Research on Database Deployment Technology in Cloud Computing Environment by Yongshun Cai, Mingchuan Yang, Wei Si, Shaoyang Rao

“…Cloud computing virtualization technology has the characteristics of elastic scalability，automatic migration （high availability），automatic recovery （high stability）.Because of the natural complexity of database systems，it makes a big challenge to deploy database systems in cloud computing virtualization environment.On the basis of research and analyzing models of deploying database systems in private cloud environment，the TPC-C benchmark test with database instances running in cloud computing virtual machines was applied.Compared with performance results of same test with database instances running in physical machines，the feasibility of deploying database systems in cloud computing virtualization environment was verified.…”
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Research on incremental deployment mechanism of network modality for polymorphic network environment by Jiong LI, Yuxiang HU, Pengshuai CUI, Le TIAN, Yongji DONG

“…Polymorphic networks support diverse network requirements and can coexist, evolve, and change on a unified physical or logical infrastructure in the form of network modality.They can maximize the innovation vitality of network technology driven by new applications.The evolution and development of modality require the incremental deployment of modality to meet the needs of convenience and privacy protection in the multi-user deployment process.However, the deployment of current network functions still uses the one-by-one deployment method, resulting in high complexity of the deployment of new modality, and mutual exposure of processing logic between different users.To address these issues, an incremental deployment mechanism for polymorphic network environments was proposed.Firstly, an incremental deployment mechanism for network modality was designed.Then, the incremental deployment of modality was abstracted as a graph merging problem based on this mechanism, and an adjacency list-based merge parsing graph algorithm was designed.The simulation results indicate that the proposed mechanism can achieve fast and correct merging of network modality, support incremental modality deployment, and greatly reduce the application deployment and service provision threshold of new network systems.…”
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Strategy of container migration and honeypot deployment based on signal game in cloud environment by Lingshu LI, Jiangxing WU, Wei ZENG, Wenyan LIU

“…Multi-tenant coexistence and resource sharing in the SaaS cloud pose serious security risks.On the one hand, soft isolation of logical namespaces is easy to be bypassed or broken.On the other hand, it is easy to be subjected to co-resident attacks due to sharing of the host operating system and underlying physical resources.Therefore it poses a serious threat to data availability, integrity and confidentiality in the container cloud.Given the problem that SaaS cloud services are vulnerable to container escape and side-channel equivalent resident attack, network deception technology increases the uncertainty of the cloud environment and reduces the effectiveness of attack by hiding the business function and characteristic attributes of the executor.Aiming at the security threat caused by the co-resident attack, combining dynamic migration and virtual honeypot security technology, the economical and reasonable network deception method was studied.Specifically, a container migration and honeypot deployment strategy based on the signal game was proposed.According to the security threat analysis, container migration and honeypot were used as defense methods.The former improved the undetectability of the system based on the idea of moving to target defense, while the latter confused attackers by placing decoy containers or providing false services.Furthermore, since network reconnaissance was the pre-step of the network attack chain, the attack and defense process was modeled as a two-person signal game with incomplete information.The sender chose to release a signal according to his type, and the receiver could only obtain the signal released by the sender but could not determine the type.Then, a game tree was constructed for the complete but imperfect information dynamic game, and the costs and benefits of different strategy combinations were set.The optimal deception strategy was determined by equilibrium analysis of attack-defense model.Experimental results show that the proposed strategy can effectively improve system security.Besides, it can also reduce container migration frequency and defense cost.…”
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Optimization Method of RFID Reader Antenna Deployment in Obstacle Environment Based on Improved FA by Tao Hong, Yixuan Jiang, Cui Wang

“…The aim of this study is to solve the problem of RFID reader antenna deployment when an obstacle in the environment or the material itself is an obstacle. …”
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Positive Effect of Shiqiao River Network Water Gate Joint Deployment to Water Environment by CHEN Wen-long, XU Feng-jun

