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    30 June 2021, Volume 5 Issue 2
    Frontier and Comprehensive Review
    Cloud native network for intelligent Internet of everything
    Quan YU, Dandan LIANG, Wei ZHANG
    2021, 5(2):  1-6.  doi:10.11959/j.issn.2096-3750.2021.00233
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    A new cloud native network architecture for the future network architecture design in the era of intelligent Internet of everything was proposed, that is, network of the cloud, for the cloud, and by the cloud.Furthermore, the concept and mechanism of Cybertwin were proposed to tackle the challenges of mobility, security and availability in cloud native network.Three fundamental questions of cloud native network architecture design were also elaborated, that is, addressing, indexing and resource orchestration.Finally, 6G use cases and future research topics of Cybertwin based cloud native network were discussed.

    Topic: Edge Intelligence and Fog Computing in IoT
    Cloud native based edge computing: vision and challenges
    Deze ZENG, Lvhao CHEN, Lin GU, Yuepeng LI
    2021, 5(2):  7-17.  doi:10.11959/j.issn.2096-3750.2021.00206
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    Cloud native computing, featured by low-cost container technology, well fits edge computing.It was proposed to apply cloud native computing to make edge computing resource management and control transparent to application development and operation.Compared with cloud computing, edge computing resources are widely distributed, highly heterogeneous, and fragmented, which called for collaborative resource management and control.According to the development status of cloud native related technologies, through the integration of future networking technologies such as soft were defined network and network function virtualization, a full-stack cloud native based edge computing architecture was proposed.Then, considering the hierarchical characteristics of containers, a low-overhead container deployment optimization problem for resource-limited edge computing was studied.Finally, the development challenges faced by cloud native based edge computing were discussed.

    LoRaWAN technologies for enabling new generation Internet of things
    Chaojie GU, Rui TAN
    2021, 5(2):  18-25.  doi:10.11959/j.issn.2096-3750.2021.00231
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    LoRaWAN is a representative low power wide area networking technology that has attracted significant attention from both the industry and academia.It can improve the connectivity of massive edge devices greatly in the era of Internet of things (IoT).The basics of LoRaWAN were reviewed briefly and the latest LoRaWAN research results were surveyed in four aspects, i.e., communication performance target localization, wireless network security, and heterogeneous network.The opportunities and challenges were also discussed regarding the wide adoption of LoRaWAN in China’s context in four aspects, i.e., business models, supervision methods, technology innovations, and emerging applications.

    Semantic communication networking for the intelligence of everything
    Guangming SHI, Yong XIAO, Yingyu LI, Dahua GAO, Xuemei XIE
    2021, 5(2):  26-36.  doi:10.11959/j.issn.2096-3750.2021.00209
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    With the fast growing demand for the wireless network intelligence, the future communication system will transform from the traditional data-oriented solution to a novel intelligence-of-everything (IoE)-based architecture.Semantic communication is a new communication technology which involves the meaning of message into the communication process.It is believed that semantic communication will have the potential to serve as the fundamental paradigm for the future IoE.The relationship between the semantic communication and IoE was discussed and the basic models and fundamental components of semantic communication were introduced.By discussing the limitations of point-to-point semantic communication, it was argued that the knowledge sharing and resource convergence-based semantic communication networking would be ideal for supporting the future massive scales of IoE systems.The basic components of the semantic communication networking system were discussed and a federated edge intelligence-based semantic communication networking architecture as a case study was considered.Simulation results show that semantic communication networking has the potential to further reduce the resource demand and improve the efficiency of semantic communication.Finally, open problems for future research were discussed.

    Leveraging edge learning and game theory for intrusion detection in Internet of things
    Haoran LIANG, Jun WU, Chengcheng ZHAO, Jianhua LI
    2021, 5(2):  37-47.  doi:10.11959/j.issn.2096-3750.2021.00226
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    With the commercialization of 5G and the development of 6G, more and more Internet of things (IoT) devices are linked to the novel cyber-physical system (CPS) to support intelligent decision making.However, the highly decentralized and heterogeneous IoT devices face potential threats that may mislead the CPS.Traditional intrusion detection solutions cannot protect the privacy of IoT devices, and they have to deal with the single point of failure, which prevents these solutions from being deploying in IoT scenarios.The edge learning and game theory based intrusion detection for IoT was proposed.Firstly, an edge learning based intrusion detection framework was proposed to detect potential threats in IoT.Moreover, a multi-leader multi-follower game was employed to motivate trusted parameter servers and edge devices to participate in the edge learning process.Experiments and evaluations show the security and effectiveness of the proposed intrusion detection framework.

