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30 March 2021, Volume 5 Issue 1

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Topic: Edge Intelligence and Fog Computing in IoT

Intelligent adaptive edge systems：exploration and open issues

Xu WANG, Nanxi CHEN, Roujia ZHANG

2021, 5(1):  1-10.  doi:10.11959/j.issn.2096-3750.2021.00210

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Edge intelligence has emerged as a promising trend of the new generation of Internet of things.Edge computing devices are widely distributed, with various diverse end devices and services, delay sensitive, and serve mobile terminals.Therefore, the edge system needs to provide flexible, diverse, reconfigurable and scalable services.From the application fields of adaptive edge computing, the application requirements of intelligent adaptive edge systems were explored, the existing adaptive edge systems and their basic framework were analyzed and summarized, and the application of artificial intelligence technologies was discussed, such as deep learning and reinforcement learning.Then, how to design a special intelligent algorithm in specific application fields was introduced.Finally, the research status and future challenges in this field were discussed.

Hierarchical resource deployment and sharing strategy in mobile edge computing

Houhao ZHANG, Hanlin LI, Lin GAO

2021, 5(1):  11-18.  doi:10.11959/j.issn.2096-3750.2021.00200

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With the coming of 5G, Internet of things (IoT) has gradually become a reality.However, the function of many terminals is limited to insufficient computing resources in IoT.Mobile edge computing (MEC) is a new network paradigm, where terminals can offload the resource sensitive applications to nearby edge services with rich resources, and thus reduced the operational cost/delay and increased the quality of service (QoS).The resource deployment of MEC was studied, a scenario was considered that the Internet service provider (ISP) paid for deploying the MEC resources, and meanwhile gained revenue from leasing resources to terminals.Note that the deployment of MEC resource is a long-term strategy, while the demands from terminals are time varying.Therefore, it is critical to deploy the MEC resource properly.A hierarchical architecture built upon the 5G radio access network for MEC resource deployment and sharing was proposed, where the MEC resource could be deployed at different network levels.Based on the hierarchical network architecture, the optimal MEC resource deployment problem was formulated and solved as a mixed integer programming problem, aiming at maximizing the ISP’s revenue.And the CVX toolbox in MATLAB was used to solve the problem.Simulation results demonstrate that the proposed solution outperforms the flat resource deployment solution in terms of both the ISP’s revenue and the deployment cost.

Optimization strategies in NOMA-based vehicle edge computing network

Jianbo DU, Nana XUE, Yan SUN, Jing JIANG, Shulei LI, Guangyue LU

2021, 5(1):  19-26.  doi:10.11959/j.issn.2096-3750.2021.00207

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Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.

Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing

Jiequ JI, Kun ZHU, Changyan YI, Ran WANG

2021, 5(1):  27-35.  doi:10.11959/j.issn.2096-3750.2021.00190

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An unmanned aerial vehicle (UAV)-assisted mobile edge computing system was proposed in which multiple UAVs equipped with computing resources were employed to provide computation offloading opportunities for mobile users with limited local resources.The computing tasks of each user can be divided into two parts.One portion was offloaded to its associated UAV for computing and the remaining portion was processed locally.It was aimed at minimizing the sum of the maximum delay among all user devices by jointly optimizing the user scheduling and the UAV trajectory in a finite period.The proposed problem was a mixed-integer non-convex optimization problem.To facilitate solving this problem, it was equivalently converted into a more tractable problem by introducing some auxiliary variables, and then a penalty concave-convex procedure algorithm was proposed to solve the converted problem.Simulation results show that the proposed joint optimization scheme achieves significantly better performance than other benchmark schemes.

Multi-access edge computing offloading in maritime monitoring sensor networks

Xin SU, Ziyi WANG, Yupeng WANG, Siyuan ZHOU

2021, 5(1):  36-52.  doi:10.11959/j.issn.2096-3750.2021.00205

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Multi-access edge computing can effectively guarantee the low-latency, high-reliability data transmission of ocean monitoring sensor networks and various related maritime applications.In the offshore scenario, two offloading models of multi-user single-hop unicast and multi-user multi-hop unicast were established in combination with the distribution of edge computing resources.The mixed integer nonlinear programming was used to separate optimization targets and effectively allocate transmission power.The unloading decisions were made by improving the traditional artificial fish swarms algorithm.The results show that the proposed optimization algorithm can reduce the network delay by nearly 19% compared with the traditional scheme.In the far-sea scenario, a multi-user single-hop unicast offloading model was established, and a reasonable channel allocation algorithm was proposed based on the network connection probability.The results show that when the network connection time is sufficient, the number of allowable sub-channels can be increased to reduce the network delay.When the network connection time is limited, the number of unloaded marine user equipment can be controlled to ensure the network transmission delay.