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End-to-End Message Exchange in a Deployable Marine Environment Hierarchical Wireless Sensor Network by César Ortega-Corral, Luis E. Palafox, J. Antonio García-Macías, Jaime Sánchez-García, Leocundo Aguilar

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Environment-aware based access point deployment optimization for cell-free massive MIMO system by Jing JIANG, Yongqiang LIU, Fengyang YAN, Sha TAO, Sutthiphan WORAKRIN

“…Cell-free massive multiple-input multiple-output (MIMO) systems deploy a large number of access point (AP) across the coverage area which can provide uniform high-rate services to users.However, the quality of coverage would be affected by path loss, shadow fading scatters, and environmental occlusions around the randomly placed AP in conventional cell-free massive MIMO systems that do not consider their impact.Considering the impact of actual wireless propagation environments, an AP deployment scheme was proposed to acquire uniform and consistent coverage.Firstly, a hybrid probabilistic path loss model was utilized to characterize various wireless propagation environments.Then, the AP deployment optimization problem was solved with the objective of maximizing the average throughput.Finally, the problem was transformed into a Markov game process and solved by the multi-agent deep deterministic policy gradient (MADDPG) algorithm.The simulation results demonstrate that the proposed scheme can provide more uniform coverage in complex environments and serve users with reliable and consistent service compared to random AP deployment and existing AP deployment methods.…”
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OBTPN: A Vision-Based Network for UAV Geo-Localization in Multi-Altitude Environments by Nanxing Chen, Jiqi Fan, Jiayu Yuan, Enhui Zheng

“…Most notably, the model’s high accuracy in low-quality image environments further substantiates its robustness in complex environments.…”
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A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state by R. Manikandan, S. Arunprakash, Rakan A. Alsowail, Tharani Pandiaraj

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Analysis of problems in the DNSSEC automation deployment by Chuan GUO, Feng LENG

“…Based on the practice of domain name system security extensions protocols' deployment in country code top-level domain,the development background of the domain name system security extensions protocols were summarized,the current applications of the domain name system security extensions protocols were analyzed,the automation problems in the domain name system security extensions protocols' deployment were put forward and analyzed,several issues that need to be addressed when deploying the domain name system security extensions protocols in the production environment were summarized and analyzed.…”
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Optimal deployment of energy-efficient ultra-dense network by Liang GE, Zhixu WANG

“…In order to maximize energy efficiency, the stochastic geometry method was used to model the uplink of a multi-cell multi-user MIMO cellular network.Then the system power consumption model was ameliorated.Based on this,an energy efficiency maximization problem with respect to base station density,transmit power,the number of base station antennas,users per cell and pilot reuse factor was obtained.Solving the problem,the optimal network deployment,as well as the relationship between the optimal variables,hardware characteristics and propagation environment was obtained.Finally, simulation and numerical results show that the ultra-dense network deployment can significantly improve the energy efficiency.However,with the further increase of the density of the base station,the improvement of the energy efficiency was quickly saturated.More interestingly,the deployment scenario determined by the energy efficiency optimization was just massive MIMO scenarios.…”
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Data-intensive service deployment based on edge computing by Yongmei GAO, Guanjie CHENG

“…In the edge computing environment, an optimized deployment strategy based on the negative selection algorithm was proposed to reduce the data transmission time in the service composition. …”
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Robust deployment strategy for security data collection agent by 陈黎丽, 王震, 郭云川, 华佳烽, 姚宇超, 李凤华