    Deep reinforcement learning to enhance the energy-efficient performance of UAV-enabled F-RAN
    Haibo MEI, Kun YANG, Xinyu FAN
    2021, 5(2):  48-59.  doi:10.11959/j.issn.2096-3750.2021.00234
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    Fog radio access network (F-RAN) is suitable for Internet of things applications of national important industries, such as pipeline network monitoring in wide area.However, the performance of the F-RAN based on the territorial fog access point will be affected greatly by the complicated territorial environment.This causes F-RAN not able to provide fog access service in a timely and effectively manner.To this problem, the research was proposed to utilize low altitude UAV as the fog access point to realize air ground edge communication and fog computing, which has attracted enormous research interests.How to use deep reinforcement learning (DRL) to improve the energy efficiency of UAV fog access point and extend the mission time of UAV were discussed.Deep reinforcement learning can ensure the UAV fog access point to adjust the configuration strategy timely of air ground communication and computing, including resource optimization, dynamic task offloading and caching.DRL can also optimize the UAV trajectory in 3-D space, and improve the overall performance of UAV enabled fog access network.The innovation of the research lies in the comprehensive discussion of the main optimization problems to be solved in the UAV-enabled F-RAN using DRL.The technical details were also summarized to solve the related optimization problems.Finally, the technical challenges and future research directions of the application of DRL in the UAV-enabled F-RAN were discussed.

    Resource allocation based on optimal transport theory in IoT edge computing
    Qi ZHANG, Yuna JIANG, Xiaohu GE, Yonghui LI
    2021, 5(2):  60-70.  doi:10.11959/j.issn.2096-3750.2021.00225
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    With the development of the Internet of things (IoT) and edge computing, the computation-intensive tasks of IoT devices can be offloaded to edge devices and processed at the edge of networks.Due to the variation of the distribution and computation requirements of IoT devices, the computation resources of edge networks need to be managed dynamically.The optimal transport theory was adopted to optimize the computation resources allocation in IoT networks.An optimized regional partition mechanism was proposed based on the distribution of IoT devices and locations of edge computing devices.Under constraints on the computing capabilities of edge computing devices, the energy consumption and delay of IoT devices were optimized.The simulation results show that, compared with the traditional Voronoi partition scheme, the proposed optimization mechanism shows better balance.The average transmitting power can be reduced by 21% and the average delay can be reduced by 45%.

    Online energy-aware task dispatching with QoS guarantee in edge computing
    Hao YUAN, Deke GUO, Guoming TANG, Lailong LUO
    2021, 5(2):  71-77.  doi:10.11959/j.issn.2096-3750.2021.00230
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    Edge computing can provide users with low-latency and high-bandwidth services by deploying many edge servers at the network edge.However, a large number of deployments also bring problems of high energy consumption.When dispatching tasks from end devices to different edge servers, different energy consumption and delays will occur due to the edge servers’ heterogeneity.Therefore, it is a challenge to select an optimal server among many edge servers for task dispatching so that energy consumption and delay are relatively low.An energy-aware task dispatching method with quality of service (QoS) guarantee based on online learning was proposed.It can obtain real-time information by interacting with the environment to ensure energy consumption was minimal while the QoS was acceptable when dispatching tasks.Experiments show that the proposed method can dispatch tasks efficiently to the optimal server compared with other methods, thereby reducing the edge computing network’s overall energy consumption significantly.

    Design of task dividing and offloading algorithm in mobile edge computing
    Jing LU, Hanlin LI, Lin GAO
    2021, 5(2):  78-86.  doi:10.11959/j.issn.2096-3750.2021.00199
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    Mobile edge computing (MEC) emerges as a new paradigm that pushes the computing infrastructure from the remote cloud data center to the edge equipments.It provides a new solution to meet the delay sensitive and computing intensive requirements of Internet of things (IoT).In this work, the problem of tasks offloading and scheduling in the multi-user and multi-server MEC system was considered.Specifically, each user had a task-dependent application and the tasks could be either executed locally or remotely according to the dependence.Thus, the network performance was improved by unloading and scheduling the sub tasks.Quality of experience (QoE) and fairness between users were used to characterize the network performance, and the optimization problem was modeled as a joint dependent task offloading and scheduling (J-DTOS) problem.The J-DTOS problem was a non-linear mixed integer programming, which was NP-hard in general.The original problem was reformulated by introducing intermediate variables and proposing a near-optimal solution.Simulation results show that the proposed offloading and scheduling design can significantly improve the performance of the system.

    Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach
    Chunmin LIN, Liekang ZENG, Xu CHEN
    2021, 5(2):  87-96.  doi:10.11959/j.issn.2096-3750.2021.00229
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    Autonomous drone navigation has received growing attention in the recent community.Compared with traditional navigation approaches which rely on location-based services highly, deep learning based visual methods have showed superior performance in self-adaption and generalization, which are a promising solution for autonomous navigation.Running the resource-hungry deep learning execution in the resource-constrained unmanned aerial vehicle (UAV), however, significant challenges were presented in power efficiency.To tackle this challenge, following the idea of edge intelligence, a deep reinforcement learning approach was introduced to dynamically configure the computational scale of the deep learning model on UAV and hence realize the autonomous navigation with low latency and high energy efficiency.Evaluations based on both simulation and real prototype experiments show that the proposed approach has the less energy consumption, longer navigation trail and higher obstacle avoidance rate.