CS-communication integration method in IoT monitoring multiple phenomena

Chengcheng HAN, Li CHEN, Weidong WANG

2021, 5(1):  53-61.  doi:10.11959/j.issn.2096-3750.2021.00214

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The large-scale Internet of things with multi observation phenomenon uses orthogonal multiple access (OMA) mechanism to transmit the observation data of distributed nodes, which will cause great transmission delay and lead to the loss of timeliness of data.To cope with the heavy latency of observations due to OMA, an efficient scheme integrating compressed sensing (CS) technique with communication was proposed for large-scale Internet of things to monitor multiple phenomena.In this proposed scheme, the nodes monitoring different phenomena were assigned to different time durations for transmission.During the assigned time duration, all nodes concurrently transmitted observations to the fusion center (FC)for CS measurement, and the FC recovered observation by CS algorithms.To evaluate the performance of the proposed scheme, the achievable rate of the observed phenomena was derived, which was closely related to the time allocation of clusters.To further improve the performance, the optimization problems of time allocation were studied under the two objectives of maximizing the total rate and ensuring the fairness of observation.Finally, the performance was verified and analyzed by numerical simulation.The simulation results show that the achievable rate of observations for different phenomena is improved the proposed scheme significantly compared with OMA schemes.

Application of edge-cloud collaborative intelligence technologies in power grids

Qing HAN, Kunlun GAO, Ting ZHAO, Jiangqi CHEN, Xinyu YANG, Shusen YANG

2021, 5(1):  62-71.  doi:10.11959/j.issn.2096-3750.2021.00204

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With the continuous development of the Internet of things on electricity (IoTE) and large-scale deployment of intelligent edge devices, an explosively increasing amount of data are being generated at the network edge.The efficient, fast and secure processing and analysis of the massive edge located data brings great challenges for the traditional cloud computing-based intelligence technologies.Instead, edge-cloud collaborative intelligence (ECCI) technologies can significantly outperform the cloud computing-based intelligence in terms of the network bandwidth saving, delay reduction and privacy protection, and therefore have shown a great potential in boosting the development of power grids.To investigate the application of ECCI in power grids, the concept and research progress of ECCI were firstly introduced.The characteristics and advantages of ECCI were summarized and its applicability in the power grids were discussed.Secondly, the key technologies of ECCI applications for power grids were discussed and the solutions based on ECCI technologies for two typical scenes were proposed respectively.Finally, a brief discussion of future work was given.

Theory and Technology

Blockchain technology for Internet of things: an overview

Cai GUO, Xuran LI, Yanhua CHEN, Hongning DAI

2021, 5(1):  72-89.  doi:10.11959/j.issn.2096-3750.2021.00201

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Internet of things (IoT) is changing the data-driven smart industry due the massive availability of IoT data.Nevertheless, the IoT also poses some challenging issues like decentralization, poor interoperability, privacy and security vulnerabilities.The recent advent of blockchain can potentially tackle the above issues.The marriage of blockchain and IoT was investigated, and named this integration as blockchain of things (BCoT).In particular, the IoT and blockchain technology was introduced firstly.The convergence of blockchain and IoT was introduced, and the proposal of BCoT architecture was presented.The application of the Internet of things in the industry was further discussed.Finally, the open research directions in the field were outlined.

Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring

Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU

2021, 5(1):  90-98.  doi:10.11959/j.issn.2096-3750.2021.00164

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In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things, a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring (ACB) was proposed.Firstly, the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1 000 bit, a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1 000 bit.On this basis, a method was proposed for predicting the access application volume based on the quantitative estimation, and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms, the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.

Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm

Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

2021, 5(1):  99-107.  doi:10.11959/j.issn.2096-3750.2021.00192

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Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.

Optimal allocation model of logistics alliance resources based on willingness to participate

Mengrong LI, Huayu ZHU, Jin QI, Yanfei SUN

2021, 5(1):  108-116.  doi:10.11959/j.issn.2096-3750.2021.00163

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In order to reduce the costs and improve the efficiency during large-scale logistics tasks, logistics alliances are established by enterprises to achieve scale effects.The stability of the cooperative operation of the logistics alliances is mainly reflected in the enthusiasm of enterprises for participating in cooperation.Therefore, a resource optimal allocation model based on the willingness to participate was proposed, considering the impact of the participation willingness of alliance members on the resource allocation of the alliance.A sorting method with the optimal solution based on the regret theory was proposed to solve the model, and the method was verified based on the participation willingness index.The results show that the participation willingness distribution score of the sorting method based on the regret theory is 3.5 times higher than that of the traditional method.It can be seen that the proposed method can effectively improve the scientificity and rationality of resource allocation, and can contribute to consolidate the stability of the collaborative operation of the supply chain.

Optimization strategy of distribution network operation considering multi-investment entities under the situation of ubiquitous power Internet of things

Xiaodong SHEN, Junyong LIU, Shuai HU, Ping RAO, Mai HE

2021, 5(1):  117-122.  doi:10.11959/j.issn.2096-3750.2021.00203

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The ubiquitous power Internet of things encourages a large number of new power equipment, such as distributed power supply, energy storage and electric vehicles, to connect to the distribution network.With the increasing penetration of renewable energy in the distribution network and the gradual opening of the electricity market, the investment entities of the power grid tend to be diversified.The uncertainty of the access of renewable energy to the power grid and the coordination of the interests of various entities resources is increasingly prominent.Considering the profits of distributed energy resources (DER) investors in the regional distribution network, an optimal operation strategy based on the incentive mechanism in ubiquitous power Internet of things was proposed.This strategy could reduce the adverse effects of load fluctuations and reversed power flow on the power grid, while increasing profits for DER investors.The calculation example shows the effectiveness of the proposed method.