidic on-chip bio-imaging,including shadow imaging,holographic imaging,fluorescent imaging and color imaging were reviewed.And the deficiencies and possible development directions were discussed.

Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing

Kai XIONG,Supeng LENG,Ke ZHANG,Hao LIU

2019, 3(2):  20-27.  doi:10.11959/j.issn.2096-3750.2019.00108

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With the development of intelligent transportation and the constant emergence of new vehicular on-board applications,such as automatic driving,intelligent vehicular interaction and safety driving.It is difficult for an independent vehicle to run a wide variety of applications with a large number of computing needs and time delay needs relying on its own limited computing resources.By distributing computing tasks in devices on the edge of the network,fog computing applies virtualization technology,distributed computing technology and parallel computing technology to enable users to dynamically obtain computing power,storage space and other services on demand.Applying fog computing architecture to Internet of vehicles can effectively alleviate the contradiction between the large computing-low delay demands and limited vehicular resources.By analyzing the channel capacity of vehicle-to-vehicle communication,vehicle-infrastructure communication and vehicle-time-delay tolerant network communication,an optimization model of heterogeneous access to multi-service resources for the Internet of vehicles was established,and various vehicle-to-fog resources were jointly dispatched to realize efficient processing of intelligent transportation applications.The simulation results show that the proposed reinforcement learning algorithm can effectively deal with the resource allocation in the heterogeneous vehicular fog architecture.

Ambient backscatter communications technology for batteryless IoT

Qin TAO,Caijun ZHONG,Zhaoyang ZHANG

2019, 3(2):  28-34.  doi:10.11959/j.issn.2096-3750.2019.00107

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As one of the core enabling technologies supporting the batteryless Internet of things(IoT),ambient backscattering communication technology has the characteristics of low power consumption and low cost.Firstly,the basic concept of ambient backscattering communication technology and the research status at home and abroad were introduced.Then,based on an in-depth analysis of the differences between ambient backscattering communication technology and RFID technology,the technical difficulties faced by the ambient backscattering communication system were described in detail,including large detection delay,high bit error rate,low transmission rate and short communication distance.In addition,a series of potential solutions to the challenges were proposed.Finally,the future research directions of ambient backscattering communication were prospected.

Data-driven data transmission of the Internet of things based on sensing spectrum

Xuanheng LI,Yi SUN,Jie WANG,Haixia ZHANG

2019, 3(2):  35-46.  doi:10.11959/j.issn.2096-3750.2019.00103

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Facing the problem of spectrum shortage caused by the mass data,in order to share as a solution,how operators use the sensing spectrum reasonably to transmit data was studied.Considering the limitation of Internet of things(IoT) devices,the ultra-dense cognitive heterogeneous network architecture was designed,based on traffic demand and perception cost,an optimal access control and perception decision method was designed to maximize network utility.Considering the uncertainty of the perceived spectrum,the optimal programming scheme was modeled as a mixed integer stochastic optimization problem,and a data-driven probabilistic solution method based on statistical characteristics was proposed.In the case of unknown probability distribution of available bandwidth,data transmission requirements meeting various service requests were counted.

Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning

Yi ZHOU,Xiaoyong MA,Fuxiao GAO,Wei LI,Nan CHENG,Ning LU

2019, 3(2):  47-55.  doi:10.11959/j.issn.2096-3750.2019.00106

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Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.

Intelligent routing strategy in the Internet of things based on deep reinforcement learning

Ruijin DING,Feifei GAO,Ling XING

2019, 3(2):  56-63.  doi:10.11959/j.issn.2096-3750.2019.00097

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At the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selection problem was re-modeled as a Markov decision process.On this basis,the deep reinforcement learning technique was utilized to choose the next-hop router for data transmission task in order to shorten the transmission path length while network congestion was avoided.The simulation results demonstrate that the congestion probability can be reduced significantly and the network throughput can be enhanced by the proposed strategy.

Energy efficiency priority IoT task collaborative migration strategy

Longyu ZHOU,Ning YANG,Guanhua QIAO,Ke ZHANG,Qilin ZHENG

2019, 3(2):  64-71.  doi:10.11959/j.issn.2096-3750.2019.00105

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Mobile edge computing can reduce transmission delay and data processing delay for IoT applications by executing communication and computing operation in the edge network.However,for a large number of IoT device connections,massive service data is simultaneously gathered on the edge computing platform,which will significantly increase the traffic load of the forward link and the computing load of the edge server.In order to meet this challenge,based on diversified IoT application requirements,a task collaborative migration strategy was designed to realize the minimum energy consumption of the system under time delay constraints by optimizing the selection control of equipment transmission.In the absence of perfect channel state prior information,a resource management algorithm based on deep reinforcement learning was proposed to obtain the optimal offloading decision with lower complexity.The simulation results show that the proposed algorithm can significantly reduce the energy consumption of the system and meet the service delay of the task compared with the random transmission selection strategy.