    Theory and Technology
    Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer
    Xiaoqiang ZHAO, Shaoya REN, Yongzhi ZHAI, Heng QUAN, Ting YANG
    2021, 5(2):  97-106.  doi:10.11959/j.issn.2096-3750.2021.00211
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    It’s one of the main goals of the heterogeneous wireless sensor network (HWSN) to extend the network lifecycle by reasonably utilizing the heterogeneity of node energy.Therefore, according to the heterogeneity of node energy, a routing protocol (SA-MGWO) for HWSN based on simulated annealing (SA) algorithm and modified grey wolf optimizer (GWO) was proposed.Firstly, the appropriate initial clusters were selected by de?ning different ?tness functions for heterogeneous energy nodes.Secondly, The ?tness values of nodes were calculated and treated as initial weights in the GWO.At the same time, the weights were updated dynamically according to the distance between the wolves and their prey and coefficient vectors to improve the GWO’s optimization ability.Finally, simulated annealing algorithm was used to ensure the selection of optimal cluster set in heterogeneous networks.Compared with stable election protocol (SEP), distribute energy efficient clustering (DEEC), modified stable election protocol (M-SEP), and fitness value based improved grey wolf optimizer (FIGWO) protocols, the experimental results indicate that the network lifecycle of the SA-MGWO protocol improves by 53.1%, 31.9%, 46.5% and 27.0% respectively.

    High-precision indoor wireless positioning method based on generative adversarial network
    Fuzhan WANG, Xiaorong ZHU, Meijuan CHEN, Hongbo ZHU
    2021, 5(2):  107-115.  doi:10.11959/j.issn.2096-3750.2021.00208
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    Because wireless signals are susceptible to interference during the propagation process, the application of traditional indoor positioning methods in real life is limited.Because location-based fingerprint positioning technology has the advantage of strong universality, it has become a current research hotspot.The number of fingerprint data is an important factor affecting the accuracy of fingerprint positioning, but the cost of collecting a large amount of fingerprint data is large.Therefore, how to use a small amount of fingerprint data to achieve higher positioning accuracy is a difficult point of fingerprint positioning technology.Aiming at this problem, a high-precision indoor wireless positioning method based on generative adversarial network was proposed.Firstly, fingerprint data was collected densely at equal intervals indoors, and the initial fingerprint data set was constructed, the part of the fingerprint data was selected in the initial fingerprint data set, and the generative adversarial network was used to obtain a large amount of fingerprint data from part of the fingerprint data.Then, based on these generated data, a KNN (k-nearest neighbor) model and a random forest model were used for location prediction.Experimental results show that this method can achieve high wireless positioning accuracy based on a small amount of fingerprint data, and the positioning accuracy can reach 15.4 cm.

    A blockchain architecture with master-slave blockchain
    Pengliu TAN, Lixuran WAN
    2021, 5(2):  116-124.  doi:10.11959/j.issn.2096-3750.2021.00219
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    With the continuous development of blockchain technology, different chains are derived due to different adaptation scenarios.Each chain has its own characteristics, such as public chains like bitcoin and ethereum, a large number of private chains and alliance chains.But as far as the current Internet is concerned, the implementation of many application scenarios on traditional blockchains has become particularly inconvenient.A master-slave blockchain (MSBC) architecture was proposed, which was mainly composed of a master block, a subordinate master block and a subordinate micro block.The master chain was composed of master blocks.Each master block has a slave master block and multiple slave micro block on its side chain.In addition, the master block and the master block were directly connected by the Hash of the previous block, the master block and the slave master block were connected by the Hash of the unique information, and the slave micro block and the previous block (whatever the slave master block or the slave micro block) was also connected by the Hash of the previous block.In talent chain, this kind of structure could put a person’s fixed resume information on the master chain, but updated resume information constantly on the slave side chain.MSBC architecture was more scalable, and it could improve the efficiency of data query.The experimental results show that the framework in the similar applications such as talent chain is feasible and the query efficiency has been improved greatly.

    Research and design of the intelligent energy management and control platform in the airport area based on the IoT technology
    Yuan HUANG, Gang WU, Junyong LIU, Chenxi YANG, Mengpin GAO
    2021, 5(2):  125-133.  doi:10.11959/j.issn.2096-3750.2021.00194
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    The construction of the green airport urgently needs an efficient energy security information system in face of the electric energy substitution and clean energy access.A design idea of the intelligent energy management and control platform in the airport area based on the Internet of things (IoT) technology was proposed.The platform included a perception layer, a network layer, a data layer and an application layer.The IoT technology and the fusion technology of the information and energy were adopted to realize the application scenarios for multiple users.Finally, an energy control platform framework was designed by combing the energy information of a civil hub airport to provide basic services for further exploration of the value of the energy.


Copyright Information
Quarterly,started in 2017
Cpmpetent Unit:Ministry of Industry and Information Technology of the People's Republic of China
Sponsor:Posts & Telecom Press Co.,Ltd.
Publisher: China InfoCom Media Group
Editor:Editor Board of Chinese Journal on Internet of Things
Editor-in-Chief:YIN Hao
Executive Editor-in-Chief:ZHU Hongbo
Director:LI Caishan
Address:F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Tel:010-53878076、53879096、53879098
E-mail:wlwxb@bjxintong.com.cn
ISSN 2096-3750
CN 10-1491/TP
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