“…With the frequent occurrence of “network black production” incidents,attackers strategically launch target attacks with the idea of “profit-seeking”.Existing network monitoring systems lack accurate and effective monitoring strategies for “strategic attacks”.Therefore,in an adversarial environment,how to optimize the deployment of collection agents for better monitoring results becomes an extremely important issue.Based on this,a robust deployment strategy of collection agents was proposed for the above mentioned problem.Firstly,the idea of attack-defense game was introduced to measure the collection agents,threat events and their relations,then the MADG model was built.Secondly,considering that the traditional accurate solution algorithm cannot solve the problem,the robust acquisition agent deployment algorithm called RCD algorithm was designed to approximate the problem by using the sub-module and non-growths of the objective function.Finally,the RCD algorithm was verified.The experimental results show that the above model and method is feasible,effective and expandable.…”
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Spatial heterogeneity in deployment and upscaling of wind power in Swedish municipalities by Yodefia Rahmad, Fredrik Hedenus, Jessica Jewell, Vadim Vinichenko

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The crane radar: Development and deployment of an operational eco-digital twin by K. De Koning

“…By showing an example of the crane radar, a real-time digital twin of the common crane (Grus grus) migration, this paper illustrates what digital twins could look like in ecology, and which challenges modellers face towards actual deployment of their digital twin in an operational environment.The most important challenges identified here are: ex ante quality and error control of the input data; accounting for time lags between observations and data availability; harmonising the updating frequencies and temporal resolutions of models and data; developing robust model/software code to keep digital twins alive; the necessity of learning new skills for ecological modellers; and effectively communicating digital twin outputs to stakeholders. …”
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Research on the Novel Primary and Secondary Embedded Deployable Cage Mechanism by Liu Ziyu, Hang Lubin, Bai Lele, Huang Xiaobo, Wang Mingyuan, Chen Junrong, Li Sulong, Zhang Pengcheng

“…Aiming at the problems that the existing offshore cages are difficult to adjust the breeding areas adaptively to optimize the ecological environment and the difficulty in transportation due to their large size， a novel primary and secondary embedded deployable offshore cage is proposed which the over-constrained spatial mechanisms and planar compliant mechanisms are respectively adopted as the primary and secondary cages. …”
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AP Deployment Research Based on Physical Distance and Channel Isolation by Dangui Yan, Chengchang Zhang, Honghua Liao, Lisheng Yang, Ping Li, Guogang Yang

“…Aiming at the problem of inefficiency of wireless local area networks (WLAN) access point (AP) deployment in urban environment, a new algorithm for AP deployment based on physical distance and channel isolation (DPDCI) is proposed. …”
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A Gateway Deployment Heuristic for Enhancing the Availability of Sensor Grids by Majid Hussain, Ki-Hyung Kim, Ali Hammad Akbar, Shehzad Khalid, Seung-Jin Bang, Mohsin Javed, Madiha Amjad

“…Wireless sensor grids form a special class of sensor networks that find more applicability than their randomly deployed counterparts in many commercial applications due to innate regularity in their design and operation. …”
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RSU deployment planning based on approximation algorithm in urban VANET by Junyu ZHU, Chuanhe HUANG, Xiying FAN, Kuangyu QIN, Bin FU

“…To minimize the number of RSU deployed to cover a specific area,a c street model transforming the area covering problem to streets covering problem was designed,and a greedy-based polynomial (GBP) time approximation algorithm was developed to obtain the optimal RSU deployment for area coverage.For complex urban environments,a Cue model (complex urban environments model) was proposed.In this model,the target area was divided into different partitions.Then,based on shifting strategy,a polynomial time approximation scheme was designed.Theoretical analysis that include the approximation ratio and time complexity of the proposed algorithm were also presented.Simulation results show that GBP can efficiently solve the coverage problem in urban VANET.…”
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Nodes deployment strategy for underwater wireless sensors networks based on grids by Kai ZHOU

“…To optimal the node deployment of underwater wireless sensor network,a node deployment strategy with multi-metrics based on the grids was proposed.Firstly,the underwater environment was divided into some certain size grids.Then,based on number of nodes,coverage quality of nodes,lifetime of network,network redundancy,a multi-objectives model was proposed.In order to solve the model,cost function with constraint conditions was given.Based on the genetic algorithm,the cost and the energy consumption of the deployment method were computed.The simulation result shows that the energy consumption and the number of deployment nodes are reduced.…”
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