Research on high security G function algorithm

Rui CHEN,Zan LI,Jia SHI,Lei GUAN

2019, 3(2):  72-79.  doi:10.11959/j.issn.2096-3750.2019.00100

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The differential frequency hopping (DFH) overcomes the limitation of frequency hopping and improves the ability to resist tracking interference.The G function determines the DFH sequence and directly affects the performance of DFH communication systems.However,with the complication of wireless communication system such as large capacity DFH network,which poses huge challenges to the use of the number theory based and chaos theory assisted sequence.As a result,in order to improve the security of the system,the novel G function construction was proposed with the aid of hybrid encryption algorithm and the security of hybrid algorithm was analyzed.Moreover,the equivalence principle of G function algorithm and the encryption algorithm was proved.The simulation results show that the DFH sequence generated by the new G function has excellent performance and the performance of the DFH systems is improved greatly.

Research on incentive mechanism for mobile intelligent edge computing

Shuyun LUO,Yuzhou WEN,Weiqiang XU,Shenghong ZHU

2019, 3(2):  80-88.  doi:10.11959/j.issn.2096-3750.2019.00101

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As a new architecture,mobile edge computing gives edge users stronger capabilities of computing,storage and communication,but it needs reasonable incentives mechanism to motivate edge users to provide resources.In terms of the three typical scenarios of mobile intelligent edge computing:computation offloading,edge caching and data collection,the incentive mechanism in the above scenarios was studied at first,then the core scientific problems were proposed that need to be solved in the incentive mechanism design of mobile intelligent edge computing from three perspectives of service quality,network quality and data quality.Finally,the technical challenges in the process of solving the above problems were analysed deeply and the corresponding feasible solutions were given.

New method of intelligent data transmission for IoV

Degan ZHANG,Pengzhen ZHAO,Jinxin GAO,Ting ZHANG,Changle GONG

2019, 3(2):  89-99.  doi:10.11959/j.issn.2096-3750.2019.00104

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Internet of vehicle (IoV) is a hot research topic of the intelligent transportation,and the research of new method of the intelligent data transmission is one of the key technologies.In order to solve the problem of intelligent data transmission of IoV,a kind of new method which includes the important parameters such as vehicle density,vehicle speed,data transmission rate and data delay was proposed.By setting up network model of IoV and delaying function,the optimal routing method for intelligent data transmission which is based on Markovian decision theory was designed.Through comparison experiments with the relative methods show that the performance on improving the data transmission rate and shortening the delay of data transmission of IoV,it has important value for many applications of intelligent transportation with IoV,such as vehicle dynamic remote monitoring.

Service and Application

Coverage optimization algorithm and implementation based on computational graph for mobile communication network and IoT service

Haobin WANG,Wei HUANGFU,Yaxi LIU,Wei LIU

2019, 3(2):  100-107.  doi:10.11959/j.issn.2096-3750.2019.00102

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The research on the mobile communication network optimization for the Internet of things large connection and differential service quality has great significance.Mobile communication network optimization is a multi-parameter complex optimization problem with high computational cost function.In order to provide the basis for parallelization of operations,the calculation method of the coverage quality assessment of the mobile communication network based on the computational graph was introduced.Based on the calculation graph,the derivative calculation method of the coverage quality index was obtained,by back propagation to guide the optimization of the antenna parameters.The momentum method was used to accelerate the convergence speed of the optimization algorithm.The simulation results show that the algorithm is suitable for the coverage optimization in mobile communication network.

Smart factory solutions based on cellular industrial Internet of things

Shulei GONG,Kun LI,En TONG,Hongde GUO,Yi ZHOU,Ye WANG,Fei DING

2019, 3(2):  108-114.  doi:10.11959/j.issn.2096-3750.2019.00112

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So far,the overall informatization level of manufacturing industry has been uneven,and there are many problems such as low intelligence and poor connectivity.First of all,with the features like low power consumption of the Internet of things,the sensor devices can be deployed flexibly to realize real-time collection and transmission of production data.Then the manufacturing execution system based on the cellular industrial Internet of things had been built,and realized the closed-loop processing of human,machine,material,method and surrounding.The industrial big data of the whole production process was modeled,analyzed and processed.The operation and maintenance cost was reduced,and the production efficiency and product quality were improved.Finally,the overall efficiency of the application was evaluated which showed that the factory capacity was improved effectively.